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APPLICATION OF NEURO-GENETIC OPTIMIZER FOR SEDIMENT FORECASTING IN LAM PHRA PHLOENG RESERVOIR

APPLICATION OF NEURO-GENETIC OPTIMIZER FOR SEDIMENT FORECASTING IN LAM PHRA PHLOENG RESERVOIR. Thanyalak Iamnarongrit Assoc. Prof. Kampanad Bhaktikul, Assoc. Prof. Chalie Navanugraha, Prof. Thongplew Kongjun. Faculty of Environment and Resource Studied, Mahidol University. Outline.

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APPLICATION OF NEURO-GENETIC OPTIMIZER FOR SEDIMENT FORECASTING IN LAM PHRA PHLOENG RESERVOIR

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  1. APPLICATION OF NEURO-GENETIC OPTIMIZER FOR SEDIMENT FORECASTING IN LAM PHRA PHLOENG RESERVOIR Thanyalak Iamnarongrit Assoc. Prof. Kampanad Bhaktikul, Assoc. Prof. Chalie Navanugraha, Prof. Thongplew Kongjun Faculty of Environment and Resource Studied, Mahidol University

  2. Outline • Background of the study • Neuro-genetic Optimizer Model • Methodology • Results • Conclusions

  3. Mun River Basin Introduction Reservoir River Basin LamTaKlong MunBon LamChae LamPhraPhloeng Upper catchments Khao Yai

  4. Land Use Type in Lam Phra Phloeng River Basin Thongchai Charupput (2002)

  5. Mun River Basin Introduction Reservoir River Basin LamTaKlong MunBon Storage Volume had decreased LamChae LamPhraPhloeng Upper catchments

  6. Capacity of Lam Phra Phloeng Reservoir from 1970 to 2004 Royal Irrigation Department (2004)

  7. Background of the Study(Con’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

  8. Neuro-genetic OptimizerModel • 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.

  9. Inputs Hidden Layer Outputs Target e e e e Neuro-genetic algorithms Back-propagation errors Structure of Neuro-genetic Optimizer

  10. Methodology

  11. Methodology • Collection and Analysis data • Land Use Data Analysis • The Estimating of Soil Loss with the Universal Soil Loss Equation • Application ofNeuro-genetic Optimizermodel

  12. Results • Land Use Change • Evaluation of Sediment from Soil Erosion • Application ofNeuro-genetic Optimizermodel

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

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

  15. in

  16. in

  17. Application of Neuro-genetic Optimizer

  18. Before Calibration

  19. After Calibration R2 = 1 RMSE = 0.58

  20. Comparison of the Model

  21. Sediment Comparison between Actual Data, Regression Model, and Neuro-genetic Optimizer Y = 198.48x 1.1783

  22. Neuro–genetic Optimizer Neuro – genetic Optimizer

  23. CONCLUSIONS • 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.

  24. CONCLUSIONS (Con’t) • 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.

  25. 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.

  26. Thank You for Your Attention

  27. GAs Process ANNs Process Flowchart ofNeuro-genetic Optimizer

  28. Methodological Framework Factors -Land Use Change - Rainfall - Runoff Analysis of data Correlation Coefficient of variable Correlation Coefficient of variable Dividing Data Span Regression model Calibration and Validation Neuro-genetic Optimizer Model Sediment from Model Sediment calculated from USLE Test and Verificationcompare with Actual Sediment Analysis and Conclusion

  29. Sensitivity Analysis (Con’t)

  30. Objectives 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.

  31. Scanning Digitizing Interpulation Soil Unit Topographic Map Rainfall Data LANDSAT-TM 2002 &2005 Elevation Map Digital Image Processing Soil Map DEM Map Supervised Classification Slope Map Isohyets Map Land Use Map K-factor LS-factor R-factor C-factor Soil Erosion Hazard Model (USLE) Schematic of Soil Erosion Hazard Model in Lam Phra Phloeng River Basin

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