Analysis of different combinations of meteorological parameters in predicting rainfall with an ANN approach: A case study in Morphou, Northern Cyprus

Abstract

Forecasting rainfall is one of the most essential issues in the hydrological cycle. It is very challenging because is still not possible to develop an ideal model given the uncertainty and unexpected varia-tion. Therefore, the objective of the study is to predict the monthly rainfall using an artificial neural network (ANN) approach. In this study, 25 ANN models are developed by varying the weather parameters. A 33-year database (1985–2017) comprising monthly rainfall, minimum temperature, maximum temperature, average temperature, global solar radiation (GSR), sunshine duration, and wind speed have been used in the ANN models. All the models are validated and the performances of the models are analyzed by using different statistical tools such as the R-squared, root mean squared error, and mean absolute error value. Out of the 25 ANN models, ANN-13, ANN-17, and ANN-23 have given the best prediction with the combinations of (Tmin, Tmax, W), (Tmin, Tmax, SD, GSR) and (Tmin, Tmax, Tav, W, SD), respectively. The proposed approach illustrates how the ANN modeling technique can be used to identify the key meteorological variables required to the most significant meteorological parameters affecting rainfall.