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Prediction and Comparison of Rainfall-Runoff Using Mathematical Model

2023; IOP Publishing; Volume: 1130; Issue: 1 Linguagem: Inglês

10.1088/1755-1315/1130/1/012044

1755-1307

S V S N D L Prasanna, K Sandeep Reddy, Chandrasekhar, S Sai Shivani, E Divya,

Hydrology and Watershed Management Studies

Abstract The Runoff assessment is a crucial parameter in understanding the urban flooding scenario. This estimation becomes the deciding factor because of the uneven distribution of rainfall. Physics-based models for simulation of Runoff from catchments are composite models based on learning algorithms. The application of models to water resource problems is complex due to the incredible spatial variability of the characteristics of watershed and precipitation forms — the pattern-learning algorithms. Fuzzy-based algorithms, Artificial Neural Networks (ANNs), etc., have gained wide recognition in simulating the Rainfall-Runoff (RR), producing a comparable accuracy. In the present study, RR modeling is carried out targeting the application and estimation of Runoff using mathematical modeling. The investigations were carried out for the Malkajgiri catchment adopting 16 years of daily data from 2005 to 2021. The statistical learning theory-based pattern-learning algorithm is further utilized to evaluate the value of Runoff for the year 2021. The results were found to have fair accordance with the analytical outcomes.