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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

Intermittent Streamflow Forecasting and Extreme Event Modelling using Wavelet based Artificial Neural Networks

Makwana, Jaydip; Tiwari, Mukesh - In: Water Resources Management 28 (2014) 13, pp. 4857-4873

<Para ID="Par1">Forecasting of intermittent stream flow is necessary for water resource planning and management at … catchment scale. Forecasting of extreme events and events outside the range of training data used for artificial neural network … proposed for intermittent streamflow forecasting and extreme event modelling. This study is carried out in a watershed in semi …

Persistent link: https://www.econbiz.de/10010997424

Performance Evaluation of Reference Evapotranspiration Estimation Using Climate Based Methods and Artificial Neural Networks

Chauhan, Seema; Shrivastava, R. - In: Water Resources Management 23 (2009) 5, pp. 825-837

This study is an attempt to find best alternative method to estimate reference evapotranspiration (ET<Subscript>o</Subscript>) for the Mahanadi reservoir project (MRP) command area located at Raipur (Chhattisgarh) in India, when input climatic parameters are insufficient to apply standard Food and Agriculture...</subscript>

Persistent link: https://www.econbiz.de/10010997489

Comparison of Artificial Neural Network Methods with L-moments for Estimating Flood Flow at Ungauged Sites: the Case of East Mediterranean River Basin, Turkey

Seckin, Neslihan; Cobaner, Murat; Yurtal, Recep; … - In: Water Resources Management 27 (2013) 7, pp. 2103-2124

A regional flood frequency analysis based on the index flood method is applied using probability distributions commonly utilized for this purpose. The distribution parameters are calculated by the method of L-moments with the data of the annual flood peaks series recorded at gauging sections of...

Persistent link: https://www.econbiz.de/10010997542

Precipitation Forecast Using Artificial Neural Networks in Specific Regions of Greece

Moustris, Kostas; Larissi, Ioanna; Nastos, Panagiotis; … - In: Water Resources Management 25 (2011) 8, pp. 1979-1993

In recent years, significant changes in precipitation regimes have been observed and these manifest in socio economic and ecological problems especially in regions with increased vulnerability such as the Mediterranean region. For this reason, it is necessary to estimate the future projected...

Persistent link: https://www.econbiz.de/10010997630

Estimation of the Change in Lake Water Level by Artificial Intelligence Methods

Buyukyildiz, Meral; Tezel, Gulay; Yilmaz, Volkan - In: Water Resources Management 28 (2014) 13, pp. 4747-4763

<Para ID="Par1">In this study, five different artificial intelligence methods, including Artificial Neural Networks based on Particle Swarm Optimization (PSO-ANN), Support Vector Regression (SVR), Multi- Layer Artificial Neural Networks (MLP), Radial Basis Neural Networks (RBNN) and Adaptive Network Based Fuzzy...</para>

Persistent link: https://www.econbiz.de/10010997841

Artificial Neural Network (ANN) Based Modeling for Karstic Groundwater Level Simulation

Trichakis, Ioannis; Nikolos, Ioannis; Karatzas, G. - In: Water Resources Management 25 (2011) 4, pp. 1143-1152

A relatively new method of addressing different hydrological problems is the use of artificial neural networks (ANN). In groundwater management ANNs are usually used to predict the hydraulic head at a well location. ANNs can prove to be very useful because, unlike numerical groundwater models,...

Persistent link: https://www.econbiz.de/10010997859

Heuristic Modelling of the Water Resources Management in the Guadalquivir River Basin, Southern Spain

Pulido-Calvo, Inmaculada; Gutiérrez-Estrada, Juan; … - In: Water Resources Management 26 (2012) 1, pp. 185-209

A model comprising blocks of artificial neural networks (ANNs) combined in sequence was used to simulate the inflow and outflow in a water resources system under a shortage of water. We assessed the selection of appropriate input data using linear and non-linear cross-correlation functions and...

Persistent link: https://www.econbiz.de/10010997874