Abstract
The ANN and fuzzy logic (FL) models were developed to forecast the runoff and sediment yield for catchment of Kal River, India in METLAB 2.9b witting the programme supporting to nntool. The input to the models were used as daily rainfall, evaporation, temperature and one day and tow day lag runoff for runoff modelling. Whereas, for sediment yield modelling inputs in ANN and Fuzzy logic model used as daily rainfall, one and two day runoff. The inputs data for both models of 21 years (1991 to 2011) were considered in present study on daily basis. The 14 years (1991 to 2004) used in developing the models whereas rest 7 years (2005 to 2011) for validation of the models. In sediment yield modelling, 7 years (2003 to 2009) data were used for developing and validation of models. The models performance were evaluated by standard statistical indices such R, RMSE, EV, CE, and MAD. It was found that ANN model performance improved with increasing the input vectors. The fuzzy logic model was performed well with R value more than 0.95 during developmental stage and validation stage over ANN model for predicting runoff and sediment yield. Hence, FL model found to be more superior to ANN in prediction of runoff and sediment yield for Kal river.
Highlights
The hydrological behaviour of a watershed assess on the basis of availability of water, vegetation and status of soil in relation to productivity
A day fuzzy logic approach was applied in water resources (Nayak, et al 200518, Zhu and Fujita, 199419; See and Openshaw, 199920; Stuber et al, 200021; Hundecha et al, 200122), mamdani approaches in predicting runoff and sediment yield (Nayak et al, 200518; Mamdani and Assilian, 1975)23); it to flood forecasting (Chang et al 2005)[24]; precipitation forecasting (Maskey et al 2004)[25]; sediment transport (Tayfur et al 2003)[26], reservoir operation (Tilmant et al 2002)[27], and storm water infiltration estimation (Hong et al 2002)[28] etc in hydrologic studies. The different models such as fuzzy logic, Artificial Neural Network (ANN) and Sediment Rating Curve (SRC) models used for predicting suspended sediment yield and the results show a higher accuracy of fuzzy rule base model assessments in comparison with neural networks and sediment rating curve assessments (Angabini et al, 201429)
artificial neural network (ANN) rainfall runoff modelling for Kal River The rainfall, runoff and sediment yield modelling was undertaken for Kal river considering rainfall, runoff, temperature and evaporation parameters of Birwadi station
Summary
The hydrological behaviour of a watershed assess on the basis of availability of water, vegetation and status of soil in relation to productivity. Accurate rainfall-runoff relationship predictions mostly depend on the availability of accurate data of rainfall and runoff. The several hydrologic models were adopted for prediction of runoff and sediment yield which cover from black-box neural network model to highly conceptualised physical based mathematical models (Porporato and Ridolfi, 2001)[1]. It is needed to have the knowledge of rainfall effect on runoff from watershed to avoid risk of flood and drought characteristic in changing climatic scenario. Forecasting of such non-linearity and uncertainty associated with rainfall-runoff process and sediment yield has lot of importance in surface hydrology for design of conservation structures, water harvesting dams, civil works, flood monitoring, etc (Shirk et al 2012)[2]. Sinha et al (2013)[3] and Chen et al (2013)[4] stated that, accurate simulation of responses to surface runoff and sediment yield from watershed due to climatic parameters (rainfall, evaporation, temperature etc) is great challenge to hydrologist
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