1. URBAN WATER DEMAND FORECASTING USING ARTIFICIAL NEURAL NETWORKS: A CASE STUDY OF BANGKOK. BY VICTOR SHINDE

2. CONTENTS Need for water demand forecasting Description of ANN Study area description Results of the study Conclusions

3. INTRODUCTION Need for water demand forecasting Need for water demand forecasting Water is a finite resource. Expanding the capacity of a water distribution system. Improving the reliability of supply. Effecting demand management instruments. Procurement of investment.

4. DESCRIPTION OF ANN Why use ANN? Accounts for non linearity between inputs and outputs Uses a universal function to convert inputs to output, for all types of problems. More realistic forecasts.

5. DESCRIPTION OF ANN A network which mimics the human brain. Input layerHidden LayerOutput layer INPUTINPUT OUTPUTOUTPUT Connection links each having some weight ‘w’ w 1j w 2j w 3j x1x1 x2x2 x3x3 j y j = f(x 1 w 1j +x 2 w 2j +x 3 w 3j ) f = Transfer function = (1/(1+e -t )

6. DESCRIPTION OF ANN Network Training Input layerHidden LayerOutput layer INPUTINPUT Output Desired Output Compares Cost function E =

7. STUDY AREA DESCRIPTION Main study area Metropolitan Waterworks Authority (MWA) responsibility area – Bangkok Metropolis, Nontaburi & Samut Prakarn Secondary study areas Hanoi (Vietnam) and Chiang Mai (Thailand)

8. STUDY AREA DESCRIPTION MWA responsibility area (2007 statistics) Population : 7.86 Million Population served: 7.36 Million (93.6%) Average Daily production : 5.52 MCM Non Revenue Water : 30.32 % Secondary study areas Secondary study areas

9. Overall objective Overall objective To develop ANN models to forecast the water demand for MWA – Bangkok. Specific objectives Specific objectives forecast ● To forecast the short term and long term water demand for MWA. identify ● To identify the factors most crucial in determining the short term and long term water demands for MWA. compare ● To compare the factors influencing long term demand for Bangkok, Hanoi and Chiang Mai OBJECTIVES OF THE STUDY

10. Demand Short term (ST) demand – Daily demand, 1,2 & 3 days lead Long term (LT) demand – Monthly demand, 1,2 & 6 months lead Data used ST Demand – Historical demand (sales), Rainfall, RH, Mean Temp LT Demand – Historical demand (sales), Population, GPP, Household connections, Education status, Rainfall, RH, Max Temp For comparing the cities (Bangkok, Hanoi and Chiang Mai) – Same as LT Demand, only production in lieu of sales & Mean Temp instead of Max Temp Software used – ANN NeuroSolutions SCOPE OF THE STUDY

11. Methodology for both ST and LT demand models Methodology for both ST and LT demand models SCOPE OF THE STUDY Data Collection and Analysis Input Selection Model Training & Testing for 1 st set Sensitivity analysis Omission of least sensitive variables Training & Testing for 2 nd set Correlation Matrix Pruning & Construction Architectures MLP GFF RBF Transfer functions Hyperbolic tan Sigmoid Learning Rules Backward Descend Conjugate Gradient 150 ST Models – 15 sets 88 LT Models – 6 sets 60 Comparison models

12. RESULTS Short term demand, 1 day lead Input Selection : Zhang et al. (2006), Msiza et al. (2007) Selected Variables: Mean Temperature, Rainfall and RH

13. RESULTS

14. RESULTS AARE = RMSE = (Threshold static) x = (n/N) x 100 Zhang et al. (2008) Adamowski (2008) Ghiassi et al. (2007) Jain et al. (2000)

15. Input Variables: HWD, Mean Temp & Rainfall RESULTS

16. RESULTS Input Variables: Only HWD Input Variables: HWD -1, HWD -2.

17. RESULTS Input Variables: HWD -1, HWD -2 & HWD -3. Input Variables: HWD -1 through HWD -7

18. Master model for seven consecutive day forecast Input variables: HWD, Rainfall, Mean Temperature & RH

19. Best fit models for Short term Demand RESULTS

20. RESULTS Best fit models for Long term Demand

21. Factors influencing ST & LT Demand Sensitivity Analysis Standardize all data Y = & σ are the mean and standard deviation Increases and decreases the input variables between the standardized -1 and +1 Thus a standardized value of ‘zero’ represents the mean of the sample Presents the trend of change in the demand. RESULTS

22. Factors influencing ST & LT Demand RESULTS

23. 20 models prepared for each city using MLP & GFF Input Variables: Population, GPP, Household connections, Education status, HWD, Rainfall, Max Temperature, RH Input Variables: Population, GPP, Household connections, Education status, HWD, Rainfall, Max Temperature, RH Best fit model results, AARE Bangkok – 1.06% Hanoi – 2.18 % Chiang Mai – 1.26% Sensitivity analysis to determine influencing variables Demand models for Bangkok, Hanoi & Chiang Mai RESULTS

24. RESULTS

25. CONCLUSIONS ANN can provide the MWA with a powerful instrument to forecast the demands. Forecasting accuracy will be over 98% for both ST & LT. Advantages for MWA Schedule pumping operations Reduce detention time to improve water quality Monthly revenues can be estimated upto 6 months in advance Diversions, Basin transfers can be planned in dry years Factors Influencing MWA sales demand ST Demand : Historical water demand LT Demand : Education status & Household connections Comparison of factors influencing production demands of Bangkok, Hanoi and Chiang Mai Bangkok : HH connections, GPP and Education Hanoi: Education status, Mean Temperature and Population Chiang Mai: HH connections, Mean Temperature & Rainfall (This information could prove vital for goverments, international agencies and funding organizations)