Land and water are two most vital natural resources of the world and hence these resources must be conserved carefully to protect environment to maintain ecological balance. Estimation of runoff and sediment yield is one of the prerequisites for conservation and management of water resources and also for many hydrological applications. The present study has been taken up to predict runoff and sediment yield for a reservoir basin viz. Megadrigedda Reservoir situated in Visakhapatnam District, Andhra Pradesh, India which is having a drainage area of 363 Sq.km. Modified Universal Soil Loss Equation (MUSLE) has been used to estimate the sediment yield. The runoff factors of MUSLE were computed by the measured values of runoff and peak rate of runoff at outlet of the reservoir. Topographic factor (LS) and crop management factor(C) were determined using geographic information system (GIS) and field-based survey of land use/land cover. The conservation practice factor (P) is obtained from the literature. Sediment yield at the outlet of the study reservoir has been simulated for fifteen storm events spread over the period of 2010-2013 and is validated with that of measured values. The resulted coefficient of determination value has been obtained as 0.84 for the study area which indicates that MUSLE model is working satisfactory for the selected basin. Fuzzy logic based model has also been developed to estimate sediment yield. The resulted correlation obtained between Fuzzy logic model and MUSLE model is 0.97 and with that of measured value it is 0.937.
Soft computing models like Artificial Neural Network (ANN) have been widely used to model complex hydrological processes, such as rainfall-runoff and have been reported to be one of the promising tools in hydrology. In this paper, the influences of back propagation algorithm and their efficiencies which affect the input dimensions on rainfall runoff model have been demonstrated. The capability of the Artificial Neural Network with different input dimensions have been attempted and demonstrated with a case study on Sarada River Basin. The developed ANN models were able to map relationship between input and output data sets used. The developed model on rainfall and runoff pattern have been calibrated and validated. The significant input variables for training of ANN models were selected based on statistical parameters viz. cross-correlation, autocorrelation, and partial autocorrelation function. Various combinations were attempted and six combinations were selected based on the statistics of these functions. It was found those models considering rainfall lag rainfall and lag discharge as inputs were performing better than those considering rainfall alone. It was found that the neural network model developed is performing well. It can be inferred from the developed model, Neural Network model was able to predict runoff from rain fall data fairly well for a small semi-arid catchment area considered in the present study.
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