Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid Wavelet packet–Support vector regression
TLDR
In this paper, a hybrid Wavelet packet-Support vector regression (WP-SVR) model is proposed for forecasting monthly groundwater level fluctuations observed in three shallow unconfined coastal aquifers.Abstract:
This research demonstrates the state-of-the-art capability of Wavelet packet analysis in improving the forecasting efficiency of Support vector regression (SVR) through the development of a novel hybrid Wavelet packet–Support vector regression (WP–SVR) model for forecasting monthly groundwater level fluctuations observed in three shallow unconfined coastal aquifers. The Sequential Minimal Optimization Algorithm-based SVR model is also employed for comparative study with WP–SVR model. The input variables used for modeling were monthly time series of total rainfall, average temperature, mean tide level, and past groundwater level observations recorded during the period 1996–2006 at three observation wells located near Mangalore, India. The Radial Basis function is employed as a kernel function during SVR modeling. Model parameters are calibrated using the first seven years of data, and the remaining three years data are used for model validation using various input combinations. The performance of b...read more
Citations
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Wavelet coupled MARS and M5 Model Tree approaches for groundwater level forecasting
TL;DR: In this paper, two machine learning models, Multivariate Adaptive Regression Splines (MARS) and M5 Model Trees (MT), have been applied to simulate the groundwater level (GWL) fluctuations of three shallow open wells within diverse unconfined aquifers.
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TL;DR: In this article, support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) were used to assess the removal efficiency of Kjeldahl Nitrogen of a full-scale aerobic biological wastewater treatment plant.
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Groundwater level prediction using machine learning models: A comprehensive review
Haiyang Wang Tao,Mohammed Majeed Hameed,Haydar Abdulameer Marhoon,Mohammad Zounemat-Kermani,Heddam Salim,Kim Sungwon,Sadeq Oleiwi Sulaiman,Mou Leong Tan,Zulfaqar Sa’adi,Ali Danandeh Mehr,Mohammed Falah Allawi,Sani Isah Abba,Jasni Mohamad Zain,Mayadah W. Falah,Mehdi Jamei,Neeraj Dhanraj Bokde,M. Bayatvarkeshi,Mustafa Al-Mukhtar,Suraj Kumar Bhagat,Tiyasha Tiyasha,Khaled Mohamed Khedher,Nadhir Al-Ansari,Shamsuddin Shahid,Zaher Mundher Yaseen +23 more
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Prediction of Groundwater Level in Ardebil Plain Using Support Vector Regression and M5 Tree Model
TL;DR: The results indicated that both SVR and M5 decision tree models performed well for the prediction of groundwater level in the Ardebil plain, however, the results obtained from the M4 decision tree model are more straightforward, more easily applied, and simpler to interpret than those from the SVR.
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Impacts of climate change on groundwater level and irrigation cost in a groundwater dependent irrigated region
TL;DR: In this article, an ensemble of general circulation model (GCMs) were used for the projection of climate, an empirical hydrological model based on support vector machine (SVM) was used to simulate groundwater level from climatic variables, and a multiple-linear regression (MLR) model is used to estimate the irrigation cost due to the changes in groundwater level.
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