FORECASTING OF DAILY RUNOFF USING ARTIFICIAL NEURAL NETWORKS

The modeling of hydrological processes is important in view of the many uses of water resources. The use of artificial neural networks (A NN) is becoming increasingly common in the analysis of hydrology and water resources problems. In the present work, ANN is applied for Gunjwani watershed in lower Bhima sub-basin (Maharashtra, India) to forecast next-day runoff. The ANN architecture, namely multi-layer perceptron (ML P) was adopted. Daily rainfall, runoff, evaporation, humidity, temperature data (full year as well as seasonal) for 7 year 10 months were used for model development. Combinations of the different input data series were studied using correlation between rainfall and runoff.