Multivariable modelling based on statistical and machine learning techniques for monthly precipitation forecasting in the eastern Amazon

Renata Goncalves Tedeschi^1*Antonio Nogueira Neto¹Claudia Priscila Wanzeler Da Costa¹Julio Cezar Gonçalves de Freitas^1,2Rafael de Lima Rocha^1,3Ronnie Alves^1,3Eduardo Costa de Carvalho¹Ewerton Cristhian Lima de Oliveira^1,3

• ¹Vale Technological Institute (ITV), Belém, Brazil
• ²Universidade Federal do Pará, Tucuruí, Brazil
• ³Federal University of Pará, Belém, Pará, Brazil

Accurate precipitation forecasting is crucial for various sectors, such as agriculture, hydrology, and disaster management. In recent years, machine learning (ML) techniques have proven invaluable in improving the accuracy of rainfall prediction and identifying the complex relationships between precipitation and other meteorological variables. This paper presents a comprehensive analysis of the use of multivariable statistical and ML models to predict monthly rainfall at 13 locations in the eastern Amazon. Each model is trained separately for each month, allowing for a tailored representation of precipitation patterns and variations. Additionally, the performance of these models is evaluated via the time series cross-validation technique and an independent test. The results indicate that for the points Serra Sul, Ac ¸ail ândia, and Ponta da Madeira, the multivariable models yielded the best monthly performance in 72.23% of the cases, mainly during the rainy season. This research highlights the promise of leveraging machine learning to enhance precipitation predictions and mitigate associated risks.