{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T04:45:23Z","timestamp":1741668323332,"version":"3.38.0"},"reference-count":32,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["KES"],"published-print":{"date-parts":[[2021,7,26]]},"abstract":"Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.<\/jats:p>","DOI":"10.3233\/kes-210066","type":"journal-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T17:18:32Z","timestamp":1627406312000},"page":"227-234","source":"Crossref","is-referenced-by-count":9,"title":["Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques"],"prefix":"10.1177","volume":"25","author":[{"given":"Sandeep","family":"Samantaray","sequence":"first","affiliation":[]},{"given":"Abinash","family":"Sahoo","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"issue":"5","key":"10.3233\/KES-210066_ref1","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1007\/s12665-015-5198-5","article-title":"Application of integrated ARIMA and RBF network for groundwater level forecasting","volume":"75","author":"Yan","year":"2016","journal-title":"Environmental Earth Sciences"},{"issue":"9","key":"10.3233\/KES-210066_ref2","doi-asserted-by":"crossref","first-page":"3265","DOI":"10.1007\/s11269-016-1347-1","article-title":"A novel method to water level prediction using RBF and FFA","volume":"30","author":"Soleymani","year":"2016","journal-title":"Water Resources Management"},{"issue":"1","key":"10.3233\/KES-210066_ref3","first-page":"98","article-title":"Seasonal prediction of groundwater levels using ANFIS and radial basis neural network","volume":"1","author":"Amutha","year":"2011","journal-title":"International Journal of Geology, Earth and Environmental Sciences"},{"key":"10.3233\/KES-210066_ref4","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.oceaneng.2012.08.012","article-title":"Sequential learning radial basis function network for real-time tidal level predictions","volume":"57","author":"Yin","year":"2013","journal-title":"Ocean Engineering"},{"key":"10.3233\/KES-210066_ref5","doi-asserted-by":"crossref","unstructured":"S. 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