The Use of Neural Network in Determining the Ideality of

Day for Cloud Seedingby

Cuesta, Yvanne Christine R.Uy, Ma. Ro-anne R.

CLOUD SEEDING

A type of weather modification

Treatment used to increase precipitation – to provide water on dams and agricultural fields during drought seasons

CLOUD SEEDING OPERATIONPreflight ObservationProceed? Yes or No

In-Flight Observation

Post flight ObservationMagnitude of precipitation?Drizzle/Light/Moderate/ Heavy

ISSUES Requires large amount of funds Doesn’t produce enough rain Rain do not fall on the right location Some believe that it does not work successfully at

all…

In other words… A large amount of money is wasted

There is no established quantitative way of pursuing cloud seeding.

Neural Network information processing model

inspired by the human brain used in classification through

pattern based learning

Process Flowchart

Training of program

Testing and Validation of Program

Acquisition and Compilation of

Cloud Seeding Data

Conversion of data into data input

Acquisition and Programming of Implementing

Software

Time Cover Speed Humidity Pressure Output14 50 5 62 1010 212 50 8 61 1004 1

15.3 20 5 67 1002 210.3 25 2 79 1009.5 210.3 30 3 74 1002 313 20 4 64 1008 39.2 20 10 64 1006 210 30 5 69.4 1006 315 25 8 64 1008 315 25 8 66 1006 3

14.49 30 8 71 1006 29.3 25 5 67 1010 19.2 25 7 64 1007 2

11.15 25 6 66 1006.8 313 20 4 50 1008 2

Cloud Seeding Data

Generated Values of Training PhaseRow Id. Predicte

d ValueActual Value Residual Time Cover Speed Humidity Pressure

1 2.649967 2

-0.64996

714 50 5 62 1010

4 2.65435 2 -0.65435 10.3 25 2 79 1009.5

5 2.648228 3 0.35177

2 10.3 30 3 74 1002

6 2.663887 3 0.33611

3 13 20 4 64 1008

9 2.653325 3 0.34667

5 15 25 8 64 1008

10 2.651824 3 0.34817

6 15 25 8 66 1006

11 2.645084 2

-0.64508

414.49 30 8 71 1006

12 2.652481 1

-1.65248

19.3 25 5 67 1010

Generated Values of Training Phase

0 1 2 3 4 5 6 7 8 902468

101214161820

Lift chart (training dataset)

Cumulative Output when sorted using predicted valuesCumulative Output us-ing average

# cases

Cum

ulat

ive

Generated Values of Validation Phase

Row Id. Predicted Value

Actual Value Residual Time Cover Speed Humidity Pressure

3 2.899099 2

-0.89909

915.3 20 5 67 1002

14 2.442311 3 0.55768

9 11.15 25 6 66 1006.8

15 2.578017 2

-0.57801

713 20 4 50 1008

Generated Values of Validation Phase

0.5 1 1.5 2 2.5 3 3.5012345678

Lift chart (validation dataset)

Cumulative Output when sorted using predicted valuesCumulative Output us-ing average

# cases

Cum

ulat

ive

Generated Values of Testing Phase

Row Id. Predicted Value

Actual Value Residual Time Cover Speed Humidity

2 2.58298 1 -1.58298 12 50 8 61

7 2.111562 2 -0.111562 9.2 20 10 64

8 2.395388 3 0.604612 10 30 5 69.4

13 2.129739 2 -0.129739 9.2 25 7 64

Generated Values of Testing Phase

0.5 1 1.5 2 2.5 3 3.5 4 4.50123456789

Lift chart (test dataset)

Cumulative Output when sorted using predicted valuesCumulative Output us-ing average

# cases

Cum

ulat

ive

Summary

PhaseTotal Sum of

Squared ErrorsRMS (Root

Mean Square) Error

Average Error

Training 4.475583417 0.747962517 -0.27739325

Validation 1.006171177 0.579128995 -0.32756267

Testing 3.63599732 0.953414564 -0.643184

t-Test

Phase Mean Standard Deviation

Significant Difference

Training

Validation

Testing

Conclusion The ranges in which cloud seeding operations are

likely to be successful

Time Observed 8-10 A.M. / 3 -5 P.M.Cloud Cover 50-75% (4 – 6 Oktas)Wind Speed (and Direction)

2 – 19 km/h (1-10 Knots) / (Direction varies based on the location)

Humidity 60% +Barometric Pressure 980-1010 mb It is possible to establish a more accurate and

precise way of predicting the ideality of Day for cloud seeding.

Recommendations

Wide range of cloud seeding data wherein taking into consideration other factors besides from the given factors

Usage of Backpropagation Neural Network

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