APPLICATION OF NEURO-GENETIC OPTIMIZER APPLICATION OF NEURO-GENETIC OPTIMIZER FOR SEDIMENT FORECASTING FOR SEDIMENT FORECASTING

IN LAM PHRA PHLOENG RESERVOIRIN LAM PHRA PHLOENG RESERVOIR

Thanyalak Iamnarongrit Assoc. Prof. Kampanad Bhaktikul,

Assoc. Prof. Chalie Navanugraha,

Prof. Thongplew KongjunFaculty of Environment and Resource Studied, Mahidol University

- Background of the study Background of the study

- Neuro-genetic Optimizer ModelNeuro-genetic Optimizer Model

- MethodologyMethodology

- ResultsResults

- ConclusionsConclusions

Upper catchments

LamTaKlong

LamPhraPhloeng

MunBon

LamChae

Reservoir

River Basin

Mun River Basin

Khao YaiKhao Yai

Land Use Type in Lam Phra Phloeng River BasinLand Use Type in Lam Phra Phloeng River Basin

Land Use Type

1973 1987 1991 2000

Rai % Rai % Rai % Rai %

Dry Evergreen Forest

268,816

52.47

101,921

19.89

99,945

19.51

98,435

19.21

Dry Dipterocarp Forest

3,696

0.72

2,402

0.47

2,229

0.44

890 0.17

Water Bodies

8,314

1.62

8,148

1.59

7,678

1.50

9,789

1.91

Forest Plantation

808 0.16

25.819

5.04

32,127

6.27

62,709

12.24

Residence

64 0.01

273 0.05

315 0.06

440 0.09

Orchard 258 0.05

1,418

0.28

2,265

0.44

4,568

0.89

Crop 230,403

44.97

372,378

72.68

367,799

71.78

335,528

65.49

Total 512,359

100 512,359

100 512,359

100 512,359

100

Thongchai Charupput (2002)

Storage Volume had decreased

LamTaKlong

LamPhraPhloeng

MunBon

LamChae

Reservoir

River Basin

Mun River Basin

Upper catchments

Capacity of Lam Phra Phloeng Capacity of Lam Phra Phloeng Reservoir from 1970 to 2004 Reservoir from 1970 to 2004

Year

Capacity of Dam(mcm)

Year

Period

(year)

Decrease of

Capacity(mcm)

1970

150 - - 0

1983

1211970 to

198313 29

1991

1081983 to

19918 13

2004

1041991 to

200413 4

Total1970 to

2004 34 46

Royal Irrigation Department (2004)

Background of the StudyBackground of the Study ((Con’tCon’t))

Most of previous researches concerning sediment in watershed area Linear model to find association

between land use changes in the area and sediment volume.

Dynamic in characteristics with rapid changes that occur constantly.

Non-Linearity Model

Neuro-genetic OptimizerNeuro-genetic Optimizer ModelModelNeuro-genetic OptimizerNeuro-genetic Optimizer ModelModel

Hybrid Model Artificial Neural Network (ANNs) and

Genetic algorithm (GAs) GAs in the structural improvement of

network and selecting key variables as one way to solve problem that

could applied with solving existing problems.

Recognize pattern and find association among various affecting factors and use them in forecasting.

Structure of Neuro-genetic OptimizerStructure of Neuro-genetic Optimizer

e

e

e

e

Inputs

Hidden Layer

Outputs

Target

Back-propagat

ion errors

Neuro-genetic algorithms

MethodologyMethodology

Collection and Analysis data

Land Use Data Analysis

The Estimating of Soil Loss with the Universal Soil Loss Equation

Application of Neuro-genetic Optimizer model

ResultsResults

Land Use Change Evaluation of Sediment

from Soil Erosion Application of Neuro-

genetic Optimizer model

The Change of Land Use in Lam Phra Phloeng River Basin between 2002 and 2005 Land Use Type

Area (rai)

2002

Area (rai)

2005 Changing (rai)

Changing Percentage

1. Agricultural Area 244,661 215,902 -28.759 5.68

- Mixed upland crop Crops 239,773 209,872 -29,901 5.91

- Sugarcane 2,837 3,646 +809 0.16

- Orchard 2,051 2,384 +333 0.07

2. Forest Area 256,146 205,926 -50,220 9.93

- Dry Evergreen Forest 30,374 25,195 -5,179 1.02

- Dry Dipterocarp Forest 225,772 180,731 -45,041 8.91

3. Miscellaneous Area 1,842 80,669 +78,827

15.59

- Open Land 1,842 80,669 +78,827 15.59

4. Water Area 3,060 3,212 +152 0.03

- Water Bodies 3,060 3,212 +152 0.03

Total 505,709 505,709 0.00 0.00

Soil Erosion Classes above Upper Lam Phra Phloeng Soil Erosion Classes above Upper Lam Phra Phloeng Reservoir between 2002Reservoir between 2002 and 2005and 2005

Soil loss Rating

Area in 2002

Area in 2005 Total Sediment

from Soil Erosion

(tons/year) in 2002

Total Sediment from Soil Erosion

(tons/year) in 2005Rai % Rai %

Very Slight

313,226

60.95

282,605

55.15 23,227 27,777

Slight 39,665

7.5186,3

6116.85 12,328 70,803

Moderate

154,187

30.0134,736

26.29 79,252 176,783

Severe 4,093 0.80

4,516

0.88 21,371 34,174

Very Severe

3,817 0.744,21

90.82 60,594 72,576

Total 514,988

100512,437

100 196,771 382,11

2

inin

inin

Before CalibrationBefore Calibration

R2 = 1RMSE = 0.58RMSE = 0.58

After CalibrationAfter Calibration

Sediment Comparison between Actual Data, Regression Model, and Neuro-genetic OptimizerSediment Comparison between Actual Data, Regression Model, and Neuro-genetic Optimizer

MonthsActual Data

Regression model

Neuro-Genetic Optimizer

April 12 9.8 3.9May 549 479.0 460.9June 86 83.6 85.5July 222 229.7 226.9

August 41 40.4 40.1September 25,896 23,747.0 26,168.8

October 5,621 5,529.5 5,554.5November 14,581 13,306.5 15,450.9December 878 1002.5 969.2January 309 361.4 350.3February 130 154.4 156.0

March 372 402.7 389.0

Annual Sediment Volume (tons) 48,697 45,346 49,856Different from

actual data (ton)

3,551 1,160

Y = 198.48x 1.1783

Comparision of Sediment between Actual, Regerssion model and Neuro-Genetic Optimizer

0

5,000

10,000

15,000

20,000

25,000

30,000

1 2 3 4 5 6 7 8 9 10 11 12 13

Month

Sedi

men

t (To

n)

Actual

Regression Model

Neural-Genetic OptimizerNeuro – genetic Optimizer

Neuro–genetic Optimizer

Forest area decreased approximately 36%, which was converted to agricultural.

Land use change affects the sediment volume due to soil loss.

Neuro-genetic Optimizer model provided forecast results for the

Lam Phra Phloeng reservoir closer to the actual sediment volume

than the regression model.

CONCLUSIONCONCLUSIONSS

The index of efficiency for Neuro-genetic Optimizer model was approximately 99%.

The forecast did not require much data.

Saved time and Expenses involved in the data collection

process.

CONCLUSIONS (Con’t)CONCLUSIONS (Con’t)

The Neuro-genetic Optimizer model is

appropriate to be apply and aid the decision making

process and further planning of reservoir management in the dynamic ecosystem and

land use change.

CONCLUSIONS (Con’t)CONCLUSIONS (Con’t)

Flowchart of Neuro-genetic

Optimizer

AN

N

sP

roces

s

GA s

Pro

cess

Factors - Land Use Change - Rainfall - Runoff

Regression model Calibration and Validation

Sediment from Model

Methodological Framework

Methodological Framework

Analysis of data

Neuro-genetic Optimizer Model

Test and Verification compare with Actual Sediment

Analysis and Conclusion

Correlation Coefficient of

variable

Correlation Coefficient of

variableDividing

Data Span

Sediment

calculated from

USLE

Sensitivity AnalysisSensitivity Analysis ( (Con’tCon’t))

Sensitivity to Weight

0

0.5

1

1.5

2

2.5

3

0 1 2 3 4 5 6

Weight

Fitn

ess

Fitness

Sensitivity to Momentum

00.5

11.5

22.5

3

0 0.2 0.4 0.6 0.8 1 1.2

Momentum

Fitn

ess

Fitness

Sensitivity to Learning Rate

0

0.5

1

1.5

2

2.5

3

0 0.2 0.4 0.6 0.8 1 1.2

Learning rate

Fitn

ess

Fitness

Sensitivity to Population

1.952

2.052.1

2.152.2

2.252.3

2.35

0 50 100 150 200 250 300 350

Population

Fitn

ess

Fitness

ObjectivesObjectives

1 . To study the land use changes in Lam Phra Phloeng river basin which affected sediment load in reservoir using LANDSAT-5 TM.

2. To apply Neuro-genetic Optimizer model in forecasting the sediment in Lam Phra Phloeng reservoir.

3. To compare results among Neuro-genetic Optimizer model, Regression Model, and the real data of sediment load in the reservoir.

Soil Unit Topographic Map Rainfall Data LANDSAT-TM 2002 &2005

Soil Map

DEM Map

Slope Map

LS-factorK-factor

Digital Image Processing

Supervised Classification

Land Use Map

C-factor

Isohyets Map

R-factor

Soil Erosion Hazard Model (USLE)

Elevation Map

Digitizing

Scanning

Interpulation

Schematic of Soil Erosion Hazard Model in Lam Phra Phloeng River Basin Schematic of Soil Erosion Hazard Model in Lam Phra Phloeng River Basin