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Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy

Campisi-Pinto, Salvatore; Adamowski, Jan; Oron, Gideon - In: Water Resources Management 26 (2012) 12, pp. 3539-3558

Forecasting urban water demand can be of use in the management of water utilities. For example, activities such as … resources at specified future dates. In this study, we tackle the problem of forecasting urban water demand by means of back … 7 year-long time series of water demand without additional predictors. Six forecasting horizons were considered, from 1 …

Persistent link: https://www.econbiz.de/10010997289

Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series

Kisi, Ozgur; Latifoğlu, Levent; Latifoğlu, Fatma - In: Water Resources Management 28 (2014) 12, pp. 4045-4057

In this study, a nonparametric technique to set up a river stage forecasting model based on empirical mode …

Persistent link: https://www.econbiz.de/10010998087

Discretized and Continuous Target Fields for the Reservoir Release Rules During Floods

Wei, Chih-Chiang - In: Water Resources Management 26 (2012) 12, pp. 3457-3477

This paper employed two classical, popular decision-tree algorithms (C5.0 and CART), and traditional Regression to deal with reservoir operations regarding decision of the releases from a reservoir system during floods. The experiment site was in Shihmen Reservoir, located in northern Taiwan. In...

Persistent link: https://www.econbiz.de/10010997288

Short-Term Water Demand Forecast Modelling at IIT Kanpur Using Artificial Neural Networks

Jain, Ashu; Varshney, Ashish Kumar; Joshi, Umesh Chandra - In: Water Resources Management 15 (2001) 5, pp. 299-321

forecasting in recent years. The primary objective of this study is to investigate the relatively new technique of artificial … neural networks for use in forecasting short-term water demand at the Indian Institute of Technology, Kanpur. Other … and time series models developed in this study. An average absolute error in forecasting of 2.41% was achieved from the …

Persistent link: https://www.econbiz.de/10010794049

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Karthikeyan, L.; Kumar, D.; Graillot, Didier; Gaur, Shishir - In: Water Resources Management 27 (2013) 3, pp. 871-883

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study...

Persistent link: https://www.econbiz.de/10010794059

Application of Neural Networks and Optimization Model in Conjunctive Use of Surface Water and Groundwater

Chen, Ching-Wen; Wei, Chih-Chiang; Liu, Hung-Jen; Hsu, … - In: Water Resources Management 28 (2014) 10, pp. 2813-2832

This study develops an optimization model for the large-scale conjunctive use of surface water and groundwater resources. The aim is to maximize public and irrigation water supplies subject to groundwater-level drawdown constraints. Linear programming is used to create the optimization model,...

Persistent link: https://www.econbiz.de/10010794086

A Genetic Programming Approach to Rainfall-Runoff Modelling

Savic, Dragan; Walters, Godfrey; Davidson, James - In: Water Resources Management 13 (1999) 3, pp. 219-231

Planning for sustainable development of water resources relies crucially on the data available. Continuous hydrologic simulation based on conceptual models has proved to be the appropriate tool for studying rainfall-runoff processes and for providing necessary data. In recent years, artificial...

Persistent link: https://www.econbiz.de/10010794120

Optimal Management of Coastal Aquifers Using Linked Simulation Optimization Approach

Bhattacharjya, Rajib; Datta, Bithin - In: Water Resources Management 19 (2005) 3, pp. 295-320

Saltwater intrusion management models can be used to derive optimal and efficient management strategies for controlling saltwater intrusion in coastal aquifers. To obtain physically meaningful optimal management strategies, the physical processes involved need to be simulated while deriving the...

Persistent link: https://www.econbiz.de/10010794175

Identifying Prominent Explanatory Variables for Water Demand Prediction Using Artificial Neural Networks: A Case Study of Bangkok

Babel, Mukand; Shinde, Victor - In: Water Resources Management 25 (2011) 6, pp. 1653-1676

The water demand of a city is a complex and non linear function of climatic, socioeconomic, institutional and management variables. Identifying the prominent variables among these is essential in order to adequately predict water demand, and to plan and manage water resources and the supply...

Persistent link: https://www.econbiz.de/10010794337

Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network

Sulaiman, Muhammad; El-Shafie, Ahmed; Karim, Othman; … - In: Water Resources Management 25 (2011) 10, pp. 2525-2541

Developing water level forecasting models is essential in water resources management and flood prediction. Accurate … water level forecasting helps achieve efficient and optimum use of water resources and minimize flooding damages. The … artificial neural network (ANN) is a computing model that has been successfully tested in many forecasting studies, including …

Persistent link: https://www.econbiz.de/10010794544