A GMDH Approach for Forecast Monthly Rainfall in Southeast of Pará

Authors

Keywords:

Group Method of Data Handling, Sea Surface Temperature, Multilayer Neural Networks, Rainfall, Forecast

Abstract

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 Marabá, located in the southeastern region of the Pará 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 (R²) 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 GMHD’s effectiveness for the monthly rainfall prediction, constituting an important tool for the planning and assistance to decision makers.

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Author Biographies

Elton Alves, Universidade Federal do Sul e Sudeste do Pará

Is holds a degree in Computer Engineering from the Federal University of Pará and Doctor in Electrical Engineering (Energy System) from the Federal University of Pará. Is Adjunct Professor at the Federal University of the South and Southeast of Pará. His areas of interest are: computational intelligence, embedded systems and atmospheric discharges.

Márcio Lopes, Centro Gestor e Operacional do Sistema de Proteção da Amazônia

Graduated in Agronomy and Meteorology, with a Master’s degree in Environmental Sciences and a PhD in Electrical Engineering. He is currently a Science & Technology Analyst of the Management and Operational Center for the Amazon Protection System. His main research interests include climatology, hydrometeorology, weather radar and satellite remote sensing, lightning, computational intelligence, renewable energy and energy planning with emphasis on the Amazon region.

Fabricio Sales, Universidade Federal do Sul e Sudeste do Pará

Is a computer engineering student at the Federal University of the South and Southeast of Pará.

Andson Balieiro, Universidade Federal de Pernambuco

Received the Ph.D. degree in Computer Science in 2015 at the Federal University of Pernambuco (UFPE), Brazil. Currently, he is a professor at the Center of Informatics (CIn) of the UFPE. He has worked in R&D projects funded by companies (e.g. Morotola Mobility and Ericsson) and governmental institution (e.g. Brazilian National Council for Scientific and Technological Development-CNPQ). He won the FET Best Paper Award in the 31th Wireless and Optical Communications Conference (WOCC). His research interests Ultra-Reliable and Low Latency Communications, 5G/6G Networks and their key-enablers such as Cognitive Radio, Network Slicing, Network Function Virtualization, Multi-Access Edge Computing, and Software-Defined Networking as well as Machine Learning Applications.

Adônis Leal, Universidade Federal do Pará

Received a Master and Doctor degree in electrical engineering (Power Systems) from the Federal University of Pará, Belem, Para, Brazil in 2014 and 2018 respectively. From 2016 to 2017, he worked as a visiting researcher in the Department of Electrical and Computer Engineering at the University of Florida, Gainesville, FL, USA. Since 2018 he is an Adjunct Professor at the Federal University of Para. His main interests are development of embedded systems, lightning physics, lightning detection and location systems and lightning occurrence in the Amazon.

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Published

2023-06-20

How to Cite

Alves, E., Lopes, M., Sales, F., Balieiro, A., & Leal, A. (2023). A GMDH Approach for Forecast Monthly Rainfall in Southeast of Pará. IEEE Latin America Transactions, 21(6), 707–714. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/7327