FORWARD A Model for FOrecasting Reservoir WAteR Dynamics Using Spatial Bayesian Network (SpaBN)
Abstract: Natural systems, like the hydrological, climatological, atmospheric, or any other environmental processes, are extremely complex as well as dynamic in nature. It is therefore difficult to forecast, analyze, and quantify these processes by using simple empirical equations. Modeling and forecasting of reservoir water dynamics are not exceptions in this respect, as these involve various challenges due to t the effect of meteorological factors, natural processes of stream flow, climatic change, and so on. The intent of our present work is to propose a novel forecasting model, FORWARD, that handles some of these issues in complex reservoir dynamics. FORWARD is based on a variant of spatial Bayesian network (SpaBN), having inherent capability of modeling impact of spatial variability of meteorological factors over the river catchment. The forecasting efficiency of FORWARD has been compared with four other line linear and non-linear linear techniques based on six different statistical performance measures. The experimental results show the superiority of FORWARD over the other techniques. Though FORWARD has been demonstrated with respect to a case study on forecasting reservoir reser live capacity, the model possesses a generic structure that can also be applied in other domains by introducing minimal augmentation.