A proper assessment of reservoir water dynamics is of utmost importance for the development of any region. However, the reservoir water dynamics is not merely a periodic event. Rather, it is a result of complex interplay among various water balancing components, especially the meteorological factors. So, the key objectives of this research work are to model the influence of spatial variability of meteorological variables on the hydrological processes in a reservoir, and to utilize this knowledge of spatial variability to aid in prediction of reservoir dynamics. In this regard, we propose FORWARD, a forecasting model based on spatial Bayesian network (SpaBN) which has inherent capability of efficiently modeling the spatial impact of meteorological and topographical factors distributed over the watershed. The forecasting efficiency of FORWARD has been compared with a set of linear and non-linear prediction techniques with respect to a case study on forecasting daily live capacity of the Mayurakshi reservoir in India. The experimental results show the superiority of FORWARD over the others.

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 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 linear and non-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 live capacity, the model possesses a generic structure that can also be applied in other domains by introducing minimal augmentation.