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In this study, multi-layer perceptron (MLP) artificial neural networks have\udbeen applied to forecast one-month-ahead inflow for the Ubonratana reservoir,\udThailand. To assess how well the forecast inflows have performed in the\udoperation of the reservoir, simulations were carried out guided by the\udsystems rule curves. As basis of comparison, four inflow situations were\udconsidered: (1) inflow known and assumed to be the historic (Type A);\ud(2) inflow known and assumed to be the forecast (Type F); (3) inflow known\udand assumed to be the historic mean for month (Type M); and (4) inflow is\udunknown with release decision only conditioned on the starting reservoir\udstorage (Type N). Reservoir performance was summarised in terms of\udreliability, resilience, vulnerability and sustainability. It was found that\udType F inflow situation produced the best performance while Type N was the\udworst performing. This clearly demonstrates the importance of good inflow\udinformation for effective reservoir operation

Inflow forecasting using Artificial Neural Networks for reservoir operation