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Multimodel combination techniques for analysis of hydrological simulations: Application to distributed model intercomparison project results
This paper examines several multimodel combination techniques that are used for streamflow forecasting: the simple model average (SMA), the multimodel superensemble (MMSE), modified multimodel superensemble (M3SE), and the weighted average method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multimodel combination results were obtained using uncalibrated DMIP model simulations and were compared against the best-uncalibrated as well as the best-calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the accuracy levels of the multimodel simulations. This study revealed that the multimodel simulations obtained from uncalibrated single-model simulations are generally better than any single-member model simulations, even the best-calibrated single-model simulations. Furthermore, more sophisticated multimodel combination techniques that incorporated bias correction step work better than simple multimodel average simulations or multimodel simulations without bias correction. © 2006 American Meteorological Society
Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management
The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management
Flood Forecasting Using Machine Learning Methods
This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate
Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques
Author name used in this publication: K. W. Chau2009-2010 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
Integration of Data-Driven Modeling and Stochastic Modeling for Multi-purpose Reservoir Simulation
Water Resources Planning and Managemen
Development of streamflow forecasting model using artificial neural network in the Awash River Basin, Ethiopia
Published ArticleEarly indication of possible drought can help in developing suitable drought mitigation strategies and measures in advance. Therefore, drought forecasting plays an important role in the planning and management of water resource in such circumstances. In this study, a non-linear streamflow forecasting model was developed using Artificial Neural Network (ANN) modeling technique at the Melka Sedi stream gauging station, Ethiopia, with adequate lead times. The available data was divided into two independent sets using a split sampling tool of the neural network software. The first data set was used for training and the second data set, which is normally about one fourth of the total available data, was used for testing the model. A one year data was set aside for validating the ANN model. The streamflow predicted using the model on weekly time step compared favorably with the measured streamflow data (R2 = 75%) during the validation period. Application of the model in assessing appropriate agricultural water management strategies for a large-scale irrigation scheme in the Awash River Basin, Ethiopia, has already been considered for publication in a referred journal
Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin
The conditions which affect the sustainability of water cause a number of serious environmental and hydrological problems. Effective and correct management of water resources constitutes an effective and important issue among scales. In this sense, a precise estimation of streamflow time series in rivers is one of the most important issues in optimal management of surface water resources. Therefore, a hybrid method combining particle swarm algorithm (PSO) and long short-term memory networks (LSTM) are proposed to predict flow with data obtained from different flow measurement stations. In this respect, the data gathered from three Flow Measurement Stations (FMS) from Zamanti and Eglence rivers located on Seyhan Basin are utilized. Besides, the proposed LSTM-PSO method is compared to an adaptive neuro-fuzzy inference system (ANFIS) and the LSTM benchmark model to demonstrate the performance achievement of proposed method. The prediction performances of the developed hybrid model and the others are tested on the determined stations. The forecasting performances of the models are determined with RMSE, MAE, MAPE, SD, and R-2 metrics. The comparison results indicated that the LSTM-PSO method provides highest results with values of R-2 (approximate to 0.9433), R-2 (approximate
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