Prediction of Regional Heavy Precipitation Occurrence in the Southwest Iran Using Synoptic Variables and Data Mining Methods

Short-term prediction of heavy precipitation events is especially crucial in flood warning and mitigation. This study offered a novel concept of the regional heavy precipitation based on the probability pattern of a typical rainstorm. Daily precipitation data of 12 synoptic stations located over southwestern Iran were used for this purpose. In addition, six synoptic variables at 1000 to 200 hPa pressure levels on one to five days before heavy precipitations (covering a wide range outside the study area) were used as predictors. All data used in this study cover the period 1987- 2018. Four feature selection methods and 10 binary classifier machine-learning models were employed in this study. The results revealed that using synoptic data up to four days prior to the events best distinguishes heavy precipitation from non-heavy precipitation events. In addition, among the four feature selection methods, Chi-Square and Extra Tree methods are superior to Correlation and Random Forest. As a result of this study, it was found that the Random Forest model with the Chi-Square feature selection method has the highest efficiency in predicting regional heavy precipitation events in the study area. Relative humidity and specific humidity 1-2 days before and wind speed 2-4 days before the precipitation events are relevant synoptic variables for predicting heavy precipitation events.