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Prediction of Ground Water Level Using SVM-WOA Approach : A Case Study


Affiliations
1 Department of Civil Engineering, OUTR Bhubaneswar, 753 014, Odisha, India
 

Reliable and accurate estimation of Groundwater Level (GWL) fluctuations is essential and vital for sustainable water resources management. Due to uncertainties and interdependencies in hydro-geological processes, GWL prediction is complex by the fact that fluctuation of GWL is extremely nonlinear and non-stationary. Utilising novel methods for accurately predicting GWL is of vital significance in arid regions. In present work, Support Vector Machine (SVM), in combination with Whale Optimisation Algorithm (SVM-WOA), is applied to forecast GWL in Bhubaneswar region (Odisha University of Agricultural Technology). Three quantitative statistical performance assessment indices, coefficient of determination (R2 ), Mean Squared Error (MSE), and Wilmott Index (WI), is used to assess model performances. Based on the assessment with conventional SVM and RBFN models, the performance of hybrid SVM-WOA model is preeminent. SVM-WOA is capable of predicting nonlinear behavior of GWLs. Proposed modelling technique can be applied in different regions for proper management of groundwater resources and provides significant information, at a short time scale, to estimate variability in groundwater at local level.

Keywords

Groundwater Level, OUAT, RBFN, Wilmott Index.
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  • Samantaray S, Sahoo A & Ghose D K, Assessment of groundwater potential using neural network: a case study, In ICICC (Springer, Singapore) 2019, 655–664.

  • Samantaray S, Sahoo A & Ghose D K, Infiltration loss affects toward groundwater fluctuation through CANFIS in arid watershed: a case study, In SCI, Springer, Singapore) 2020, 781–789

  • Samantaray S, Biswakalyani C, Singh D K, Sahoo A & Satapathy D P, Prediction of groundwater fluctuation based on hybrid ANFIS-GWO approach in arid Watershed, India, Soft Comput, 26(11) (2022) 5251–5273.

  • Samantaray S, Sahoo A & Satapathy D P, Prediction of groundwater-level using novel SVM-ALO, SVM-FOA, and SVM-FFA algorithms at Purba-Medinipur, India, Arabian J Geosci, 15(8) (2022) 1–22.

  • Samantaray S, Sumaan P, Surin P, Mohanta N R & Sahoo A, Prophecy of groundwater level using hybrid ANFIS-BBO Approach, In ICDSA (Springer, Singapore) 2022, 273–283.

  • Sridharam S, Sahoo A, Samantaray S & Ghose D K, Estimation of water table depth using wavelet-ANFIS: a case study, In INDIA (Springer, Singapore) 747–754

  • Patel N, Bhoi A K, Paika D K, Sahoo A, Mohanta N R & Samantaray S, Water Table Depth Forecasting Based on Hybrid Wavelet Neural Network Model, In FICTA (Springer, Singapore) 2022, 233-242

  • Samantaray S & Sahoo A, Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques, Int J Knowledge-Based Intell Eng Syst, 25(2) (2021) 227–234.

  • Sun Y & Clement T, A decomposition method for solving coupled multi-species reactive transport problems, Transp Porous Media, 37(3) (1999) 327–346.

  • Sun S, Zhang C & Yu G, A Bayesian network approach to traffic flow forecasting, IEEE Trans Intell Transp Syst, 7(1) (2006) 124–132.

  • Sun S & Zhang C, The selective random subspace predictor for traffic flow forecasting, IEEE Trans Intell Transp Syst, 8(2) (2007) 367–373.

  • Sun S & Xu X, Variational inference for infinite mixtures of Gaussian processes with applications to traffic flow prediction, IEEE Trans Intell Transp Syst, 12(2) (2011) 466–475.

  • Samantaray S, Sahoo A, Paul S & Ghose D K, Prediction of bed-load sediment using newly developed support-vector machine techniques, J Irrig Drain Eng, 148(10) (2022) 04022034.

  • Yoon H, Jun S C, Hyun Y, Bae G O & Lee K K, A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer, J Hydrol, 396(1-2) (2011) 128–138.

  • Moharana L, Sahoo A & Ghose D K, Prediction of rainfall using hybrid SVM-HHO model, in IOP Conf Ser Earth Environ Sci (IOP Publishing) October 2022

  • Sahoo A & Ghose D K, Application of hybrid support vector machine model for streamflow prediction in Barak valley, India, in IOP Conf Ser Earth Environ Sci (IOP Publishing) June 2022

  • Sahoo A & Ghose D K, Application of hybrid MLP-GWO for monthly rainfall forecasting in Cachar, Assam: A case study, in SCI, 1 (2022) 307–317.

  • Yan Q & Ma C, Application of integrated ARIMA and RBF network for groundwater level forecasting, Environ Earth Sci, 75(5) (2016) 1–13.

  • Nie S, Bian J, Wan H, Sun X & Zhang B, Simulation and uncertainty analysis for groundwater levels using radial basis function neural network and support vector machine models, J Water Supply Res Technol AQUA, 66(1) (2017) 15–24.

  • Tang Y, Zang C, Wei Y & Jiang M, Data-driven modeling of groundwater level with least-square support vector machine and spatial–temporal analysis, Geotech Geol Eng, 37(3) (2019) 1661–1670.

  • Ebrahimi H & Rajaee T, Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine, Global Planet Change, 148 (2017) 181–191.

  • Guzman S M, Paz J O, Tagert M L M & Mercer A E, Evaluation of seasonally classified inputs for the prediction of daily groundwater levels: NARX networks vs support vector machines, Environ Model Assess, 24(2) (2019) 223–234.

  • Banadkooki F B, Ehteram M, Ahmed A N, Teo F Y, Fai C M, Afan H A, Sapitang M & El-Shafie A, Enhancement of groundwater-level prediction using an integrated machine learning model optimized by whale algorithm, Nat Resour Res, 29(5) (2020) 3233–3252.

  • Natarajan N & Sudheer C, Groundwater level forecasting using soft computing techniques, Neural Comput Appl, 32(12) (2020) 7691–7708.

  • Tikhamarine Y, Malik A, Pandey K, Sammen S S, Souag-Gamane D, Heddam S & Kisi O, Monthly evapotranspiration estimation using optimal climatic parameters: efficacy of hybrid support vector regression integrated with whale optimization algorithm, Environ Monit Assess, 192(11) (2020) 1–19.

  • Band S S, Heggy E, Bateni S M, Karami H, Rabiee M, Samadianfard S, Chau K W & Mosavi A, Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression, Eng Appl Comput Fluid Mech, 15(1) (2021) 1147–1158.

  • Sahoo A, Nayak M, Raju R S, Samantaray S & Mohanta N R, Application of RBFN and FFA for flood prediction: A case study, in FICTA (Springer, Singapore) 2022, 243–253

  • Samantaray S, Sahoo A & Satapathy D P, Improving accuracy of SVM for monthly sediment load prediction using Harris hawks optimization, Mater Today Proc, 61 2022 604–617.

  • Samantaray S & Sahoo A, Appraisal of runoff through BPNN, RNN, and RBFN in Tentulikhunti Watershed: a case study, in FICTA (Springer, Singapore) 2020, 258–267.

  • Cortes C & Vapnik V, Support vector machine. Machine learning, 20(3) (1995) 273-297.

  • Samantaray S, Sah M K, Chalan M M, Sahoo A & Mohanta N R, Runoff Prediction Using Hybrid SVM-PSO Approach, in ICICC (Springer, Singapore) 2022, 281–290

  • Sahoo A, Samantaray S & Sathpathy D P, Prediction of sediment load through novel SVM-FOA approach: A case study, in ICICC (Springer, Singapore) 2022, 291–301.

  • Singh U K, Kumar B, Gantayet N K, Sahoo A, Samantaray S & Mohanta N R, A Hybrid SVM–ABC Model for Monthly Stream Flow Forecasting, in ICMEET (Springer, Singapore) 2022, 315–324

  • Sahoo A, Samantaray S & Ghose D K, Multilayer perceptron and support vector machine trained with grey wolf optimiser for predicting floods in Barak river, India, J Earth Syst Sci, 131(2) (2022) 1–23.

  • Samantaray S, Das S S, Sahoo A & Satapathy D P, Monthly runoff prediction at Baitarani river basin by support vector machine based on Salp swarm algorithm, Ain Shams Eng J, 13(5) (2022) 101732.

  • Sahoo A & Ghose D K, Flood forecasting using hybrid SVM‑GOA model: A case study, in ICMIB (Springer, Singapore) 2022, 407–416.

  • Mirjalili S & Lewis A, The whale optimization algorithm, Adv Eng Softw, 95 (2016) 51–67.

  • Samantaray S & Sahoo A, Prediction of suspended sediment concentration using hybrid SVM-WOA approaches, Geocarto Int, (2021) 1–27.

  • Hsu C-W, Chang C-C & Lin C-J, A Practical Guide to Support Vector Classification, Technical report, Department of Computer Science, National Taiwan University.


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  • Prediction of Ground Water Level Using SVM-WOA Approach : A Case Study

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Authors

Deba Prakash Satapathy
Department of Civil Engineering, OUTR Bhubaneswar, 753 014, Odisha, India
Sujeet Kumar Sahoo
Department of Civil Engineering, OUTR Bhubaneswar, 753 014, Odisha, India

Abstract


Reliable and accurate estimation of Groundwater Level (GWL) fluctuations is essential and vital for sustainable water resources management. Due to uncertainties and interdependencies in hydro-geological processes, GWL prediction is complex by the fact that fluctuation of GWL is extremely nonlinear and non-stationary. Utilising novel methods for accurately predicting GWL is of vital significance in arid regions. In present work, Support Vector Machine (SVM), in combination with Whale Optimisation Algorithm (SVM-WOA), is applied to forecast GWL in Bhubaneswar region (Odisha University of Agricultural Technology). Three quantitative statistical performance assessment indices, coefficient of determination (R2 ), Mean Squared Error (MSE), and Wilmott Index (WI), is used to assess model performances. Based on the assessment with conventional SVM and RBFN models, the performance of hybrid SVM-WOA model is preeminent. SVM-WOA is capable of predicting nonlinear behavior of GWLs. Proposed modelling technique can be applied in different regions for proper management of groundwater resources and provides significant information, at a short time scale, to estimate variability in groundwater at local level.

Keywords


Groundwater Level, OUAT, RBFN, Wilmott Index.

References