Effectiveness of heuristic approach for daily sediment flow prediction of Koyna river basin
| Autor: | Tarate Suryakant Bajirao, Pravendra Kumar |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Journal of Soil and Water Conservation. 20:12-21 |
| ISSN: | 2455-7145 0022-457X |
| DOI: | 10.5958/2455-7145.2021.00004.7 |
| Popis: | The aim of this study was to test the applicability of heuristic approach for daily suspended sediment concentration (SSC) prediction of Koyna river basin situated in western Maharashtra region of India. Daily rainfall, runoff and SSC data were used for development of different models. Heuristic based models like artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), statistical model like multiple linear regressions (MLR) and traditional sediment rating curve (SRC) models were developed for SSC prediction. In order to avoid time consuming and tedious trial and error method by varying inputs, Gamma test was applied to select best inputs for development of different SSC prediction models. 39 ANN models were developed using tansig, logsig and purelin transfer function. 48 ANFIS models were developed using subtractive clustering method of FIS generation. The best model was selected on the basis performance criteria such as higher correlation coefficient (r) and coefficient of determination (R2). It was found that ANN (6-7-1) model with purelin transfer function outperformed all other models with r and R2 values of 0.79 and 0.62, respectively for daily SSC prediction. The traditional SRC model performed worst and could not able to catch the overall shape of observed SSC time series. The coefficient of determination (R2) was increased from 0.42 to 0.62 using heuristic approach than traditional SRC method. It was found that the heuristic approach could be successfully applied for cumulative SSC load estimation which is essential in reservoir operations and managements. It proves the applicability of heuristic approach in hydrologic modelling for better watershed planning and management. |
| Databáze: | OpenAIRE |
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