The Experts below are selected from a list of 56919 Experts worldwide ranked by ideXlab platform
Bijaya Ketan Panigrahi - One of the best experts on this subject based on the ideXlab platform.
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an integrated wavelet support vector machine for Groundwater Level prediction in visakhapatnam india
Neurocomputing, 2014Co-Authors: Ch Suryanarayana, Ch. Sudheer, Vazeer Mahammood, Bijaya Ketan PanigrahiAbstract:Accurate and reliable prediction of the Groundwater Level variation is significant and essential in water resources management of a basin. The situation is complicated by the fact that the variation of Groundwater Level is highly nonlinear in nature because of interdependencies and uncertainties in the hydro-geological process. Models such as Artificial Neural Networks (ANN) and Support Vector Machine (SVM) have proved to be effective in modeling virtually any nonlinear function with a greater degree of accuracy. In recent times, combining several techniques to form a hybrid tool to improve the accuracy of prediction has become a common practice for various applications. This integrated method increases the efficiency of the model by combining the unique features of the constituent models to capture different patterns in the data. In the present study, an attempt is made to predict monthly Groundwater Level fluctuations using integrated wavelet and support vector machine modeling. The discrete wavelet transform with two coefficients (db2 wavelet) is adopted for decomposing the input data into wavelet series. These series are further used as input variables in different combinations for Support Vector Regression (SVR) model to forecast Groundwater Level fluctuations. The monthly data of precipitation, maximum temperature, mean temperature and Groundwater depth for the period 2001–2012 are used as the input variables. The proposed Wavelet-Support Vector Regression (WA-SVR) model is applied to predict the Groundwater Level variations for three observation wells in the city of Visakhapatnam, India. The performance of the WA-SVR model is compared with SVR, ANN and also with the traditional Auto Regressive Integrated Moving Average (ARIMA) models. Results indicate that WA-SVR model gives better accuracy in predicting Groundwater Levels in the study area when compared to other models.
Jiurong Liu - One of the best experts on this subject based on the ideXlab platform.
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Upgrading a regional Groundwater Level monitoring network for Beijing Plain, China
Geoscience Frontiers, 2013Co-Authors: Yangxiao Zhou, Dianwei Dong, Jiurong LiuAbstract:Monitoring of regional Groundwater Levels provides important information for quantifying Groundwater depletion and assessing impacts on the environment. Historically, Groundwater Level monitoring wells in Beijing Plain, China, were installed for assessing Groundwater resources and for monitoring the cone of depression. Monitoring wells are clustered around well fields and urban areas. There is urgent need to upgrade the existing monitoring wells to a regional Groundwater Level monitoring network to acquire information for integrated water resources management. A new method was proposed for designing a regional Groundwater Level monitoring network. The method is based on Groundwater regime zone mapping. Groundwater regime zone map delineates distinct areas of possible different Groundwater Level variations and is useful for locating Groundwater monitoring wells. This method was applied to Beijing Plain to upgrade a regional Groundwater Level monitoring network.
Ch Suryanarayana - One of the best experts on this subject based on the ideXlab platform.
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an integrated wavelet support vector machine for Groundwater Level prediction in visakhapatnam india
Neurocomputing, 2014Co-Authors: Ch Suryanarayana, Ch. Sudheer, Vazeer Mahammood, Bijaya Ketan PanigrahiAbstract:Accurate and reliable prediction of the Groundwater Level variation is significant and essential in water resources management of a basin. The situation is complicated by the fact that the variation of Groundwater Level is highly nonlinear in nature because of interdependencies and uncertainties in the hydro-geological process. Models such as Artificial Neural Networks (ANN) and Support Vector Machine (SVM) have proved to be effective in modeling virtually any nonlinear function with a greater degree of accuracy. In recent times, combining several techniques to form a hybrid tool to improve the accuracy of prediction has become a common practice for various applications. This integrated method increases the efficiency of the model by combining the unique features of the constituent models to capture different patterns in the data. In the present study, an attempt is made to predict monthly Groundwater Level fluctuations using integrated wavelet and support vector machine modeling. The discrete wavelet transform with two coefficients (db2 wavelet) is adopted for decomposing the input data into wavelet series. These series are further used as input variables in different combinations for Support Vector Regression (SVR) model to forecast Groundwater Level fluctuations. The monthly data of precipitation, maximum temperature, mean temperature and Groundwater depth for the period 2001–2012 are used as the input variables. The proposed Wavelet-Support Vector Regression (WA-SVR) model is applied to predict the Groundwater Level variations for three observation wells in the city of Visakhapatnam, India. The performance of the WA-SVR model is compared with SVR, ANN and also with the traditional Auto Regressive Integrated Moving Average (ARIMA) models. Results indicate that WA-SVR model gives better accuracy in predicting Groundwater Levels in the study area when compared to other models.
Yangxiao Zhou - One of the best experts on this subject based on the ideXlab platform.
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Upgrading a regional Groundwater Level monitoring network for Beijing Plain, China
Geoscience Frontiers, 2013Co-Authors: Yangxiao Zhou, Dianwei Dong, Jiurong LiuAbstract:Monitoring of regional Groundwater Levels provides important information for quantifying Groundwater depletion and assessing impacts on the environment. Historically, Groundwater Level monitoring wells in Beijing Plain, China, were installed for assessing Groundwater resources and for monitoring the cone of depression. Monitoring wells are clustered around well fields and urban areas. There is urgent need to upgrade the existing monitoring wells to a regional Groundwater Level monitoring network to acquire information for integrated water resources management. A new method was proposed for designing a regional Groundwater Level monitoring network. The method is based on Groundwater regime zone mapping. Groundwater regime zone map delineates distinct areas of possible different Groundwater Level variations and is useful for locating Groundwater monitoring wells. This method was applied to Beijing Plain to upgrade a regional Groundwater Level monitoring network.
Vazeer Mahammood - One of the best experts on this subject based on the ideXlab platform.
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an integrated wavelet support vector machine for Groundwater Level prediction in visakhapatnam india
Neurocomputing, 2014Co-Authors: Ch Suryanarayana, Ch. Sudheer, Vazeer Mahammood, Bijaya Ketan PanigrahiAbstract:Accurate and reliable prediction of the Groundwater Level variation is significant and essential in water resources management of a basin. The situation is complicated by the fact that the variation of Groundwater Level is highly nonlinear in nature because of interdependencies and uncertainties in the hydro-geological process. Models such as Artificial Neural Networks (ANN) and Support Vector Machine (SVM) have proved to be effective in modeling virtually any nonlinear function with a greater degree of accuracy. In recent times, combining several techniques to form a hybrid tool to improve the accuracy of prediction has become a common practice for various applications. This integrated method increases the efficiency of the model by combining the unique features of the constituent models to capture different patterns in the data. In the present study, an attempt is made to predict monthly Groundwater Level fluctuations using integrated wavelet and support vector machine modeling. The discrete wavelet transform with two coefficients (db2 wavelet) is adopted for decomposing the input data into wavelet series. These series are further used as input variables in different combinations for Support Vector Regression (SVR) model to forecast Groundwater Level fluctuations. The monthly data of precipitation, maximum temperature, mean temperature and Groundwater depth for the period 2001–2012 are used as the input variables. The proposed Wavelet-Support Vector Regression (WA-SVR) model is applied to predict the Groundwater Level variations for three observation wells in the city of Visakhapatnam, India. The performance of the WA-SVR model is compared with SVR, ANN and also with the traditional Auto Regressive Integrated Moving Average (ARIMA) models. Results indicate that WA-SVR model gives better accuracy in predicting Groundwater Levels in the study area when compared to other models.
