list of wetlands in india under ramsar...

APPENDIX-l

LIST OF WETLANDS IN INDIA UNDER RAMSAR CONVENTION

SINo Name of Ramsar Site State Area(ha) Date

Kolleru Lake Andhra Pradesh 90100 19108/02

2 Deepor Bee! Assam 4000 19108/02

3 Pong Dam Lake Himachal Pradesh 15662 19108/02

4 Renuka Wetland Himachal Pradesh 20 08111105

5 ChandertalWetland Himachal Pradesh 49 08111105

6 Wular Lake Jammu and Kashmir 18900 23/03/90

7 Tsomoriri Jammu and Kashmir 12000 19108/02

8 Hokera Wetland Jammu and Kashmir 1375 08111/05

9 Surinsar-Mansar Lakes Jammu and Kashmir 350 08111105

10 Ashtamudi Wetland Kerala 61400 19108/02

11 Sasthamkotta Lake Kerala 373 19108/02

12 Vembanad-Kol Wetland Kerala 151250 19108/02

13 Bhoj Wetland Madhya pradesh 3201 19108/02

14 Loktak lake manipur 26600 23/03/90

15 .Chilika Lake Orissa 116500 01110/81

16 Bhitarkanika Mangroves Orissa 65000 19108/02

17 Harike Lake Punjab 4100 23/03/90

18 Kanjli Punjab 183 22/01102

19 Ropar Punjab 1365 22/01/02

20 Sambhar Lake Rajasthan 24000 23/03/90

21 Keoladeo National Park Rajasthan 2873 01110/83

22 Point Calimere Wildlife Tamil Nadu 38500 19108/02

and Bird Sanctuary 240 0811110523 Rudrasagar Lake Tripura

24 Uppar Ganga Uttar Prdaesh 26590 08/11105

River(Brijghat to NaroraStretch) 12500 19/08/02

25 East Kolkata Wetlands West Bengal

Total Sites (25)677131

147

Arab S

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148

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'd

APPENDIX-2

APPENDIX-3

INFILTRATION CHARACTERISTICS OF SOIL IN VARIOUS LSG OF KOLLAMDISTRICT

No Name ofLSGGravel

Sand% Silt&Clay% Penneability Infiltration% mm/sec nature

1 Adichanal 21 54 25 4.68.6 x 10-4 Very slow2 Alayamon 8 79 13 4.216 x 10-4 Very slow3 Anchal 11 73 16 3.87 x 10-4 Very slow4 Arienkavu 24 66 10 3.12 x 10-4 Very slow5 Chadayama 22 64 14 4.82 x 10-4 Very slow6 Chathannur 22 61 17 4.64 x 10-3 Moderate7 Chavara 11 65 24 6.92 x 10-3 Moderate8 Chithara 28 51 21 4.1 x 10-4 Very slow9 Clappana 18 56 26 3.68 x 10-3 Moderate10 Edamulack 18 60 22 5.26 X 10-4 Slow11 Elamad 18 62 20 3.59 x 10-4 Very slow12 Elampallo 22 56 22 3.92 x 10-3 Moderate13 Ezhukone 26 60 14 3.8 x 10-3 Moderate14 Kadakkal 24 69 17 3.69 x 10-3 Moderate15 Kalluvath 4 81 15 3.71 x 10-3 Moderate16 Karavaloo 16 71 13 3.46 x 10-4 Very slow17 Kareepra 18 62 20 4.19x10-3 Moderate18 Karunagap 18 62 20 3.85 x 10-3 Moderate19 Kizhakkek 24 66 10 3.91 x 10-3 Moderate20 Kallam (M 17 54 29 4.58 x 10-3 Moderate21 Kottarnkar 17 66 17 5.01 x 10-3 Moderate22 Kottarakk 13 71 16 4.01 x 10-3 slow23 kulakkada 27 60 11 3.90 x 10-3 slow24 Kulasekha 17 51 29 4.06 x 10-3 slow25 Kulathupu 18 72 10 3.01 x 10-4 Very slow26 Kundara 19 59 22 4.19xl0-4 Very slow

27 Kunnathur 16 65 19 4.52 x 10-4 Slow

28 Melila 23 58 19 4.42 x 10-4 Slow

29 Mundrothu 24 61 15 3.74 x 10-3 slow

30 Mylom 14 65 21 5.61 x 10-4 Slow

31 Mynagappa 24 49 27 4.11 x 10-4 Very slow

32 Mayyanadu 30 59 11 5.18 x 10-3 Moderate

33 Nedumpana 13 58 29 4.18 x 10-3 slow

34 Neduvatho 32 55 13 4.99 x 10-4 Slow

35 Neendakar 28 58 14 4.86x 10-3 Moderate

36 Nilamel 61 17 5.24 x 10-4 Slow22 Slow37 Oachira 55 27 3.99 x 10-318 Moderate38 Panayam G 68 12 5.95 x 10-320

3.92 x 10-3 slow39 Panmana 11 68 214.08 x 10-3 slow40 Paravoor 13 69 18

41 Pathanapu 24 60 16 3.51 x 10-4 Very slow

42 Pattazhy 12 75 13 3.61 x 10-4 Very slow

43 Pattazhy 23 71 6 4.11 x 10-4 Very slow

44 Pavithres 55 32 4.12 x 10-4 Very slow13 3.98 x 10-3 Moderate45 Perayam 12 56 22

5.28 x 10-3 Moderate46 Perinad 20 70 1047 4.51 x 10-3 Moderate

Poothakku 32 57 11

149

48 Pooyappa1 32 47 21 5.68 x 10-4 Slow49 Poruvazhy 11 73 16 5.72 x 10-4 Slow50 Puna1ur 16 69 15 5.64 x 10-4 Slow51 Sasthamko 17.1 59.9 24 4.42 x 10-4 Slow52 Sooranad 19 67 14 3.96 x 10-4 Very slow53 Sooranad 23 56 21 3.78 x 10-3 slow54 Thalavur 37 56 7 3.64 x 10-4 Very slow55 Thazhava 9 59 32 4.15 x 10-3 slow56 Thekkumbh 20 69 11 6.18 x 10-3 Moderate57 Thenma1a 15 68 17 3.12 x 10-4 Very slow58 Thevalakk 12 64 24 3.16 x 103 slow59 Thodiyoor 22 54 24 4.16 x 103 slow60 Thrikkada 21 67 12 5.91 x 10-3 Moderate61 THRIKKARU 22 66 12 6.15 x 10-3 Moderate62 Thrikkovi 26 61 13 5.85 x 10-3 moderate63 Ummannoor 18 67 15 3.61 x 10-4 Very slow64 Velinallo 25 64 11 3.16 x 10-3 slow65 Veliyam 18 58 24 3.54 x 10-4 Very slow66 Vettikkav 25 65 10 4.61 x 10-4 Slow67 Vilakkudy 32 56 12 5.98 x 10-4 moderate68 West Kall 23 65 12 3.74 x 10-3 slow69 Yeroor 18 71 11 2.45 x 10-4 Very slow70 Piravanth 22 75 3 2.9 x 10-3 slow71 Alappad 12 60 28 3.98 x 10-4 Very slow72 Ittiva 20 63 17 3.83 x 10-4 Very slow

150

APPENDIX-4

LAND USE PATTERN OF KOLLAM DISTRICT AREA IN SQ.KM

1

1510.160.020.042.24y

Park

Residential Commercial Industrial Public Agricultural&

Transportation RailwayWaterLSGbodies

Vacant Forestopenspace

Adichanallur 9.37 0.13 0.21 0.46 9.77 0.53 0.31 0 1.46 0.01 0

Alayamon 2.84 0.06 0.02 0.18 21.13 0.12 0.26 0 0.01 0.14 6.84

Anchal 5.22 0.45 0.04 0 18.81 0 0.12 0 0.08 0.04 0

Arienkavu 9.31 0.13 0.09 0.55 66.61 4.2 0 0 0.01 0 168.57

Chadayama 7.88 0.05 0.02 0.16 13.15 0 0.26 0 0.43 0.24 0

Chathannu 19.24 0.16 0.33 0.64 11.46 0 0.5 0 1.13 0.19 0

Chavara 10.88 0.07 0.31 0 0.54 0.12 0.11 0 0.74 0 0

Chithara 12.81 0.18 0.01 0.23 49.05 0.1 0.52 0 0.03 0 0

Clappana 8.06 0.03 0.01 0.21 0.68 0 0.19 0 0.21 0.95 0

Edamulack 12.78 0.25 0.09 0.32 25.24 0.09 0.17 0 0 0 0

Elamad 4.27 0.02 0.06 0.16 24.41 0 0.37 0 0.11 0.78 0

Elampallo 10.26 0.1 0.22 0.28 3.57 0 0.88 0 0.29 1.05 0

Ezhukone 8.39 0.14 0.08 0.58 5.77 0 0.39 0 0.03 0.19 0

Kadakkal 29.12 0.17 0.02 0.48 22.79 0.09 0.59 0 0.1 0 0

Kalluvath 19.34 0.21 0.37 15.56 0 0.68 0 0.35 0.08 00.2Karavaloo 5.49 0.01 20.98 0 0.04 0 0 0.04 00.06 0.01Kareepra 7.12 0.16 0.23 0 0.18 0 014.74 0.06 0.1 0.68Karunagap 1.66 0 0 0.45 1.14 0.1 013.85 0.28 0.03 0.37Kizhakkek 0 0 0.23 1.28 1.61 07.38 0.04 0.08 0.34 2.46Kollam (M 0 3.05 0 3.45 1.83 0

41.5 1.28 1.2 3.31 2.15

Kottamkar 0 0.15 0 0.07 0.16 07.03 0.02 0.11 0.13 1.29

Kottarakk 0.4 0 0.1 0.21 06.96 0.28 0.04 1.53 8.03 0

kulakkada 0.62 0 1.12 0.34 016.05 0.04 0.16 0.44 9.98 0

Kulasekha 0.3 0 0.24 0.26 011.5 0.25 0.11 0.5 2.52 0

KUlathupu 0 0 8.16 12.17 282.586.79 0.09 0.04 0.36 101.3 0

Kundara 0.23 0 0.01 0.24 06.56 0.15 0.24 0.33 1.59 0

0.42 0 1.19 0.43 0Kunnathur 5.75 0.06 0.11 0.26 13.2 0

0 0Melila 0.31 0 0.184.81 0.09 0.07 0 13.4 0.14

0.31 0Mundrothu 0.18 0 6.8

3.35 0 0.01 0.1 2.47 00.41 0.33 0.18 0

Mylom 12.32 0.05 0.04 0.14 14.07 0 00.57 0.16 0

Mynagappa 0.4 014.34 0.04 0.08 0.27 3.94 0

1.26 0.37 0Mayyanadu 2.63 0 0 0.18

12.56 0.08 0.13 0.45

0 1.13 0.08 0Nedumpana 17.31 0.05 0.11 0.2 5.86 0 0.45

0 0.02 0.11 0Neduvatho 7.06 0.17 0.15 0.41 14.82 0 0.41

1.57 0.18 0Neendakar 0.41 0

3.08 0.09 0.16 0.1 00 0.06 0.22 0Nilamel 0.23 0

4.39 0.01 0.37 13.91 00.14 0.06 0.08 0Oachira 0.32 0

11.13 0.15 0.03 0.26 1.3 00.03 0

Panayam G 0.3 0 2.26.47 0.04 0.21 1.26 0

00.03 1.88 0.78Panmana 0 0.34 0

13.5 0.04 0.83 0.39 1.610.37 3.86 0.04 0

Paravoor 0 09.02 0.13 0.05 0.59 2.16 0.12 0.4Pathanapu 0.11 0 0.42

4.52 0.37 19 0 00.27 0.07 1.05 0Pattazhy 0.07 0

2.29 0.1 0.01 0.11 15.67 0.110.11 0 0

Pattazh 0.12 03.99 0.05

Park

Residential Commercial Industrial Public Agricultural&

Transportation RailwayWater

lSG bodiesVacant Forest

openspace

Pavithres 15.92 0.07 0.35 0.27 7.73 0.13 0.14 0 0.72 0 0

Perayam 8.95 0.06 0.3 0.29 0.9 0 0.15 0 7.17 0.22 0

Perinad 7.77 0.03 0.22 0.2 1.87 0 0.27 0 5.15 0.11 0

poothakku 9.09 0.08 0.02 0.24 4.71 0 0.2 0 1.87 0.04 0

Pooyappal 9.05 0.03 0.07 0.14 12.13 0 0.29 0 0.18 0.25 0

Poruvazhy 7.83 0.05 0.05 0.15 9.84 0 0.64 0 0.61 0.18 0

Punalur 8.55 0.32 0.07 0.62 23.11 0 0.55 0 0.81 0.44 0

Sasthamko 13.94 0.11 0.04 0.38 4.09 0 0.29 0 5.14 0.47 0

Sooranad 11.61 0.05 0.14 0.42 7.06 0 0.99 0 0.87 1.59 0

Sooranad 11.5 0.04 0.22 0.38 5.18 0 0.14 0 0.76 0.05 0

Thalavur 6.91 0.07 0.03 0.32 24.85 0 0 0.12 0.51 0.22 0

Thazhava 17.64 0.06 0.03 0.42 4.15 0 0.25 0 0.42 0.16 0

Thekkumbh 4.46 0.03 0.01 0.2 0.73 0.01 0.05 0 9.23 0 0

Thenmala 5.3 0.09 0.01 0.4 55.17 0 0.61 0 0.04 2.72 69.16

Thevalakk 12.52 0.07 0.07 0.27 1.59 0 0.22 0 2.44 0.07 0

Thodiyoor 14.41 0.12 0.23 0.51 3.48 0.22 0.81 0 0.76 0 0

Thrikkada 6.55 0.07 0.04 0.24 1.35 0.05 0.34 0 6.22 0 0

THRIKKARU 5.61 0.02 0.01 0.2 0.74 0 0.07 0 11.52 0.09 0

Thrikkovi 13.86 0.1 0.3 0.38 4.43 0.08 0.3 0 0.66 0 0

Ummannoor 6.8 0.11 0.09 0.44 26.05 0 0.46 0 0.06 0.31 0

Velinallo 7.94 0.17 0.05 0.2 17.21 0.27 0.31 0 0.33 0 0

Veliyam 8.07 0.09 0.06 0.2 20.42 0 0.28 0 0.23 0.8 0

Vettikkav 10.72 0.12 0.04 0.52 23.07 0 0.85 0 0.53 0.28 0

Vilakkudy 9.32 0.04 0.04 0.58 11.17 0 0.18 0 0.11 0 0

West Kall 6.05 0.02 0.1 0.21 3.13 2.34 0.41 0 0.8 0 0

Yeroor 2.48 0.09 0.01 0.17 48.45 0 0.44 0 0.42 1.61 5.37

Piravanth 16.27 0.21 0.01 4 51.27 0.03 0 0 1.99 0 63.55

Alappad 3.96 0.01 0 0.2 0.01 0 0.15 0 2.21 0.27 0

Ittiva 7.93 0.06 0.03 0.16 31.86 0 0.51 0 0.69 0.28 0

152

THE EXTENT OF PADDY IN KOLLAM DISTRICT

APPENDIX-5

SI.No

12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455

Name ofLSG

AlayamonAnchalAryankavuEdamulakkKaravalooKulathupuThenrnalaYeroorThrikkadaThrikkaruChadayamaChitharaElammaduIttivaKadakkalNilamelVelinalloChavaraNeedakaraPanmanaThekkubhaThevalakkKizhakkekKundaraMandrothuPanayamPerayamPerinaduAdichanalChathanooKalluvathNedumpanaBhootahkuAlappadKarunagapMynagappaThodiyoorEzhukoneKareepraKottarakkNeduvathoPooyappalVeliyamElampalloKottarnkarMayyanaduThrkkovilClappanaKulasekhaOachiraThazhavaPathanapuPartazhyPattazhyPiravanth

153

Area(ha)

100150o

245110125401709201301021802252807016525o10o50150753

12020

23568180400400

94.52o

9012018012546012010021022940225351702580501303715070100

56

57585960616263646566676869707172

ThalavoorVilakudy

KunnathurPoruvazhySasthamco

SooranadSooranad

West KallKu1akkadaMe1ilaMylom

Pavithres

UmmannoorVettikkavKollam CoParavurM

PunalurM

154

10010060170205310236702402001502452752303325275

APPENDIX-6

LAND USE PATTERN OF KOLLAM CORPORATION AREA IN SQ.KM

No Name of Ward Res Com Ind Pub Agri Tot Tot T Water Vacca Forestm Park bodies nt

&Open

I Shasthiku1angaraSpace

0.895 0.015 0.01 0.13 0.004 0.004 0.019 0.146 0 0West

2 Shasthiku1angara 0.95 0.014 0 0.06 0 0 0.031 0.004 0.0073 0Middle

3 Shasthiku1angara 0.87 0.021 0.04 0.07 0.043 0 0.03 0.922 0.1176 0North

4 Kavanadu 0.518 0.024 0 0.02 0 0 0.021 0 0 0

5 Kureepuzha 0.685 0.01 0.02 0.04 0 0 0 0 0.0017 0North

6 Kureepuzha 0.67 0.011 0.02 0.1 0.022 0.013 0.072 0.414 0.0144 0South

7 Mulankadakam 0.68 0.017 0 0.03 0.035 0 0.081 0.001 0.0032 0

8 Thevally 0.635 0.045 0.02 0.2 0.003 0.001 0.098 0.001 0.0159 0

9 Asramom South 0.476 0.043 0.1 0.13 0.008 0.222 0.141 0.721 0.1089 0

10 Asramom North 0.56 0.017 0.02 0.06 0.021 0 0.089 0 0.0671 0

11 Uliyakovi1 West 0.561 0.002 0.01 0.02 0.024 0 0.049 0 0.0743 0

12 Uliyakovi1 East 0.86 0.004 0.05 0.04 0.029 0.005 0.076 0.006 0.0554 0

13 Kadappakada 0.756 0.014 0.01 0.02 0 0 0.066 0.001 0.0027 0.0351

14 Koickal 0.82 0.008 0 0.01 0 0 0.026 0.001 0 0.0418

15 Kallumthazham 1.14 0.12 0.1 0.12 0.013 0 0.074 0.035 0.0156 0.0386

16 Mangad 1.14 0.0078 0.02 0.06 0.1002 0.0577 0.104 0.0004 0.0264 0

17 Arunootimangala 1.0115 0.0008 0.03 0.1 0.053 0 0.0011 0.0007 0.093 0.0169

m18 Chathanarnkulam 1.1161 0.0236 0.05 0.01 0.06 0 0.0023 0 0 0.0327

19 Karicode 0.96 0.0132 0.02 0.03 0 0 0.0186 0.0004 0.1218 0.0816

20 College division 0.76 0.0035 0.04 0.08 0.0772 0 0.0012 0.0002 0 0.0747

21 Palkulangara 0.92 0.0217 0.05 0.03 0.0572 0 0.0438 0.0353 0.0081 0.0091

22 Ammannada 0.63 0.0013 0 0.04 0.0124 0 0.0169 0.0009 0.0005 0

23 Vadakkevi1a 0.77 0.0057 0.01 0.03 0.0029 0 0.0007 0 0.0008 0

24 Pallimukku 0.74 0.0212 0.02 0.01 0 0 0 0.0004 0.013 0

25 Ayathil 0.8335 0.0039 0.03 0.02 0.0622 0 0.0219 0.0019 0 0

26 Mulluvi1a 0.9015 0.0125 0.02 0.01 0.1214 0 0.0887 0.0291 0.0022 0

27 Kilikollur 0.9652 0.0134 0.05 0.02 0.0604 0.0103 0.001 0.0026 0.0341 0

28 Palathara 1.7227 0.0109 0.01 0.07 0.7875 0.0022 0.0736 0.0234 0.0013 0

29 Manakkadu 1.0878 0.0524 0.01 0.06 0.0563 0.0023 0.0001 0.001 0.04 0

30 Kollurvi1a 0.51 0.0127 0.04 0.02 0.0042 0 0.0234 0.0002 0 0.0166

155

JI Kayyalakkal 0.81 0.0222 0.01 0.01 0 0 0.0127 0.0163 0 0.01j2 Valathungal 1.1 0.0161 0.01 0.08 0.0299 0 0.0005 0.0002 0.0004 0.0251

18298)3 Akkolil 1.42 0.0133 0 0.05 0.1586 0 0.0126 0.0068 0 0

53998)4 Thekkumbagam 0.8418 0 0.01 0.07 0.0702 0 0.0055 0.2611 0.039 0)5 Eravipuram 1.11 0.0169 0.04 0.06 0.0103 0 0.0199 0 0 0.0001)6 Bharanikavu 1.07 0.0111 0.01 0.06 0 0.0117 0.0261 0.0003 0 0.0255J7 Thekkevila 1.1 0.0099 0.09 0.05 0.0761 0 0 0 0 0)8 Mundakkaleast 0.989 0.0027 0 0.02 0 0 0.0903 0.0417 0.0439 0.0001)9 Pattathanam 0.4354 0.0056 0 0.05 0 0.0013 0.0487 0.0032 0.0127 0.000240 Cantonment 0.42 0.1109 0.01 0.67 0 0.1146 0.1699 0.0012 0.005 0.21941 Mundakka1west 0.6673 0.0269 0.18 0.07 0.0001 0.0151 0.0899 0.0482 0.0312 0.0289

42 Thamarakkulam 0.31 0.3762 0 0.1 0.0108 0 0.12940.1001 0.0047 04) Pallithottam 0.2012 0.0106 0.01 0.02 0.0041 0.0246 0.0561 0.0018 0.0386 044 Beach north 0.149 0.0057 0.01 0.03 0 0 0.0255 0.0001 0.0121 045 Cuchery 0.35 0.0277 0.02 0.1 0 0 0.0803 0.0004 0.0115 046 kaikulangara 0.3694 0.0107 a 0.07 0.0002 0.0076 0.0713 0.0109 0.0412 0

47 Thankaserry west 0.9 0.0032 a 0.03 0.0581 0.0034 0.0889 0.01 0.0164 0

48 Thirumullavaram 0.9741 0.0031 0.01 0.04 0 0.0009 0.0942 0.0105 0.0962 0

49 Kannime1 East 1.81 0.0318 0.01 0.1 0.0066 0 0.065 0.18070.0319 0

50 Kannime1 West 1.36 0.0044 0.01 0.02 0.0552 0 0.011 0.0196 0.0088 0

156

APPENDIX-7

DETAILS OF POPULATION DENSITY, HOUSE DENDITY AND TYPE OF HOUSES INKOLLAM CORPORATION

SI. Name of Ward Population HouseType of Houses

No density density Pucca SemiPucca Kutcha

Shasthikulangara West 5527 1249.18 1233 208 83

2 Shasthikulangara6758 1501.87 528Middle 120 959

3 Shasthikulangara North 2719 626.066 660.5 136.591 523.9094 Kavanadu 7505 1644.07 362.489 489.947 117.5645 Kureepuzha North 8016 1776 587.279 566.366 178.3556 Kureepuzha South 5834 1225.37 1141.85 386.855 113.2987 Mulankadakam 10046 2197.65 1205.23 642.779 20.17448 Thevally 6611 1558.42 635.581 696.18 242.0819 Asramom South 3528 780 1100.6 86.5449 333.8610 Asramom North 7640 1700 624.65 204.595 581.61411 Uliyakovil West 7758 1742.67 1104.55 165.728 36.8574

12 Uliyakovil East 7389 1639.29 1673.15 44.4312 118.422

13 Kadappakada 7451 1693.41 1017.06 369.84 154.1

14 Koickal 7049 1492.39 1031.95 251.259 89.7942

15 Kallumthazham 4211 896.386 578.683 358.162 551.155

16 Mangad 3566 824.183 630.5 504.4 126.1

17 Arunootimanga1am 5242 1138.93 633.802 765.844 92.3548

18 Chathanamkulam 6283 1335.39 980.319 367.685 387.996

19 Karicode 5540 1208 899.205 525.933 84.862

20 College division 6932 1498.06 980.268 344.861 217.872

21 Palkulangara 5181 1152.94 513.128 580.082 278.928

22 Ammannada 9545 2097.14 1435.41 16.2948 16.2948

23 Vadakkevi1a 8038 1803.61 1058.83 304.34 133.832

24 Pallimukku 9597 1907.41 965.625 401.082 178.293

25 Ayathil 7653 1603.06 1025.55 240.049 305.402

26 Mulluvila 6375 1380.83 837.448 748.301 71.251

27 Kilikollur 6539 1342.61 1479 48.7904 16.212

28 Palathara 2821 618.298 785.347 549.815 117.838

29 Manakkadu 5961 1199.24 853.21 663.59 54.1995

30 Kollurvila 12578 2322.22 859.22 580.518 23.2617

31 Kayyalakkal 10934 2019.32 1680.51 57.9302 38.5609

32 Valathunga1 5165 1112.6 678.24 649.98 84.78

33 Akkolil 4487 968.675 582.739 679.862 345.398

34 Thekkumbagam 5609 1271.54 713.6 514.91 424.49

35 Eravipuram 6036 1374.4 695.446 797.667 225.058

36 Bharanikavu 5831 1285.95 986.193 525.928 43.8792

157

)7 Thekkevila 5824 1318.8 707.213 933.654 113.133)8 Mundakkaleast 6471 1510.92 445.544 1288.81 63.6492)9 Pattathanam 9911 2107.14 1020.94 145.848 13.21640 Cantonment 4737 1100 1729.58 40.2182 221.241 Mundakkalwest 6254 1423.48 1195.01 261.92 180.0742 Thamarakkulam 7046 1387.38 864.116 465.282 99.744243 Pallithottam 25233 5010.81 927 927 044 Beach north 31822 6039.13 519.208 843.818 25.974345 Cuchery 9821 2201.28 1113.3 566.095 37.774

46 kaikulangara 13058 2715.25 1549.13 52.866 0

47 Thankaserry west 7429 1601.8 1289.58 468.859 19.558

48 Thirumullavaram 5611 1254.47 701.294 689.721 151.986

49 Kannime1 East 4000 892.825 947.119 850.555 193.326

50 Kannimel West 5581 1205.37 453.131 1107.77 234.917

46220.1 23237.3 8725.03

158

VALUES OF RUNOFF COEFFICIENT

SI.No Land use K

1 Dry Agriculture OJ2 Paddy 0.15

3 Residential 0.6

4 Commercial 0.7

5 Industrial 0.7

6 Open Space 0.15

7 Public Place 0.5

8 Transport 0.79 Vaccant 0.310 Forest 0.3

APPENDIX-8

PARTICLE SIZE ANALYSIS OF SOIL FROM THE SELECTED WARDS IN KOLLAMCORPORATION

Particle size Shasthikulangara Shasthikulangara Uliyakovil Karicode(mm) West Middle

1 2 1 2 1 2 1 2

4.75 98.5 99.7 99.7 99.7 85 91 52.4 94.2

2 97.6 98.2 99 99.5 65 75 46.8 92.8

1 94.9 83.8 9203 97.7 55 65 42 9103

0.6 93.4 73.8 84.7 9603 50 55 40.5 88.9

0.425 90.7 61 68.2 92.7 48.6 44 37.7 80.4

OJ 88.7 55.9 60.1 90.3 45.2 38 35.9 74.6

0.15 33.4 30.7 21.6 44.9 34.9 26 24.5 43.7

0.075 6.8 21.8 8.8 25.2 28 13 20.7 25.5

0.003 18 16 20 8 21

.0015 16 12 1315

DIO

159

j_UNCONVERTED SOIL

l.CONVERTED SOIL

Particle size Kollur vila Ammannada Valathungal(mm) Cutchery

ongmconverted original

Converte1 2al d 2

4.75 86 90.7 97.7 67.3 98.2 54.1 95.3 99.12 84.7 87.2 97.5 62.1 97.7 48 93.4 98.8I 78.9 71.4 96.1 56.8 95.7 39.4 84.4 94.2

0.6 69.7 61.7 91.4 53 82.3 34.9 71.7 83.6

0.425 34.9 49 44.6 46.3 35.5 29.1 47.5 52.6

0.3 26.1 44.3 36 42.2 25.3 26.9 41 42.8

0.15 24 34.3 16.8 30 12.1 23.1 20.1 22.3

0.075 10.6 29.5 12.6 24 6.8 20.7 11.5 13.4

0.003 22 16 14

0.0016 17 12 9

Particle sizeThirumullavaram Tankassery Kannimel

. (nun)

Original converted Original

1 2 1 2 1 2

4.75 96.1 95.5 92 65.1 98.9 57.1

2 95 93.7 90.4 59.8 98.6 49.9

1 76.8 83.4 84.2 54 96.8 42.5

0.6 65.8 71.1 . 77.4 51.9 94 40.1

0.425· 54.3 57.2 47.4 49.1 78.7 36.5

OJ 50.6 52.3 38 47.8 67.2 34.5

0.15 33 32 14.2 41.6 23.7 28.5

0.Q75 12 24.5 8.9 36.6 13 22.8

0.003 18 4 22 19

0.0016 13 12 9

160

APPENDIX-9

DEMAND SUPPLY, SHORTAGE AND SOURCE OFWATER IN THE WARDS

No Name of WardWater Demand in supply by water

mid authorityin mid Shortage in mld

I Shasthikulangara West 1.34 1.02

2 Shasthikulangara Middle 1.42 1.02 0.4

3 Shasthikulangara North 1.45 1.02 0.43

4 Kavanadu 1.35 1.02 0.33

5 Kureepuzha North 1.3 0.35 0.95

6 Kureepuzha South 1.35 0.35 1

7 Mulankadakam 1.28 0.54 0.74

8 Thevally 1.34 0.54 0.8

9 Asramom South 1.38 0.42 0.96

10 Asramom North 1.31 0.42 0.89

11 Uliyakovil West 1.36 0.42 0.94

12 Uliyakovil East 1.42 0.42 1

13 Kadappakada 1.36 0.42 0.94

14 Koickal 1.3 0.15 1.15

IS Kallumthazham 1.4 0.15 1.25

16 Mangad 1.41 0.15 1.26

17 Arunootimangalam 1.47 0.7 0.77

18 Chathanamkulam 1.42 0.7 0.72

19 Karicode 1.29 0.7 0.59

20 College division 1.42 0.7 0.72

21 Palkulangara 1.26 0.7 0.56

22 Ammannada 1.32 0.35 0.97

23 Vadakkevila 1.42 0.15 1.27

24 Pallimukku 1.38 1.02 0.36

25 Ayathil 1.41 0.7 0.71

Mulluvila 1.53 0.7 0.8326

0.7 0.827 Kilikollur 1.5

0.7 0.7228 Palathara 1.42

0.70.67

29 Manakkadu 1.370.25

1.2230 Kollurvila 1.47

0.251.14

31 Kayyalakkal 1.39

161

]2 Valathungal 1.49 0.25 1.2433 Akkolil 1.36 0.25 1.1134 Thekkumbagam 1.46 0.25 1.2135 Eravipuram 1.35 0.25 1.136 Bharanikavu 1.39 0.25 1.1437 Thekkevila 1.35 1.02 0.3338 Mundakkaleast 1.53 1.02 0.51

39 Pattathanam 1.33 1.02 0.31

40 Cantonment 1.52 1.02 0.5

41 Mundakkalwest 1.39 1.02 0.37

42 Thamarakkulam 1.45 0.35 1.1

43 Pallithottam 1.31 0.35 0.96

44 Beach north 1.42 0.54 0.88

45 Cuchery 1.5 0.54 0.96

46 kaikulangara . 1.31 0.54 0.77

47 Thankaserry 1.43 0.54 0.89

48 Thirumullavaram 1.37 0.54 0.83

49 Kannimel East 1.44 0.54 0.9

50 Kannimel West 1.32 0.54 0.78

162

APPENDIX-IO

RESPONSE OF SURVEY ABOUT SOURCE AND SHORTAGE

Ward Source of water Water scarcity peoples response %

well pIpe No Yes

1 Shasthikulangara West 1.83 98.17 3.67 96.33

2 Shasthikulangara Middle 30.3 69.7 43.08 56.92

3 Shasthikulangara North 46.43 53.57 46.07 53.93

4 Kavanadu 61.46 23.96 46.38 53.62

5 Kureepuzha North 79.28 20.72 0.79 98.41

6 Kureepuzha South 48.28 51.72 45.66 53.76

7 Mulankadakam 94.51 5.49 9.89 90.11

8 Thevally 73.79 26.21 2.91 97.09

9 Asramom South 74.8 25.2 39.11 60.08

10 Asramom North 77.69 22.31 33 66.92

11 Uliyakovil West 54.93 45.07 44.93 55.07

12 Uliyakovil East 82.93 17.07 7.32 92.68

13 Kadappakada 96 0 32 68

14 Koickal 100 0 37.35 62.65

15 Kallumthazham 98.11 1.89 22.38 77.62

16 Mangad 65 35 32.5 67.5

17 Arunootimangalam 99.11 0.89 1.79 98.21

18 Chathanamkulam 100 0 27.38 72.62

19 Karicode 96.63 0 8.05 91.96

20 College division 97.65 2.35 38.82 58.82

21 Palkulangara 94.26 5.74 35.9 64.1

22 Ammannada 100 0 30 70

23 Vadakkevila 98.33 1.67 36.67 73.33

24 Pallimukku 99.04 0.96 40 60

25 Ayathil 0 32.5 67.598.59

26 Mulluvila 91.4 6.9 93.18.6

11.58 45.32 54.6827 Kilikollur 88.42

28 Palathara 10.81 28.65 78.6583.78

0 30 7029 Manakkadu 100

0 1.59 98.4130 Kollurvila 100

40 6031 Kayyalakkal 100 0

163

II Valathunga1 100 a 2.1 97.87

JJ Akko1il 91.95 7.38 24.6 75.37

J4 Thekkumbagam 73.22 26.78 35.08 64.92

J) Eravipuram 100 0 0 100

J6 Bharanikavu 100 0 0 97.18

17 Thekkevila 98.28 1.72 5.17 94.83

J8 Mundakka1east 98.23 1.77 38 72

J9 Pattathanam 89.77 10.23 36.67 63.33

~O Cantonment 61.62 38.38 26.97 73.03

~l Mundakkalwest 97 3 49 51

~2 Thamarakku1am 90.48 9.52 13.33 86.67

43 Pallithottam 25 73.33 48.33 61.67

44 Beach north 36.19 63.81 25.71 74.29

45 Cuchery 79.12 20.88 30.55 69.45

46 kaikulangara 65.22 34.78 62.64 67.36

47 Thankaserry 75.82 23.08 28.02 70.88

48 Thirumullavaram 74.44 24.81 13.46 85.79

49 Kannimel East 89.11 10.89 18.04 81.96

50 Kannimel West 58.82 41.18 33.73 66.27

164

APPENDIX -11

DETAILS OF SANITATION FACILITIES

No Ward Spetic Sanitary % o/publictank(%) latrine (%) places

1 Shasthikulangara West 94.23 0 5.77

2Shasthikulangara

27.69 32.31Middle 40

3 Shasthikulangara North 100 0 0

4 Kavanadu 89.58 0 10.42

5 Kureepuzha North 100 0 0

6 Kureepuzha South 97.69 0 2.31

7 Mulankadakam 100 0 0

8 Thevally 98.06 0 1.94

9 Asramom South 98.37 1.63 0

10 Asramom North 99.23 0 0.77

11 Uliyakovil West 100 0 0

12 Uliyakovil East 98.39 1.61 0

13 Kadappakada 97.96 0 2.04

14 Koickal 98.67 1.33 0

15 Kallumthazham 100 0 0

16 Mangad 95 5 0

17 Arunootimangalam 13.39 86.61 0

18 Chathanamkulam 97.62 0 2.38

19 Karicode 13.48 85.39 1.12

20 College division 96.47 3.53 0

21 Palkulangara 98.36 1.64 0

22 Ammannada 100 0 0

23 Vadakkevila 100 0 0

24 Pallimukku 0.96 99.04 0

25 Ayathil 98.59 1.41 0

26 Mulluvila 100 0 0

27 Kilikollur 97.89 2.11 0

28 Palathara 18.92 43.24 37.84

29 Manakkadu 3.45 96.55 0

30 Kollurvila 36.51 63.49 0

31 Kayyalakkal 97.83 2.17 0

32 Valathungal 63.83 36.17 0I

33 Akkolil 99.3 0.7 0

34 Thekkumbagam 1.06 95.74 3.19

35 Eravipuram 51.81 46.99 1.2

165

36 Bharanikavu 50 50 a37 Thekkevila 20.69 77.59 1.72

38 Mundakkaleast 100 a a39 Pattathanam 100 a a40 Cantonment 98.98 a 1.02

41 Mundakkalwest 98 2 a42 Thamarakkulam 100 a a43 Pallithottam 93.22 6.78 a44 Beach north 97.14 2.86 a45 Cuchery 98.9 a 1.1

46 kaikulangara 100 a a47 Thankaserry 96.7 3.3 a48 Thirumullavaram 8.33 90.15 1.52

49 Kannimel East 34.31 63.73 1.96

50 Kannimel West 66.34 27.72 5.94

166

APPENDIX -12

SAMPLE QUESTIONAIIRE FOR SOCIO ECONOMIC SURVEY

Serial No

Location: Latitude:

Date:

Longitude:

Yes/No

Yes/No

Yes/No

1. Name of House Owner:

2. House No:

3. Type of house: Pucca Semi-pucca Kutcha

4. Source of Water Supply: Well Pipe Others

5. Have you ever felt water shortage? Yes\No

Ifyes, since when: -Jyear

6. Have you ever felt salinity: Yes/No

If yes, since when: -Jyear

7. Have you ever experienced any fluctuation in water level in well: Yes/No

8. Mode of Sanitation:

9. Distance of well from septic tan1e

10. Have you ever experienced flooding?:

If yes, when: _

11. Is there water logging during rainy season:

12. Is there any settlement cracks in building:

167

APPENDIX -13

RESULTS OF SOCIO ECONOMIC SURVEY CONDUCTED IN KOLLAMCORPORATION ABOUT THE IMPACTS

No Name of ward Percntage of people marked Yes

flooding & Salinity Settlement

water loggingWater scarcity

intrusioncracks inbuildinds

1 Shasthikulangara West 68.21 96.33 61.79 92.12

2 Shasthiku1angara Middle 71.23 56.92 62.5 91.14

3 Shasthiku1angara North 20.36 53.93 63.36 65

4 Kavanadu 91.65 53.62 61.23 8569

5 Kureepuzha North 98.41 72.36. 79.89

6 Kureepuzha South 53.76 61.13. 75.63.

7 Mulankadakam 67.67 90.11 21.23 6956

8 Thevally 95.56 97.09 42.56 84.23

9 Asramom South 26.23. 60.08 21.89 87.54

10 Asramom North 61.45 66.92 22.12 83.68

11 U1iyakovi1 West 69.12 55.07 23.45 74.69

12 U1iyakovi1 East 76.78 92.68 63.78 96.67

13 Kadappakada 79.23 68 32.13 58.62

14 Koicka1 82.56 62.65 31.46 98.63

15 Ka11umthazham 84.89 77.62 23.79 65.52

16 Mangad 85.32 67.5 24.31 64.89

17 Arunootimanga1am 83.65 98.21 28.64 6895

18 Chathanamku1am 84.98 72.62 18.97 6897

19 Karicode 52.12 91.96 22.25 69.56

20 College division 57.45 58.82 12.28 85.56.

21 Pa1ku1angara 67.78 64.1 23.36 8554

22 Ammannada 86.89 70 62.39 84.56

23 Vadakkevi1a 95.56 73.33 63.14 81.24

24 Pallimukku 96.23 60 35.17 87.26

25 Ayathi1 87.37 67.5 56..12 86.28

26 Mulluvi1a 71.35 93.1 63.45 83.27

27 Ki1ikollur 59.26 54.68 54.78 82.29

28 Pa1athara 45.29 78.65 48.98 78.23

29 Manakkadu 65.24 70 56.97 76.54

30 Kollurvi1a 75.27 98.41 87.65 79.56

31 Kayya1akka1 76.21 60 36.64 74.36

32 Va1athunga1 63.15 97.87 72.32 79.41

33 Akko1i1 64.18 75.37 58.31 65.45

34 Thekkumbagam 31.14 64.92 69.96 89.47

35 Eravipuram 69.17 100 67.93 94.58

168

36 Bharanikavu 59.12. 97.18 65.95 91.6537 Thekkevila 61.25 94.83 36.58 92.8538 Mundakkaleast 48.58 72 3859 79.8739 Pattathanam 66..14 63.33 35.54 75.4540 Cantonment 63.17 73.03 45.23 78.1241 Mundakkalwest 61.74 51 56.14 98.5642 Thamarakkulam 52.71 86.67 64.57 95.0143 Pallithottam 48.85 61.67 68.56 90.744 Beach north 34.82 74.29 67.34 91.3545 Cuchery 52.52 69.45 79.54 8.36446 kaikulangara 42..25 67.36 54.68 86.9647 Thankaserry 42.28 70.88 69.73. 84.6948 Thirumullavaram 41.36 85.79 85.59 87.8749 Kannimel East 61.39 81.96 84.92 89.23.50 Kannimel West 62.96 66.27 82.91 87.12

169

APPENDIX 14

REGRESSION ANALYSIS USING SUPPORT VECTOR MACHINE

=== Run information ===scheme:

Relation:Instances:Attributes:

Test mode:

weka.classifiers.functions.SVMreg -c 100.0 -N 0 -I"weka.classifiers.functions.supportvector.RegSMOImproved -L0.0010 -w 1 -P 1.0E-12 -T 0.0010 -v" -K"we ka.classifiers.functions.supportvector.polyKernel -c 80 -E1.0"

uth_train485xlx2x3x4yevaluate on training data

=== classifier model (full training set) ---sVMreg

weights (not support vectors):0.518 * (normalized) xl

+ 0.0228 * (normalized) x2+ 1.6684 * (normalized) x3+ 0.3379 * (normalized) x4+ 0.1975

Number of kernel evaluations: 58297 (58.278% cached)

Time taken to build model: 0.05 seconds

=== Predictions on training set ===inst#, actual, predicted, error

1 0.868 0.878 0.0112 0.801 0.776 -0.0253 0.708 0.837 0.1294 0.784 0.8 0.0165 0.935 0.889 -0.0466 1.118 1.068 -0.057 0.69 0.764 0.0748 1.041 0.928 -0.1139 0.764 0.8 0.035

10 0.659 0.787 0.12811 1.003 1.002 -0.00112 0.922 0.915 -0.00813 0.889 0.802 -0.08714 0.896 0.848 -0.04815 0.682 0.772 0.0916 0.952 0.856 -0.09617 1.068 1.021 -0.04718 0.935 0.913 -0.02219 1.127 1.496 0.36820 1.046 1.113 0.06721 0.864 0.984 0.1222 0.927 0.846 -0.0823 1.039 1.038 -0.00124 1.042 0.975 -0.06725 0.758 0.849 0.09126 0.736 0.823 0.08727 0.893 0.894 0.00128 0.829 0.828 -0.00129 1.015 0.919 -0.09630 1.021 1.001 -0.0231 0.997 0.897 -0.132 0.775 0.86 0.085

170

33343536373839404142434445464748

1.1490.7191. 0821.0561. 081.0220.70.6470.6540.9561. 0881.0420.9450.8150.8430.742

1. 2790.7850.9461. 0571. 0351.0470.8380.7910.790.8470.9231. 0650.9130.8120.8720.868

0.1310.066

-0.1360.001

-0.0460.0250.1370.1440.136

-0.109-0.1650.023

-0.032-0.0030.0290.126

=== Evaluation on training set=== Summary ===Correlation coefficientMean absolute errorRoot mean squared errorRelative absolute errorRoot relative squared errorTotal Number of Instances

=== Run information ===

0.77010.07330.0973

57.8252 %66.7153 %48

scheme:


Test mode:

weka.classifiers.functions.SVMreg -c 100.0 -N 0 -I"we ka.classifiers.functions.supportvector.RegsMoImproved -L0.0010 -w 1 -p 1.0E-12 -T 0.0010 -v" -K"weka.classifiers.functions.supportvector.RBFKernel -c 100 -G1.0"


=== classifier model (full training set) ---

SVMreg

support vectors:-100.0 * k[O]+100.0 * k[l]-100.0 * k[2]-100. 0 * k[3]-71.64936871252853 * k[4]-30.7672618284859 * k[5]+100.0 * k[6]+100.0 * k[7]+100.0 * k[8]-100.0 * k[9]+20.845318214657524 * k[10]+12.082376455822146 * k[ll]+100.0 * k[12]+100. 0 * k[13]-68.04460127731681 * k[14]+100.0 * k[15]+100.0 * k[16]-100.0 * k[17]+2.7984624882109204 * k[18]+26.314069590368774 * k[19]+100.0 * k[21]

171

-100.0 ~, k[22]-34.98565276673217 * k[23]-100.0 ~, k[24]+8.985798854637958 * k[25]-58.02445129657898 * k[26]+35.004401741288426 * k[27]+100.0 ~, k[28]-100.0 ~, k[29]+100. 0 ~, k[30]-100. 0 ~, k[31]+0.5572562992293632 * k[32]+44.7092648944652 * k[33]+100.0 ~, k[34]-19.25872577776615 * k[35]+100.0 * k[36]-67.94476290124636 * k[37]-100.0 ~, k[38]-100.0 * k[39]-100.0 * k[40]+100.0 * k[41]+100.0 * k[42]+6.146753052081801 * k[43]-6.768877030107148 * k[44]+100.0 * k[45]-100.0 * k[46]-100.0 * k[47]+ 0.7464



=== Predictions on training set ===inst#, actual, predicted, error

1 0.868 0.93 0.0632 0.801 0.654 -0.1473 0.708 0.85 0.1424 0.784 0.821 0.0375 0.935 0.936 0.0016 1.118 1.119 0.0017 0.69 0.686 -0.0048 1.041 0.993 -0.0489 0.764 0.753 -0.011

10 0.659 0.775 0.11611 1.003 1.002 -0.00112 0.922 0.922 -0.00113 0.889 0.804 -0.08414 0.896 0.887 -0.00915 0.682 0.683 0.00116 0.952 0.869 -0.08317 1.068 1.058 -0.0118 0.935 0.954 0.0219 1.127 1.127 020 1.046 1.045 021 0.864 0.863 022 0.927 0.826 -0.123 1.039 1. 045 0.00624 1.042 1.043 0.00125 0.758 0.868 0.1126 0.736 0.736 027 0.893 0.894 0.00128 0.829 0.828 -0.00129 1.015 0.996 -0.01930 1. 021 1.032 0.0131 0.997 0.979 -0.01832 0.775 0.883 0.10833 1.149 1.148 034 0.719 0.718 035 1. 082 0.996 -0.08536 1.056 1.056 0

172

37 1.08 1 -0.0838 1.022 1.022 039 0.7 0.797 0.09640 0.647 0.649 0.00241 0.654 0.702 0.04942 0.956 0.874 -0.08243 1.088 1.074 -0.01444 1.042 1.042 045 0.945 0.946 0.00146 0.815 0.81 -0.00547 0.843 0.907 0.06448 0.742 0.841 0.099

--- Evaluation on training set ------ summary ===correlation coefficient 0.9194Mean absolute error 0.0361Root mean squared error 0.0575Relative absolute error 28.4938 %Root relative squared error 39.4343 %Total Number of Instances 48



Test mode:

weka.classifiers.functions.SVMreg -c 100.0 -N 0 -I"weka.classifiers.functions.supportvector.RegsMOImproved -L0.0010 -w 1 -P 1.0E-12 -T 0.0010 -v" -K"weka.classifiers.functions.supportvector.RBFKernel -c 100 -G1.0"uth_train485xlx2x3x4yuser supplied test set: 20 instances

=== classifier model (full training set) ===

sVMreg

support vectors:-100.0 * k[O]+100.0 * k[l]-100 . 0 * k[2]-100 . 0 * k[3]-71.64936871252853 * k[4]-30.7672618284859 * k[S]+100.0 * k[6]+100. 0 * k[7]+100.0 * k[8]-100.0 * k[9]+20.845318214657524 * k[10]+12.082376455822146 * k[ll]+100.0 * k[12]+100.0 * k[13]-68.04460127731681 * k[14]+100.0 * k[15]+100.0 * k[16]-100.0 * k[17]+2.7984624882109204 * k[18]+26.314069590368774 * k[19]+100.0 * k[21]-100 .0 * k[22]-34.98565276673217 * k[23]

173

-100.0 ~( k[24]+8.985798854637958 * k[25]-58.02445129657898 * k[26]+35.004401741288426 * k[27]+100.0 ~( k[28]-100.0 ~( k[29]+100.0 ~( k[30]-100. 0 ~( k[31]+0.5572562992293632 * k[32]+44.7092648944652 * k[33]+100.0 * k[34]-19.25872577776615 * k[35]+100.0 ~( k[36]-67.94476290124636 * k[37]-100.0 ~( k[38]-100.0 * k[39]-100.0 * k[40]+100.0 * k[41]+100.0 * k[42]+6.146753052081801 * k[43]-6.768877030107148 * k[44]+100.0 * k[45]-100.0 * k[46]-100.0 * k[47]+ 0.7464



=== predictions on test set ===inst#, actual, predicted, error

1 1 0.943 -0.0572 0.927 0.891 -0.0363 0.964 0.9 -0.0644 0.69 0.662 -0.0285 1.021 0.902 -0.1196 1.054 1.094 0.0417 1.069 1. 091 0.0228 1.05 0.982 -0.0679 1.058 0.928 -0.13

10 1.056 1.108 0.05211 1.01 1. 01 012 0.699 0.72 0.02113 0.761 0.832 0.07114 0.731 0.79 0.05915 0.768 0.812 0.04416 0.831 0.896 0.06517 0.965 0.907 -0.05818 0.656 0.349 -0.30719 1.1 0.838 -0.26220 0.688 0.682 -0.006

=== Evaluation on test set ====== summary ===correlation coefficientMean absolute errorRoot mean squared errorRelative absolute errorRoot relative squared errorTotal Number of Instances


0.8130.07550.1075

53.1765 %70.2793 %20

scheme: weka.classifiers.functions.SVMreg -c 5.0 -N 0 -I"weka.classifiers.functions.supportvector.RegsMoImproved -L0.0010 -w 1 -P 1.0E-12 -T 0.0010 -v" -K

174


Test mode:

"weka.classifiers.functions.supportvector.polYKernel -c 5 -E2.0"uth_train485xlx2x3x4yuser supplied test set: 20 instances

==~ classifier model (full training set) ===

SVMreg

support vectors:-5.0 * k[O]-5.0 * k[l]-5.0 * k[2]+5.0 * k[3]+5.0 * k[4]+5.0 * k[5]-5.0 * k[6]+5.0 * k[7]-5.0 * k[8]-5.0 * k[9]+5.0 * k[10]+2.633524558135476 * k[ll]+5.0 * k[12]+5.0 * k[13]-5.0 *k[14]+5.0 * k[15]+5.0 * k[16]-1.0165578493039158 * k[18]-5.0 * k[19]-5.0 * k[20]+5.0 * k[21]+5.0 * k[22]+5.0 * k[23]-5.0 * k[24]-5.0 * k[25]+5.0 * k[26]-5.0 * k[27]+5.0 * k[28]+2.165285161086492 * k[29]+5.0 * k[30]-5.0 * k[31]-5.0 * k[32]-5.0 * k[33]+5.0 * k[34]-5.0 * k[35]+5.0 * k[36]+5.0 * k[37]-5.0 * k[38]-5.0 * k[39]-5.0 * k[40]+5.0 * k[41]+5.0 * k[42]-5.0 * k[43]+5.0 * k[44]+1.2177481300819453 * k[45]-5.0 * k[46]-5.0 * k[47]+ 0.4737


Time taken to build model: 0 seconds

175


1 1 0.901 -0.0992 0.927 0.871 -0.0553 0.964 0.839 -0.1254 0.69 0.85 0.1615 1. 021 0.916 -0.1056 1.054 1.03 -0.0247 1.069 0.994 -0.0758 1. 05 0.991 -0.0589 1.058 1.1 0.042

10 1.056 1.048 -0.00811 1. 01 1.003 -0.00812 0.699 0.853 0.15413 0.761 0.778 0.01714 0.731 0.813 0.08315 0.768 0.855 0.08816 0.831 0.9 0.06917 0.965 0.915 -0.0518 0.656 0.877 0.2219 1.1 0.912 -0.18720 0.688 0.83 0.142

Evaluation on test setSummary ===

correlation coefficientMean absolute errorRoot mean squared errorRelative absolute errorRoot relative squared errorTotal Number of Instances


0.74720.08850.1064

62.3418 %69.533 %20

scheme:


Test mode:

weka.classifiers.functions.SVMreg -c 50.0 -N 0 -I"weka.classifiers.functions.supportvector.RegsMoImproved -L0.0010 -w 1 -P 1.0E-12 -T 0.0010 -v" -K"weka.classifiers.functions.supportvector.RBFKernel -c 50 -G1.0"uth_train485xlx2x3x4yuser supplied test set: 20 instances

=== classifier model (full training set) ===

SVMreg

support vectors:-50.0 * k[O]+50.0 * k[l]-50.0 * k[2]-50.0 * k[3]-24.43273240124022 * k[4]-9.17486580253727 * k[5]+50.0 * k[6]+50.0 * k[7]+50.0 * k[8]-50.0 * k[9]+14.24950770755425 * k[10]+50.0 * k[12]+37.15791822316835 * k[13]-42.21607519559065 * k[14]+50.0 * k[15]

176

+50.0 * k[16]-50.0 * k[17]+1.1998849104045402 * k[18]+10.809179561489902 * k[19]-3.8764671495335588 * k[20]+50.0 * k[21]-36.18088575581 * k[22]-10.035307970947914 * k[23]-50.0 * k[24]-19.7526170105338 * k[25]-28.490471744812268 * k[26]+50.0 ,~ k[D]+50.0 * k[28]-50.0 -I: k[29]+50.0 -I: k[30]- 50. 0 * k[31]+2.608301536841294 * k[32]+29.54335574150932 * k[33]+50.0 * k[34]-24.956048554309984 * k[35]+50.0 * k[36]-44.92236154796964 * k[37]-50.0 * k[38]-50.0 * k[39]-50.0 * k[40]+50.0 * k[41]+50.0 * k[42]+8.05642969211064 * k[43]-9.586744239793264 * k[44]+50.0 * k[45]-50.0 * k[46]-50.0 * k[47]+ 0.6248




1 1 0.932 -0.0682 0.927 0.902 -0.0253 0.964 0.903 -0.0614 0.69 0.671 -0.0195 1.021 0.886 -0.1356 1. 054 1. 097 0.0437 1.069 1.058 -0.0118 1.05 0.975 -0.0759 1.058 1. 033 -0.024

10 1.056 1.122 0.06511 1.01 1.006 -0.00412 0.699 0.729 0.0313 0.761 0.831 0.0714 0.731 0.788 0.05715 0.768 0.811 0.04416 0.831 0.884 0.05317 0.965 0.898 -0.06618 0.656 0.41 -0.24719 1.1 1.003 -0.09720 0.688 0.683 -0.005

=== Evaluation on test set ====== summary ====Correlation coefficientMean absolute errorRoot mean squared errorRelative absolute errorRoot relative squared errorTotal Number of Instances

177

0.88690.060.0802

42.3023 %52.4284 %20

::::= Run i nformati on ======

scheme:

Relation:Instances:Attr; butes :

Test mode:

weka.classifiers.functions.SVMreg -c 50.0 -N 0 -I"weka.classifiers.functions.supportvector.RegsMo1mproved -L0.0010 -w 1 -P 1.0E-12 -T 0.0010 -v" -K"weka.classifiers.functions.supportvector.RBFKernel -c 50 -G1.0"uth_train485xlx2x3x4yevaluate on training data

=== classifier model (full training set) --

SVMreg

support vectors:-50.0 * k[O]+50.0 * k[l]-50.0 * k[2]-50.0 * k[3]-24.43273240124022 * k[4]-9.17486580253727 * k[5]+50.0 * k[6]+50.0 * k[7]+50.0 * k[8]-50.0 * k[9]+14.24950770755425 * k[10]+50.0 * k[12]+37.15791822316835 * k[13]-42.21607519559065 * k[14]+50.0 * k[15]+50.0 * k[16]-50.0 * k[17]+1.1998849104045402 * k[18]+10.809179561489902 * k[19]-3.8764671495335588 * k[20]+50.0 * k[21]-36.18088575581 * k[22]-10.035307970947914 * k[23]-50.0 * k[24]-19.7526170105338 * k[25]-28.490471744812268 * k[26]+50.0 * k[27]+50.0 * k[28]-50.0 * k[29]+50.0 * k[30]-50.0 * k[31]+2.608301536841294 * k[32]+29.54335574150932 * k[33]+50.0 * k[34]-24.956048554309984 * k[35]+50.0 * k[36]-44.92236154796964 * k[37]-50.0 * k[38]-50.0 * k[39]-50.0 * k[40]+50.0 * k[41]+50.0 * k[42]+8.05642969211064 * k[43]

178

-9.586744239793264 ~< k[44]+50.0 * k[45]-50.0 * k[46]-50.0 ~< k[47]+ 0.6248



=== Predictions on training set ===inst#,

123456789

101112131415161718192021222324252627282930313233343536373839404142434445464748

actual,0.8680.8010.7080.7840.9351.1180.691.0410.7640.6591.0030.9220.8890.8960.6820.9521.0680.9351.1271.0460.8640.9271.0391.0420.7580.7360.8930.8291.0151.0210.9970.7751.1490.7191.0821.0561.081.0220.70.6470.6540.9561. 0881.0420.9450.8150.8430.742

predicted,0.9310.6580.8470.8210.9361.1190.6840.9570.7510.7741.0020.9220.8060.8960.6830.8691.0460.9361.1271.0450.8640.8261.041.0420.8680.7360.8940.8240.9721.0230.9620.8881.1480.7180.9891.0560.9981.0220.7920.6560.7050.8721. 0461. 0410.9460.8070.90.835

error0.064

-0.1430.1390.0370.0010.001

-0.006-0.084-0.0130.115

-0.001-0.001-0.083-0.0010.001

-0.083-0.0230.001o

-0.001o

-0.10.001o0.11o0.001

-0.005-0.0430.002

-0.0340.113oo

-0.093o

-0.082o0.0910.0090.051

-0.084-0.042o0.001

-0.0080.0570.094

=== Evaluation on training set ====== Summary ===correlation coefficientMean absolute errorRoot mean squared errorRelative absolute errorRoot relative squared errorTotal Number of Instances

179

0.91620.03790.0587

29.8904 %40.2433 %48

=:::: Run i nformati on ===

Scheme:

Relati on:Instances:Attri butes :

Test mode:

weka.classifiers.functions.SvMreg -C 50.0 -N 0 -I"weka.classifiers.functions.supportvector.RegSMOImproved -L0.0010 -w 1 -p 1.0E-12 -T 0.0010 -v" -K"weka.classifiers.functions.supportvector.RBFKernel -C 50 -G2.0"


:::== Classifier model (full training set)

sVMreg

support vectors:-50.0 * k[O]+50.0 * k[l]-50.0 * k[2]-50.0 * k[3]-2.8405314609818975 * k[4]-0.5666030212208605 * k[5]+16.150873346770396 * k[6]+42.25778342250524 * k[7]+50.0 * k[8]-50.0 * k[9]+1.7995581042526412 * k[10]+29.16266932832355 * k[11]+50.0 * k[12]+50.0 * k[13]-13.398241393861227 * k[14]+50.0 * k[15]+40.34835148669473 * k[16]-50.0 * k[17] .+0.8445996980690933 * k[18]+7.208041226894296 * k[19]]-5.27738574057603 * k[20+50.0 * k[21]-50.0 * k[22]-17.151680848806482 * k[23]-50.0 * k[24]+50.0 * k[25]-21.323928268045822 * k[26]+8.020710451941666 * k[27]+0.3373695854247397 * k[28]-38.698189745277574 * k[29]+46.32000892464536 * k[30]-50.0 * k[31]+0.09238179388371164 *[k

33[]32]

+9.213728343066316 * k+50.0 * k[34]-24.60924777463711 * k[35]+50.0 * k[36]-20.057303711325783 * k[37]-50.0 * k[38]-50.0 * k[39]-50.0 * k[40]+50.0 * k[41]+36.462067886008725 * k[42]+16.060025889813865 * k[43]-10.355057523561516 * k[44]+50.0 * k[45]-50.0 * k[46]-50.0 'I, k[47]

180

+ 0.6178


~me taken to build model: 0.13 seconds

=== Predi cti ons on training set ---inst#, actual, predicted, error

1 0.868 0.917 0.0492 0.801 0.663 -0.1383 0.708 0.853 0.1454 0.784 0.793 0.0085 0.935 0.935 0.0016 1.118 1.119 07 0.69 0.689 -0.0018 1.041 1.04 -0.0019 0.764 0.75 -0.01410 0.659 0.758 0.09911 1. 003 1.002 012 0.922 0.921 -0.00113 0.889 0.786 -0.10214 0.896 0.861 -0.03515 0.682 0.683 0.00116 0.952 0.863 -0.08917 1.068 1.067 -0.00118 0.935 0.946 0.01219 1.127 1.127 020 1.046 1. 045 -0.00121 0.864 0.865 0.00122 0.927 0.812 -0.11423 1. 039 1.053 0.01424 1.042 1.042 025 0.758 0.863 0.10526 0.736 0.725 -0.01127 0.893 0.893 028 0.829 0.829 029 1.015 1.015 030 1.021 1.022 0.00131 0.997 0.996 -0.00132 0.775 0.878 0.10333 1.149 1.149 034 0.719 0.719 035 1.082 1.014 -0.06836 1.056 1.057 0.00137 1.08 1.015 -0.06538 1.022 1.023 0.00139 0.7 0.78 0.07940 0.647 0.655 0.00841 0.654 0.706 0.05242 0.956 0.872 -0.08443 1.088 1.088 044 1.042 1.041 -0.00145 0.945 0.946 0.00146 0.815 0.802 -0.01347 0.843 0.902 0.05948 0.742 0.807 0.065

=== Evaluation on training set ========= Summary ======

Correlation coefficient 0.92880.0322Mean absolute error0.0541Root mean squared error

25.4111 %Relative absolute error37.0636 %Root relative squared error48Total Number of Instances

181

17181920

0.9650.6561.10.688

0.8850.3720.8040.686

-0.08-0.284-0.296-0.002

=== Eva1uati on on test set=== summary ===

correlation coefficientMean absolute error~ot mean squared errorRelative absolute errorRoot rel ati ve squared errorTotal Number of Instances

=== Run i nformati on ===

0.7810.08040.117

56.6688 %76.4719 %20

scheme:

Relation:Instances:Attri butes:

Test mode:

weka.classifiers.functions.SVMreg -C 100.0 -N 0 -I"weka.classifiers.functions.supportVector.RegsMo1mproved -L0.0010 -w 1 -P 1.0E-12 -T 0.0010 -v" -K"weka.classifiers.functions.supportvector.polyKernel -C 80 -E1.0"uth_train485xlx2x3x4yuser supplied test set: 20 instances

=== Classifier model (full training set)

sVMreg

weights (not support vectors):0.518 * (normalized) xl

+ 0.0228 * (normalized) x2+ 1.6684 * (normalized) x3+ 0.3379 * (normalized) x4+ 0.1975



=== Predictions on test set ===inst#,

123456789

1011121314

actual,10.9270.9640.691.0211.0541.0691. 05 .1.0581.0561.010.6990.7610.731

predicted,0.8880.8870.8570.8010.8851.0680.9810.9831.2121.0890.9910.8110.8040.808

error-0.112-0.04-0.1070.111

-0.1360.015

-0.088-0.0660.1550.033

-0.020.1120.0430.078

182

15 0.768 0.824 0.05616 0.831 0.87 0.03917 0.965 0.888 -0.07618 0.656 0.742 0.08619 1.1 1.31 0.21120 0.688 0.783 0.095

=== Evaluation on test set=== Summary ===correlation coefficientMean absolute errorRoot mean squared errorRelative absolute errorRoot relative squared errorTotal Number of Instances


0.8010.08390.0964

59.0905 %62.9972 %20

Scheme:


Test mode:

weka.classifiers.functions.SVMreg -c 100.0 -N 0 -I"we ka.classifiers.functions.supportVector.RegsMOImproved -L0.0010 -w 1 -P 1.0E-12 -T 0.0010 -v" -K"weka.classifiers.functions.supportvector.RBFKernel -c 100 -G2.0"uth_train485xlx2x3x4yevaluate on training data

=== classifier model (full training set)

SVMreg

Support vectors:-100.0 * k[O]+100 . 0 * k[1]-100.0 * k[2]-91.21710273997503 * k[3]+32.14562958443941 * k[4]+8.620276087922814 * k[5]-19.10343703542555 * k[6]+58.2366071804668 * k[7]+100.0 * k[8]-100.0 * k[9]-0.4729136216663974 * k[10]+26.448109370774155 * k[11]+100.0 * k[12]+100.0 * k[13]+13.453504309544298 * k[14]+100.0 * k[15]+68.1185813814273 * k[16]-35.053347205490105 * k[17]+1.2506783114706443 * k[18]+7.438557255949097 * k[19]-9.26428874984027 * k[20]+100.0 * k[21]-100.0 * k[22]-21.4954571640254 * k[23]-100.0 i, k[24]+100.0 * k[25]-22.58434918929922 * k[26]+23.662134540205383 * k[27]-17.480753357339847 * k[28]-33.88415668170604 * k[29]+38.21801105884325 * k[30]-100.0 * k[31]

183

-0.05841967276076776 * k[32]+23.005057745492806 * k[33]+100.0 ~( k[34]-54.552566750648424 * k[35]+100. 0 ~( k[36]-34.17282656141672 * k[37]-100. 0 ~( k[38]-98.0334704757984 * k[39]-100 . 0 ~( k[40]+100.0 ~( k[41]+43.909874279119734 * k[42]+21.31282744064489 * k[43]-28.446759340908454 * k[44]+100. 0 ~( k[45]-100.0 ~( k[46]-100.0 * k[47]+ 0.6374

Number of kernel evaluations: 240164 (85.119% cached~


=== predictions on training set ===inst#,

123456789

101112131415161718192021222324252627282930313233343536373839404142434445

actual,0.8680.8010.7080.7840.9351.1180.691.0410.7640.6591.0030.9220.8890.8960.6820.9521.0680.9351.1271.0460.8640.9271.0391.0420.7580.7360.8930.8291.0151.0210.9970.7751.1490.7191.0821.0561.081.0220.70.6470.6540.9561.0881.0420.945

predicted,0.9040.6610.8550.7840.9351.1180.6911.040.7470.7551. 0030.9210.7840.850.6820.8581.0680.9361.1271.0450.8640.8021.0541.0430.8590.7090.8940.8291.0151.0220.9960.8731.150.7191.0281.0561. 0411.0220.7640.6470.7040.8711.0871.0410.945

error0.037

-0.140.147oooo

-0.001-0.0170.096o

-0.001-0.104-0.047o

-0.094o0.001o

-0.0010.001

-0.1240.0150.0010.1

-0.0270.001

-0.001oo

-0.0010.0990.001o

-0.053o

-0.039o0.064o0.05

-0.085-0.001oo

184

464748

0.8150.8430.742

0.7990.8960.782

-0.0160.0530.04

=== Evaluation on training set=== summary ===correlation coefficientMean absolute errorRoot mean squared errorRelative absolute errorRoot relative squared errorTotal Number of Instances


0.93270.03040.0526

23.9728 %36.0945 %48

scheme:


Test mode:

weka.classifiers.functions.SVMreg -c 100.0 -N 0 -I"weka.classifiers.functions.supportvector.RegsMoImproved -L0.0010 -w 1 -P 1. OE-12 -T 0.0010 -v" -K"weka.classifiers.functions.supportvector.RBFKernel -c 100 -G2.0"uth_train485xlx2x3x4yuser supplied test set: 20 instances

=== classifier model (full training set) ===SVMreg

support vectors:-100.0 * k[O]+100 .0 * k[1]-100.0 * k[2]-91.21710273997503 * k[3]+32.14562958443941 * k[4]+8.620276087922814 * k[5]-19.10343703542555 * k[6]+58.2366071804668 * k[7]+100.0 * k[8]-100.0 * k[9]-0.4729136216663974 * k[10]+26.448109370774155 * k[ll]+100.0 * k[12]+100.0 * k[13]+13.453504309544298 * k[14]+100.0 * k[15]+68.1185813814273 * k[16]-35.053347205490105 * k[17]+1.2506783114706443 * k[18]+7.438557255949097 * k[19]-9.26428874984027 * k[20]+100.0 * k[21]-100.0 'I: k[22]-21.4954571640254 * k[23]-100.0 * k[24]+100.0 * k[25]-22.58434918929922 * k[26]+23.662134540205383 * k[27]-17.480753357339847 * k[28]-33.88415668170604 * k[29]+38.21801105884325 * k[30]-100.0 'I: k[31]

185

-0.05841967276076776 * k[32]+23.005057745492806 * k[33]+100 . 0 ~, k[34]-54.552566750648424 * k[35]+100 . 0 * k[36]-34.17282656141672 * k[37]-100 . 0 ~, k[38]-98.0334704757984 * k[39]-100 . 0 ~, k[40]+100.0 ~, k[41]+43.909874279119734 * k[42]+21.31282744064489 * k[43]-28.446759340908454 * k[44]+100 . 0 ~, k[45 ]-100.0 * k[46]-100.0 * k[47]+ 0.6374




1 1 0.95 -0.052 0.927 0.874 -0.0533 0.964 0.879 -0.0854 0.69 0.66 -0.0295 1.021 0.898 -0.1236 1.054 1. 091 0.0377 1.069 1.127 0.0588 1.05 1.041 -0.0099 1.058 0.638 -0.42

10 1.056 1.07 0.01411 1.01 0.996 -0.01412 0.699 0.719 0.0213 0.761 0.794 0.03314 0.731 0.784 0.05315 0.768 0.796 0.02816 0.831 0.884 0.05317 0.965 0.869 -0.09618 0.656 0.347 -0.3119 1.1 0.625 -0.47520 0.688 0.683 -0.005

Evaluation on test setsummary ===

correlation coefficientMean absolute errorRoot mean squared errorRelative absolute errorRoot relative squared errorTotal Number of Instances


0.62050.09830.1656

69.2316 %108.2147 %

20

scheme:


weka.classifiers.functions.SVMreg -c 5.0 -N 0 -I"weka.classifiers.functions.supportvector.RegsMoImproved -L0.0010 -w 1 -P 1.0E-12 -T 0.0010 -v" -K"weka.classifiers.functions.supportvector.polyKernel -c 5 -E2.0"

uth_train485

186

Test mode:

xlx2x3x4yevaluate on training data

=== classifier model (full training set)

SVMreg

Support vectors:-5.0 * k[O]-5.0 * k[l]-5.0 * k[2]+5.0 * k[3]+5.0 * k[4]+5.0 * k[5]-5.0 * k[6]+5.0 * k[7]-5.0 * k[8]-5.0 * k[9]+5.0 * k[10]+2.633524558135476 * k[ll]+5.0 * k[12]+5.0 * k[l3]-5.0 * k[14]+5.0 * k[15]+5.0 * k[16]-1.0165578493039158 * k[18]-5.0 * k[19]-5.0 * k[20]+5.0 * k[21]+5.0 * k[22]+5.0 * k[23]-5.0 * k[24]-5.0 * k[25]+5.0 * k[26]-5.0 * k[27]+5.0 * k[28]+2.165285161086492 * k[29]+5.0 * k[30]-5.0 * k[31]-5.0 * k[32]-5.0 * k[33]+5.0 * k[34]-5.0 * k[35]+5.0 * k[36]+5.0 * k[37]-5.0 * k[38]-5.0 * k[39]-5.0 * k[40]+5.0 * k[41]+5.0 * k[42]-5.0 * k[43]+5.0 * k[44]+1.2177481300819453 * k[45]-5.0 * k[46]-5.0 * k[47]

+ 0.4737


Time taken to build model: 0 seconds

~== predictions on training set ===

inst#,12

actual, predicted, error0.868 0.869 0.0010.801 0.835 0.035

187

3 0.708 0.832 0.1244 0.784 0.763 -0.0215 0.935 0.88 -0.0556 1.118 1.044 -0.0747 0.69 0.788 0.0988 1.041 0.92 -0.1219 0.764 0.825 0.061

10 0.659 0.775 0.11611 1.003 0.969 -0.03412 0.922 0.922 -0.00113 0.889 0.776 -0.11214 0.896 0.796 -0.115 0.682 0.782 0.116 0.952 0.87 -0.08217 1.068 1. 023 -0.04618 0.935 0.935 0.00119 1.127 1.128 020 1.046 1.151 0.10521 0.864 0.931 0.06722 0.927 0.881 -0.04623 1. 039 1.026 -0.01424 1.042 0.908 -0.13425 0.758 0.856 0.09726 0.736 0.876 0.1427 0.893 0.877 -0.01628 0.829 0.833 0.00429 1.015 0.944 -0.07130 1.021 1.02 -0.00131 0.997 0.9 -0.09732 0.775 0.854 0.0833 1.149 1. 269 0.12134 0.719 0.853 0.13435 1.082 0.949 -0.13236 1.056 1.065 0.0137 1.08 1.029 -0.05138 1.022 1.018 -0.00439 0.7 0.884 0.18440 0.647 0.846 0.19941 0.654 0.829 0.17542 0.956 0.844 -0.11243 1.088 0.891 -0.19744 1.042 1.085 0.04345 0.945 0.866 ,.0.07946 0.815 0.814 -0.00147 0.843 0.892 0.04948 0.742 0.909 0.167

--- Evaluation on training set ===--- Summary ===

correlation coefficient 0.7568Mean absolute error 0.0773Root mean squared error 0.096Relative absolute error 60.9804 %Root relative squared error 65.8135 %Total Number of Instances 48

:=== Run information ===

scheme:


weka.classifiers.functions.SMOreg -c 50.0 -N 0 -I"weka.classifiers.functions.supportvector.RegsMOlmproved -L0.0010 -w 1 -P 1.0E-12 -T 0.0010 -V" -K"weka.classifiers.functions.supportvector.RBFKernel -c 50 -G1.0"uth_train485xlx2x3x4

188

Test mode:yevaluate on training data

=== Classifier model (full training set)

SMOreg

support vectors:-50.0 * k[O]+50.0 * k[1]-50.0 * k[2]-50.0 * k[3]-24.43273240124022 * k[4]-9.17486580253727 * k[5]+50.0 * k[6]+50.0 * k[7]+50.0 ;: k[8]-50.0 * k[9]+14.24950770755425 * k[10]+50.0 * k[12]+37.15791822316835 * k[13]-42.21607519559065 * k[14]+50.0 * k[15]+50.0 * k[l6]-50.0 ;: k[17]+1.1998849104045402 * k[18]+10.809179561489902 * k[19]-3.8764671495335588 * k[20]+50.0 * k[21]-36.18088575581 * k[22]-10.035307970947914 * k[23]":50.0 * k[24]-19.7526170105338 * k[25]-28.490471744812268 * k[26]+50.0 * k[27]+50.0 * k[28]-50.0 * k[29]+50.0 * k[30]-50.0 ;: k[31]+2.608301536841294 * k[32]+29.54335574150932 * k[33]+50.0 * k[34]-24.956048554309984 * k[35]+50.0 * k[36]-44.92236154796964 * k[37]-50.0 * k[38]-50.0 * k[39]-50.0 * k[40]+50.0 * k[41]+50.0 * k[42]+8.05642969211064 * k[43]-9.586744239793264 * k[44]+50.0 ;: k[45]-50.0 J. k[46]-50.0 * k[47]+ 0.6248



=== Predictions on training set ===

inst# actual predicted error1 0.868 0.931 0.0642 0.801 0.658 -0.1433 0.708 0.847 0.1394 0.784 0.821 0.0375 0.935 0.936 0.0016 1.118 1.119 0.0017 0.69 0.684 -0.006

189

8 1.041 0.957 -0.0849 0.764 0.751 -0.013

10 0.659 0.774 0.11511 1.003 1. 002 -0.00112 0.922 0.922 -0.00113 0.889 0.806 -0.08314 0.896 0.896 -0.00115 0.682 0.683 0.00116 0.952 0.869 -0.08317 1.068 1.046 -0.02318 0.935 0.936 0.00119 1.127 1.127 020 1.046 1.045 -0.00121 0.864 0.864 022 0.927 0.826 -0.123 1.039 1. 04 0.00124 1.042 1.042 025 0.758 0.868 0.1126 0.736 0.736 027 0.893 0.894 0.00128 0.829 0.824 -0.00529 1.015 0.972 -0.04330 1.021 1. 023 0.00231 0.997 0.962 -0.03432 0.775 0.888 0.11333 1.149 1.148 034 0.719 0.718 035 1.082 0.989 -0.09336 1.056 1.056 037 1.08 0.998 -0.08238 1.022 1.022 039 0.7 0.792 0.09140 0.647 0.656 0.00941 0.654 0.705 0.05142 0.956 0.872 -0.08443 1. 088 1. 046 -0.04244 1.042 1.041 045 0.945 0.946 0.00146 0.815 0.807 -0.00847 0.843 0.9 0.05748 0.742 0.835 0.094

--- Evaluation on training set ===--- summary ===correlation coefficient 0.9162Mean absolute error 0.0379Root mean squared error 0.0587Relative absolute error 29.8904 %Root relative squared error 40.2433 %Total Number of Instances 48



weka.classifiers.functions.SVMreg -c 50.0 -N 0 -I"weka.classifiers.functions.supportVector.RegsMo1mproved -L0.0010 -w 1 -P 1.0E-12 -T 0.0010 -v" -K"weka.classifiers.functions.supportvector.RBFKernel -c 50 -G1.0"uth_train485xlx2x3

190

Test mode:

x4yuser supplied test set: 20 instances

=== classifier model (full training set) ===SVMreg

support vectors:-50.0 * k[O]+50.0 1< k[1]-50.0 * k[2]-50.0 * k[3]-24.43273240124022 * k[4]-9.17486580253727 * k[5]+50.0 1< k[6]+50.0 * k[7]+50.0 1< k[8]-50.0 * k[9]+14.24950770755425 * k[10]+50.0 * k[12]+37.15791822316835 * k[13]-42.21607519559065 * k[14]+50.0 * k[15]+50.0 * k[16]-50.0 * k[17]+1.1998849104045402 * k[18]+10.809179561489902 * k[19]-3.8764671495335588 * k[20]+50.0 1< k[21]-36.18088575581 * k[22]-10.035307970947914 * k[23]-50.0 * k[24]-19.7526170105338 * k[25]-28.490471744812268 * k[26]+50.0 * k[27]+50.0 1< k[28]-50.0 * k[29]+50.0 1< k[30]-50.0 * k[31]+2.608301536841294 * k[32]+29.54335574150932 * k[33]+50.0 * k[34]-24.956048554309984 * k[35]+50.0 * k[36]-44.92236154796964 * k[37]-50.0 * k[38]-50.0 * k[39]-50.0 * k[40]+50.0 * k[41]+50.0 1< k[42]+8.05642969211064 * k[43]-9.586744239793264 * k[44]+50.0 * k[45]-50.0 * k[46]-50.0 * k[47]+ 0.6248



=== Predictions on test set ===inst#,

12345

actual,10.9270.9640.691.021

predicted,0.9320.9020.9030.6710.886

error-0.068-0.025-0.061-0.019-0.135

191

6789

1011121314151617181920

1.0541. 0691.051.0581. 0561.010.6990.7610.7310.7680.8310.9650.6561.10.688

1.0971. 0580.9751.0331.1221.0060.7290.8310.7880.8110.8840.8980.411. 0030.683

0.043-0.011-0.075-0.0240.065

-0.0040.030.070.0570.0440.053

-0.066-0.247-0.097-0.005

--- Evaluation on test set ====== summary ===Correlation coefficientMean absolute errorRoot mean squared errorRelative absolute errorRoot relative squared errorTotal Number of Instances

*

0.88690.060.0802

42.3023 %52.4284 %20

192

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