(untitled) (International Journal of Communication Systems and Network Technologies, 2024)
Rainfall forecasting has been one of the most challenging problem for scientists and engineers around the globe. A rainfall forecast is highly beneficial for efficient management of surface and subsurface water resources in arid regions. Rainfall is a complex hydrological phenomenon which depends on many atmospheric processes. These atmospheric conditions are generally used for rainfall prediction. Two atmospheric variables, Temperature and Cloud Cover, are used in this study for rainfall prediction. Rainfall distribution is not uniform among different stations but stations share a strong correlation, and this property has been used for improve the prediction accuracy of a model. Bayesian network offers framework to incorporate the conditional dependencies between the nodes/variables. K2 algorithm is used to finding out the rainfall dependencies among the stations. Thirty-two stations of Rajasthan, India are used for this study. Rainfall, temperature and cloud cover data of 102 years has been used for this study which is divided into standard 70:30 ratio for training and testing respectively. The prediction results of both cases (considering inter-station rainfall dependencies and without considering inter-station rainfall dependencies) are compared and an improvement in accuracy was found at most of the stations while considering inter-station dependencies.