Development of advanced artificial intelligence models for daily rainfall prediction

In this study, the main objective is to develop and compare several advanced Artificial Intelligent (AI) models namely Adaptive Network based Fuzzy Inference System optimized with Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for the prediction of daily rainfall in Hoa Binh province, Vietnam. For this, meteorological variable parameters such as maximum temperature, minimum temperature, wind speed, relative humidity and solar radiation were collected and used as input parameters and daily rainfall as an output parameter in the models. Validation of the developed models was achieved using various quality assessment criteria such as correlation coefficient (R) and Mean Absolute Error (MAE), Skill Score (SS), Probability of Detection (POD), Critical Success Index (CSI), and False Alarm Ratio (FAR). The results showed that all the AI models provided reasonable predictions of daily rainfall but the SVM was found to be the best method for predicting rainfall. This method was also found to be the most robust and efficient prediction model while taking into account of input variability using the Monte Carlo approach. This AI based study would be helpful in quick and accurate prediction of daily rainfall.