Investigating the Performance of Data Mining Models in Rainfall Forecasting and Drought Analysis of Bandar Abbas Synoptic Station

It is common to use different data mining methods in drought prediction. However, the selection of the best model is mainly based on the accuracy of the simulation, while most of the studies do not mention the features of the models. In this paper, the performance of the most common data mining models, including Multilayer Perceptron Artificial Neural Network (ANN-MLP), Radial Base Function Neural Network (ANN-RBF), Regression Decision Tree (CART), Model Tree (M5P), and Support Vector Machine (SVM) is evaluated in order to predict monthly one year ahead rainfall at Bandar Abbas synoptic station and then the characteristics of each of them are described. Calibration and validation of the models were done using raw data and a three-year moving average of climatic parameters from 1347 to 1396. The performance of the models has been evaluated using different statistical indices and comparative diagrams. The results showed that the SVM and M5P models have good prediction performance with RMSE of 7.93 and 8.31 mm, the MAE of 3.66 and 4.69 mm, and the CC of 0.83 and 0.82, respectively. Also, with the exception of the CART, the change in the data mining tool makes an eight to 11 percent difference in the accuracy of the estimates. Therefore, the most appropriate model should be selected based on other characteristics of the methods besides their accuracy. In addition, using the three-year moving average of the input parameters has increased the correlation coefficient by about 78 percent and reduced the RMSE by about 63 percent. The analysis of the long-term drought situation showed that with the increase in the period of the standard precipitation index, the separation of wet and dry years becomes more specific.