@article{scholars17381,
            note = {cited By 0; Conference of 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 ; Conference Date: 17 December 2020 Through 18 December 2020; Conference Code:286319},
          volume = {758},
             doi = {10.1007/978-981-16-2183-3{$_3$}{$_3$}},
           title = {Application of Artificial Intelligence for Reservoir Storage Prediction: A Case Study},
            year = {2022},
       publisher = {Springer Science and Business Media Deutschland GmbH},
         journal = {Lecture Notes in Electrical Engineering},
           pages = {343--354},
        keywords = {Digital storage; Forecasting; Mean square error; Neural networks, Artificial intelligence methods; Artificial intelligence techniques; Case-studies; Forecast; Hydropower; Machine-learning; Regression algorithms; Reservoir inflow; Reservoir storage; Storage level, Machine learning},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142740501&doi=10.1007\%2f978-981-16-2183-3\%5f33&partnerID=40&md5=362749903ddcf973f5add4ae847ea59a},
        abstract = {There are many relevant and interesting contributions using Artificial Intelligence (AI) based techniques, with different purposes. It has been used as an effective way for estimating the forecasted data of reservoir daily storage value. The efficiency of various AI methods was explored in this article and later the best method is selected for reservoir storage level prediction. In estimating reservoir storage levels several regression algorithms and artificial neural network (ANN) approaches have been evaluated. There is better agreement between the ANN model compared to regression algorithms. The findings were demonstrated by significant correlation coefficient (R2) rate among the expected and calculated training outcome variables up to 0.91 and the highest validity outcome of Root Mean Square Error (RMSE) was 5.1989. Consequently, this method is therefore adequate for robustness and generalizability abilities and is ideal for forecasts. {\^A}{\copyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
            issn = {18761100},
          author = {Azad, A. S. and Vasant, P. M. and G{\~A}!mez Vintaned, J. A. and Watada, J.},
            isbn = {9789811621826}
}