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- Title
Prediction of groundwater-level using novel SVM-ALO, SVM-FOA, and SVM-FFA algorithms at Purba-Medinipur, India.
- Authors
Samantaray, Sandeep; Sahoo, Abinash; Satapathy, Deba Prakash
- Abstract
Accurate and reliable prediction of groundwater level (GWL) fluctuation is vital and significant in water resources planning and management. Because of intricacies in underground geological arrangement, efficiency of real-time GWL prediction is inadequate. In the present study, a hybrid support vector machine (SVM) incorporated with ant lion optimizer (SVM-ALO) was employed to estimate monthly GWL using data collected from two observational wells in Purba-Medinipur, India, considering ten input combinations. The accuracy of hybrid SVM-ALO model is assessed against hybrid SVM-FOA (fruit fly optimization algorithm), SVM-FFA (firefly algorithm), and conventional SVM models using five statistical performance indices, i.e. root mean-squared error (RMSE), Willmott Index (WI), Pearson's correlation coefficient (PCC), coefficient of determination (R2), and graphical analysis. Analysis of results indicated that SVM-ALO model showed superior performance than SVM-FOA and SVM-FFA models for all scenarios at both stations. The SVM-ALO-M10 model had the lowest value of RMSE = 7.6638/8.838 and the highest value of PCC = 0.9815/0.98001, and WI = 0.98215/0.98067 for testing period at Sherkhanchawk/Basantia high school, respectively. The developed robust SVM-ALO model is more efficient and appropriate compared to SVM-FOA, SVM-FFA, and standalone SVM models for estimating monthly ground water levels in the study region.
- Publication
Arabian Journal of Geosciences, 2022, Vol 15, Issue 8, p1
- ISSN
1866-7511
- Publication type
Article
- DOI
10.1007/s12517-022-09900-y