Groundwater Level Prediction Using Artificial Neural Network Model

Porte, Pallavi; Isaac, R. K.; Mahilang, Kipoo Kiran Singh; Sonboier, Khilendra; Minj, Pankaj

doi:10.20546/ijcmas.2018.702.358

Cited by 8 publications

(3 citation statements)

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“…Our results could help water resource and agricultural managers make the proper decisions as well as help decrease the chance of desertification and land degradation. Our findings agree with Mohanty et al, (2010), Lohani and Krishnan (2015), Porte et al (2018), Chitsazan et al (2013), Nair andSindhu (2016), Suprayogi et al, (2020) and show the efficiency of ANN in monitoring the level of groundwater. There are only a few differences due to the selection of algorithms.…”

Section: Discussionsupporting

confidence: 92%

“…ANN is an applicable estimation tool for groundwater levels in areas without information or inadequate observation points. (Sethi et al, 2010;Karimi et al, 2012;Rankovic et al, 2014;Khaki et al, 2015;Alizamir et al, 2017;Mohanty et al, 2010;Lohani and Krishnan 2015;Porte et al, 2018;Chitsazan et al, 2013;Nair and Sindhu, 2016) Accordingly, the Partial Least Square Regression (PLSR) method was initially used as a calculation algorithm for special vectors, but was quickly interpreted by statistical criteria. In fact, the general idea of PLSR is to find the hidden variables.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of land degradation by Machine Learning Methods

et al. 2021

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In this study, three models were used to monitor and predict the GWL and the land degradation index via the IMDPA method. In all models, 70% of the data was applied for training, while 30% of data were employed for testing and validation. Monthly rainfall, TWI index, the distance of the river, Geographic location was the inputs and the level of groundwater was the output of each method. we found that ANN has the highest efficiency, which agrees with other findings. We combined the results of ANN with Ordinary Kriging and produced a groundwater condition map. According to the potential desertification map and groundwater level index, the potential of desertification had become severe since 2002 and was at a rate of 60% of land area, which, due to incorrect land management in 2016, increased to almost 98% of the land surface in the study area. Using ANN, we predicted that around 99% of the area was severely degraded for 2017. We also used latitude and longitude as input variables which improved the model. In addition to the target variable, latitude and longitude play important roles in Ordinary Kriging and decreased the total error of two combined models.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Prediction of land degradation by Machine Learning Methods

et al. 2021

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“…Numerical methods are often computationally expensive and need many input variables in predicting GWL (Anderson & Woessner, 1992). Hence, in recent years, a number of studies used machine learning algorithms to predict GWL (Taormina et al, 2012;Porte et al, 2018;Lal & Datta, 2018;Su et al, 2020;Niu & Feng, 2021; other two approaches for 3, 6, and 9 months ahead forecast scenarios.…”

Section: Introductionmentioning

confidence: 99%

Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression

Band

Heggy

Bateni

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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Utilizing new approaches to accurately predict groundwater level (GWL) in arid regions is of vital importance. In this study, support vector regression (SVR), Gaussian process regression (GPR), and their combination with wavelet transformation (named wavelet-support vector regression (W-SVR) and wavelet-Gaussian process regression (W-GPR)) are used to forecast groundwater level in Semnan plain (arid area) for the next month. Three different wavelet transformations, namely Haar, db4, and Symlet, are tested. Four statistical metrics, namely root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R 2 ), and Nah-Sutcliffe efficiency (NS), are used to evaluate performance of different methods. The results reveal that SVR with RMSE of 0.04790 (m), MAPE of 0.00199%, R 2 of 0.99995, and NS of 0.99988 significantly outperforms GPR with RMSE of 0.55439 (m), MAPE of 0.04363%, R2 of 0.99264, and NS of 0.98413. Besides, the hybrid W-GPR-1 model (i.e. GPR with Harr wavelet) remarkably improves the accuracy of GWL prediction compared to GPR. Finally, the hybrid W-SVR-3 model (i.e. SVR with Symlet) provides the best GWL prediction with RMSE, MAPE, R2, and NS of 0.01290 (m), 0.00079%, 0.99999, and 0.99999, respectively. Overall, the findings indicate that hybrid models can accurately predict GWL in arid regions.

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Prediction of Seasonal Monthly Rainfall Using Back Propagation Neural Network

Karthik¹,

Prasad²

2021

Lecture Notes in Civil Engineering

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Groundwater Level Prediction Using Artificial Neural Network Model

Cited by 8 publications

References 0 publications

Prediction of land degradation by Machine Learning Methods

Prediction of land degradation by Machine Learning Methods

Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression

Prediction of Seasonal Monthly Rainfall Using Back Propagation Neural Network

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