2018
DOI: 10.1007/s10706-018-0713-6
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Data-Driven Modeling of Groundwater Level with Least-Square Support Vector Machine and Spatial–Temporal Analysis

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Cited by 49 publications
(7 citation statements)
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“…The aforementioned soft-computing techniques have been widely used to predict hydrological parameters due to mul-tiple factors, such as low computational complexity, high precision, fast training, fast performance time, to name a few [33]. For instance, in [34], the authors developed a hybrid prediction model based on ANN and wavelet theorem to predict GWL in Canada.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The aforementioned soft-computing techniques have been widely used to predict hydrological parameters due to mul-tiple factors, such as low computational complexity, high precision, fast training, fast performance time, to name a few [33]. For instance, in [34], the authors developed a hybrid prediction model based on ANN and wavelet theorem to predict GWL in Canada.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Several approaches have recently been utilized in the research domain of groundwater level predictions. These include machine learning-based prediction modelling [21,22,24], ANNs [25][26][27], hybridized wavelet transform-machine learning methods [16,[28][29][30], hybridized ensemble empirical mode decomposition and machine learning-based models [31], nonlinear autoregressive with exogenous inputs (NARX) neural networks [21], ARIMA-particle swarm optimization [32], ANN-whale algorithm [33], integrated linear polynomial and nonlinear system identification models [34], ANFIS [30,[35][36][37][38], wavelet-ANFIS [39], Support Vector Machine (SVM) [35,40], hybrid SVM-PSO [41], Gaussian Process Regression [30], Genetic Programming [42], Facebook's prophet approach of groundwater level forecasting [43], physics-inspired coupled space-time artificial neural networks [44]. A detailed review of artificial intelligence-based approaches to groundwater level modelling is given in [45].…”
Section: Introductionmentioning
confidence: 99%
“…However, since GWL data are typical geographic spatiotemporal series data, LSTM prediction models consider the temporal autocorrelation of GWL data as well as the spatial correlations among different observation wells in the prediction process [27]. Existing studies have considered only the spatial distance factor and have not involved analyses of spatial correlations among sites under the influence of complex factors [27][28][29]. GWL dynamic characteristics are closely related to external factors, such as rainfall, and internal hydrological characteristics, especially in karst aquifers.…”
Section: Introductionmentioning
confidence: 99%