A GMDH Approach for Forecast Monthly Rainfall in Southeast of Para
Elton Rafael Alves, Márcio Nirlando Gomes Lopes, Fabricio Sales, Andson Balieiro, Adonis F. R. Leal · IEEE Latin America Transactions · 2023
Modeling for rainfall forecasting is widely used in climate forecasting. A high volume of precipitation or scarcity brings serious problems for society. Thus, a methodology for monthly rainfall forecast is presented using Group Method of Data Handling (GMDH) and sea surface temperature (SST). The intelligent model gets the mean monthly SST, in predefined and temporally lagged areas, after a variable selection step. For model training, precipitation data from the Climate Prediction Center were used. The methodology was applied in a certain area of the municipality of Maraba, located in the southeastern region of the Para state. The results obtained with GMDH overcame those got by using a conventional multilayer Artificial Neural those got by using a conventional multilayer Artificial Neural Network (RNA), reaching values for regression coefficient (R) and root mean square error (RMSE) equal to 0.96363 and 30.8100 in the test stage, while the RNA-based model got 0.81712 and 68.2607, respectively. Results show the GMHDs effectiveness for the monthly rainfall prediction, constituting an important tool for the planning and assistance to decision makers.