Analysis of drought and storage for Mula project using ANN and stochastic generation models

Abstract

A hybrid model for streamflow generation is presented to explore the possibilities of using the multilayer feedforward artificial neural networks (ANNs) as generators of future scenarios, with emphasis on the ability to reproduce the statistics of flows related to drought and storage. The artificial neural network model has two components: deterministic and random. The second part of the model incorporates the uncertainty associated with the hydrological processes. The model is applied to the monthly inflows of Mula irrigation project in Maharashtra, India. A comparison of drought and storage among other statistics was made between the performance of the ANN-based model results and the results of the Thomas–Fiering models. The results show that ANN is a promising alternative modelling approach for flow simulation purposes, with interesting potential in the context of water resources systems management and optimization.