Prediction of groundwater level variations in coastal aquifers with tide and rainfall effects using heuristic data driven models

Shiri, Jalal; Kişi, Özgür; Yoon, Heesung; Kazemi, Mohammad Hossein; Shiri, Naser; Poorrajabali, Mohammad; Karimi, Shahram

doi:10.1080/09715010.2020.1729876

Cited by 12 publications

(3 citation statements)

References 30 publications

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“…This study's outcomes follow the studies carried out in other parts of the world to predict the groundwater level with slightly different input parameters [44,62,65,68,69,82,83], and found the performance of GAs implemented with ANN promising for the prediction of groundwater table depth in various regions. Shiri et al [84] predicted groundwater depth (GWD) fluctuations of two coastal aquifers located in Donghae City, Korea, by employing six heuristic models: boosted regression tree (BRT), random forests (RF), multivariate adaptive regression spline (MARS), ANN, support vector machine (SVM), and gene expression programming (GEP). They found the GEP model with tide and rainfall data provided better estimates than the other models.…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India

et al. 2020

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Accurate information about groundwater level prediction is crucial for effective planning and management of groundwater resources. In the present study, the Artificial Neural Network (ANN), optimized with a Genetic Algorithm (GA-ANN), was employed for seasonal groundwater table depth (GWTD) prediction in the area between the Ganga and Hindon rivers located in Uttar Pradesh State, India. A total of 18 models for both seasons (nine for the pre-monsoon and nine for the post-monsoon) have been formulated by using groundwater recharge (GWR), groundwater discharge (GWD), and previous groundwater level data from a 21-year period (1994–2014). The hybrid GA-ANN models’ predictive ability was evaluated against the traditional GA models based on statistical indicators and visual inspection. The results appraisal indicates that the hybrid GA-ANN models outperformed the GA models for predicting the seasonal GWTD in the study region. Overall, the hybrid GA-ANN-8 model with an 8-9-1 structure (i.e., 8: inputs, 9: neurons in the hidden layer, and 1: output) was nominated optimal for predicting the GWTD during pre- and post-monsoon seasons. Additionally, it was noted that the maximum number of input variables in the hybrid GA-ANN approach improved the prediction accuracy. In conclusion, the proposed hybrid GA-ANN model’s findings could be readily transferable or implemented in other parts of the world, specifically those with similar geology and hydrogeology conditions for sustainable planning and groundwater resources management.

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Section: Discussionmentioning

confidence: 99%

Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India

et al. 2020

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“…Also, the authors gure out that groundwater extraction for agricultural usage is the main driving force for aquifer storage changes. In another study, Shiri et al (2020) used six AI-based models, ANN, BT, MARS, RF, GEP, and SVM, in a coastal aquifer to forecast GWL, and they gured out that GEP's outcomes were the superior one. Osman et al's (2021) study showed that the Xgboost model had the best results among other used AI-based models such as ANN and support vector regression to predict GWL.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components

Samani

Vadiati

Azizi³

et al. 2022

Water Resour Manage

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Precise and accurate estimates of groundwater level might be of great importance for attaining sustainable development goals and integrated water resources management. Compared with alternative numerical models, soft computing methods are promising tools for groundwater level simulation and prediction, which need more hydrogeological and aquifer characteristics. The central aim of this research is to explore the performance of such well-accepted data-driven models to simulate the groundwater level (GWL t ) with emphasis on major meteorological components, including; precipitation (P), temperature (T), evapotranspiration (ET) dataset on a monthly interval. Arti cial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH), and least-square support vector machine (LSSVM) are used to predict one-, two-, and three-month ahead groundwater level in an uncon ned aquifer. The main meteorological components (T t , ET t , P t , P t−1 ) and GWL for one, two, and three lag-time (GWL t−1 , GWL t−2 , GWL t−3 ) were used as input for different scenarios to attain precise and accurate prediction. The results showed that all models could have the best simulation for one month ahead in scenario 5, comprising

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“…Despite their success in GWL modelling efficiently with less data, these techniques suffer from some shortcomings. For instance, the ANN also exhibits high sensitivity to the trained data, overfitting problem and dependence on hidden neurons and poor forecasting (Shiri et al 2020). The performance of SVM depends on the optimal selection of kernel functions (Sheikh Khozani et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Groundwater Level Estimation in Northwest Region of Bangladesh Using Hybrid Locally Weighted Linear Regression and Gaussian Process Regression Modeling

Elbeltagi

Salam

Pal

et al. 2022

Preprint

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The knowledge about the role of the underlying variables on groundwater level (GWL) fluctuation at local scale in the drought-prone urban areas of Bangladesh is still not explored. To better insight into the relative contribution of underlying factors on GWL fluctuation, this study proposed a novel hybrid ensemble modeling framework based on locally weighted linear regression (LWLR) and four Gaussian Process Regressions (GPRs) e.g., poly kernel, Pearson universal kernel (PUK), radian basis function (RBF) and normalized poly kernel. The proposed framework has employed to predict GWL at six wells in the drought-prone local areas of North-western urban region of Bangladesh, where GWL is declining rapidly. The rainfall, temperature (Tave), soil moisture (SM), normalized difference vegetation index (NDVI), Indian Ocean Dipole (IOD), Southern Oscillation Index (SOI), Nina3.4, and population growth rate for the period 1993-2017 were utilized as inputs to developed GWL models. The best input combination was explored using the best subset regression model and sensitivity analysis, and the optimal input combination was applied in LWLR and GPRs to estimate monthly GWL fluctuation. The hybrid LWLR-GPR-PUK model, on average, improves the prediction accuracy from 10 to 50% during the training stage and 20-70% during the testing stage compared to other models. The proposed modeling approach can act as a promising substitute tool to estimate GWL fluctuation, especially in drought-prone local areas in urban regions where groundwater data scarcity hinders the physical law-based model development.

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Prediction of groundwater level variations in coastal aquifers with tide and rainfall effects using heuristic data driven models

Cited by 12 publications

References 30 publications

Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India

Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India

Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components

Groundwater Level Estimation in Northwest Region of Bangladesh Using Hybrid Locally Weighted Linear Regression and Gaussian Process Regression Modeling

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