Forecasting water demand under climate change using artificial neural network: a case study of Kathmandu Valley, Nepal

With a water demand of 370 MLD, Kathmandu Valley is currently facing a water shortage of 260 MLD. The Melamchi Water Supply Project (MWSP) is an interbasin project aimed at diverting 510 MLD of water in three phases (170 MLD in each phase). Phase I of the project was expected to complete by 2018. Water demand forecasting is the first and important activity in managing water supply. Using the socio-economic factors of number of connections, water tariff and ratio of population to number of university students and climatic factor of annual rainfall, artificial neural network (ANN) was used to predict the water demand of Kathmandu Valley until the year 2040. The analysis suggests that, even after the completion of Phase I of MWSP, the water scarcity in the valley will be 160 MLD in 2020. Therefore, Phase II of MWSP should be completed within 2025 and Phase III should be completed by 2040. The result of this study aids KUKL for better management of the water system. In addition, this research can help in decision making to construct the second and third phase for MWSP, the construction date of which still has not been decided.

[1]  S. Soyupak,et al.  Case studies on the use of neural networks in eutrophication modeling , 2000 .

[2]  Hubert H. G. Savenije,et al.  FORECAST OF WATER DEMAND IN WEINAN CITY IN CHINA USING WDF-ANN MODEL , 2003 .

[3]  K. Adamowski,et al.  Short‐term municipal water demand forecasting , 2005 .

[4]  M. Babel,et al.  A multivariate econometric approach for domestic water demand modeling: An application to Kathmandu, Nepal , 2007 .

[5]  N. Jayasuriya,et al.  Temperature and rainfall thresholds for base use urban water demand modelling , 2007 .

[6]  Mohammad Bagher Tavakoli,et al.  Modified Levenberg-Marquardt Method for Neural Networks Training , 2007 .

[7]  Lazaros S. Iliadis,et al.  An Artificial Neural Network model for mountainous water-resources management: The case of Cyprus mountainous watersheds , 2007, Environ. Model. Softw..

[8]  N. Bhaskar,et al.  SHORT-TERM WATER DEMAND FORECASTING: A CASE STUDY , 2008 .

[9]  J. Adamowski.  Peak Daily Water Demand Forecast Modeling Using Artificial Neural Networks , 2008 .

[10]  Mahmut Firat,et al.  Comparative analysis of neural network techniques for predicting water consumption time series , 2010 .

[11]  Joaquín Izquierdo,et al.  Predictive models for forecasting hourly urban water demand , 2010 .

[12]  V. R. Shinde,et al.  Identifying Prominent Explanatory Variables for Water Demand Prediction Using Artificial Neural Networks: A Case Study of Bangkok , 2011 .

[13]  Pham Cong-Kha,et al.  Parameter extraction and optimization using Levenberg-Marquardt algorithm , 2012, 2012 Fourth International Conference on Communications and Electronics (ICCE).

[14]  Samuel Lukas,et al.  Backpropagation and Levenberg-Marquardt Algorithm for Training Finite Element Neural Network , 2012, 2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation.

[15]  Maamar Sebri.  ANN versus SARIMA models in forecasting residential water consumption in Tunisia , 2013 .

[16]  M. Moglia,et al.  Towards sustainable urban water management: a critical reassessment. , 2013, Water research.

[17]  Massoud Tabesh,et al.  A long-term prediction of domestic water demand using preprocessing in artificial neural network , 2014 .

[18]  Wolfgang Rauch,et al.  Exploring critical pathways for urban water management to identify robust strategies under deep uncertainties. , 2014, Water research.

[19]  M. Babel,et al.  Assessment of climate change impact on water diversion strategies of Melamchi Water Supply Project in Nepal , 2017, Theoretical and Applied Climatology.

[20]  Zoran Kapelan,et al.  Forecasting Domestic Water Consumption from Smart Meter Readings Using Statistical Methods and Artificial Neural Networks , 2015 .

[21]  T. Asefa,et al.  Improving Short-Term Urban Water Demand Forecasts with Reforecast Analog Ensembles , 2016 .

[22]  M. Ghorbani,et al.  A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction , 2016, Environmental Earth Sciences.

[23]  Bhatawdekar Ramesh Murlidhar,et al.  Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique , 2016, Environmental Earth Sciences.

[24]  M. Babel,et al.  Modelling the potential impacts of climate change on hydrology and water resources in the Indrawati River Basin, Nepal , 2016, Environmental Earth Sciences.

[25]  Jan Adamowski,et al.  Using extreme learning machines for short-term urban water demand forecasting , 2017 .

[26]  Suwash Chandra Acharya,et al.  Bias correction of climate models for hydrological modelling – are simple methods still useful? , 2017 .

[27]  Manuel Herrera,et al.  Correlation analysis of water demand and predictive variables for short-term forecasting models , 2017 .

[28]  Shuming Liu,et al.  Short-Term Water Demand Forecast Based on Deep Learning Method , 2018, Journal of Water Resources Planning and Management.

[29]  Mawada Abdellatif,et al.  A Novel approach for predicting monthly water demand by combining singular spectrum analysis with neural networks , 2018, Journal of Hydrology.

[30]  T. Tadesse,et al.  Urban drought challenge to 2030 sustainable development goals. , 2019, The Science of the total environment.

[31]  T. Tingsanchali,et al.  Evaluation of Soil and Water Assessment Tool and Artificial Neural Network models for hydrologic simulation in different climatic regions of Asia. , 2019, The Science of the total environment.

[32]  Juan Antonio Rodríguez Díaz,et al.  Optimisation of water demand forecasting by artificial intelligence with short data sets , 2019, Biosystems Engineering.

[33]  Stefano Alvisi,et al.  A Comparison of Short-Term Water Demand Forecasting Models , 2017, Water Resources Management.

[34]  Maytham S. Ahmed,et al.  A Method for Predicting Long-Term Municipal Water Demands Under Climate Change , 2020, Water Resources Management.

[35]  Rupp Carriveau,et al.  Short-Term Water Demand Forecasting Using Nonlinear Autoregressive Artificial Neural Networks , 2020 .