Rainfall Projections for the Brazilian Legal Amazon: An Artificial Neural Networks First Approach
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Control and Quality
2.2.1. Bilinear Interpolation
2.2.2. Convolutional Neural Networks (CNNs)
- -
- Efficiency in processing large volumes of data;
- -
- Ability to handle complex data: CNNs can address the complexity of Amazon rainfall data, which include spatial and temporal variability, seasonality, and extreme events;
- -
- Automatic feature extraction: CNNs automatically learn the relevant features from rainfall data without the need for manual feature engineering, which simplifies the analysis process and improves model accuracy.
2.3. Applied Statistics
- n is the number of data pairs;
- x and y are the variables;
- ∑ represents the sum;
- ∑xy is the sum of the products of x and y;
- ∑x and ∑y are the sums of the variables x and y;
- ∑x2 and ∑y2 are the sums of the squares of the variables x and y.
- i is the value observed at position i;
- Mi is the modeled value at position I;
- Ō is the average of the observed values;
- n is the total number of observations.
- n is the number of observations;
- is the observed values of the dependent variable;
- is the values predicted by the model;
- is the average of the observed values.
- n is the total number of observations;
- is the actual value observed at position I;
- is the predicted (or estimated) value at position I;
- ∣⋅∣ represents the absolute value.
- n is the total number of observations;
- is the real value observed at position I;
- is the predicted (or estimated) value at position i.
2.4. Data Grouping
- Access to the database: The manipulation of the method needed access to a database built by spatial downscaling, which includes information such as the position of each point, expressed as geographic coordinates;
- Application of K-Means: The K-Means algorithm was used to perform the clustering task, and the number of clusters was determined to be 9 after applying techniques such as the elbow method, as illustrated in Figure 2, and the silhouette method (Silhouette Method), as shown in Figure 3 [60,61]. This option proved to be more suited for representing the BLA.
- Model Training: The K-Means model was trained on the spatial downscaling database, and the cluster centroids (Figure 4) were determined as each group’s midpoints. Each color represents a set of coordinates within each ALB state. When the midpoint of each group was returned, no coordinates from the database were supplied; thus, the coordinate closest to the results obtained from the centroids was picked.
2.5. Artificial Neural Networks
3. Results
3.1. Scattering Diagram
3.2. Recurrent Neural Network
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Duffy, P.B.; Brando, P.; Asner, G.P.; Field, C.B. Projections of future meteorological drought and wet periods in the Amazon. Proc. Natl. Acad. Sci. USA 2015, 112, 13172–13177. [Google Scholar] [CrossRef] [PubMed]
- Macedo, G. Climate security, the Amazon, and the responsibility to protect. Braz. Political Sci. Rev. 2021, 15, e0007. [Google Scholar] [CrossRef]
- Carneiro, M.S.; Assis, W.S.; Souza, U.D.V.; Dourado, L. Deforestation governance in the Amazon from a Strategic Action Fields perspective. Ambiente Soc. 2020, 23, e02472. [Google Scholar] [CrossRef]
- Do Nascimento Moura, M.; Vitorino, M.I.; Adami, M. Análise de componentes principais da precipitação pluvial associada à produtividade de soja na Amazônia legal. Rev. Bras. Climatol. 2018, 22. [Google Scholar] [CrossRef]
- Marca-Zevallos, M.J.; Filho, D.D.A.L.; Amaral, I.L.; Matos, F.D.D.A.; Rincón, L.M.; Revilla, J.D.C.; Pansonato, M.P.; Gribel, R.; Barbosa, E.M.; de Andrade Miranda, I.P.; et al. Local hydrological conditions influence tree diversity and composition across the Amazon basin. Ecography 2022, 2022, e06125. [Google Scholar] [CrossRef]
- Silva Junior, C.H.L.; Almeida, C.T.; Santos, J.R.; Anderson, L.O.; Aragão, L.E.; Silva, F.B. Spatiotemporal rainfall trends in the Brazilian legal amazon between the years 1998 and 2015. Water 2018, 10, 1220. [Google Scholar] [CrossRef]
- Almeida, C.T.; Oliveira-Júnior, J.F.; Delgado, R.C.; Cubo, P.; Ramos, M.C. Spatiotemporal rainfall and temperature trends throughout the Brazilian Legal Amazon, 1973–2013. Int. J. Climatol. 2017, 37, 2013–2026. [Google Scholar] [CrossRef]
- Sousa, R.N.d.; Brito, R.X.d.; Ximenes, J.N.d.S. Estudo Comparativo a Partir da Aplicação de Técnicas de Aprendizagem Profunda Baseadas em Dados Pluviométricos Coletados por Estação Meteorológica Automática. Rev. Sist. Comput. RSC 2022, 12, 1. [Google Scholar] [CrossRef]
- Duarte, M.L.; Da Silva Filho, E.P. Estimativa da erosividade da chuva na bacia hidrográfica do rio Juma com base em dados do satélite TRMM/Estimation of rain erosion in the Juma river basin based on TRMM satellite data. Cad. Geogr. 2019, 29, 45. [Google Scholar] [CrossRef]
- De Oliveira-Júnior, J.F.; da Silva Junior, C.A.; Teodoro, P.E.; Rossi, F.S.; Blanco, C.J.C.; Lima, M.; Gois, G.; Correia Filho, W.L.F.; Santiago, D.B.; dos Santos Vanderley, M.H.G. Confronting CHIRPS dataset and in situ stations in the detection of wet and drought conditions in the Brazilian Midwest. Int. J. Climatol. 2021, 41, 4478–4493. [Google Scholar] [CrossRef]
- Tostes, J.O.; Lyra, G.B.; Oliveira-Júnior, J.F.; Francelino, M.R. Assessment of gridded precipitation and air temperature products for the State of Acre, southwestern Amazonia, Brazil. Environ. Earth Sci. 2017, 76, 153–171. [Google Scholar] [CrossRef]
- De Almeida, V.B.; Marton, E. Heavy Rain in Santa Catarina: Synoptic Analysis of an Extreme Event and Numerical Simulation Using WRF Model. Anuário Inst. Geociências 2014, 37, 169–179. [Google Scholar] [CrossRef]
- Rocha, V.M. Avaliação dos impactos das mudanças climáticas na reciclagem de precipitação da Amazônia: Um estudo de modelagem numérica. Rev. Bras. Climatol. 2016, 19. [Google Scholar] [CrossRef]
- Gloor, M.; Barichivich, J.; Ziv, G.; Brienen, R.; Schöngart, J.; Peylin, P.; Barcante Ladvocat Cintra, B.; Feldpaush, T.; Phillips, O.; Baker, J. Recent Amazon climate as background for possible ongoing and future changes of Amazon humid forests. Glob. Biogeochem. Cycles 2015, 29, 1384–1399. [Google Scholar] [CrossRef]
- Cavalcanti, I.F.d.A.; Marengo, J.A.; Alves, L.M.; Costa, D.F. On the opposite relation between extreme precipitation over west Amazon and southeastern Brazil: Observations and model simulations. Int. J. Climatol. 2017, 37, 3606–3618. [Google Scholar] [CrossRef]
- Coutinho, E.d.; Rocha, E.D.; Lima, A.M.M.; Ribeiro, H.M.C.; Gutierrez, L.A.C.L.; Barbosa, A.J.S.; Paes, G.K.A.A.; Bispo, C.J.C.; Tavares, P.A. Variabilidade climática da precipitação na bacia amazônica brasileira entre 1982 e 2012. Rev. Bras. Climatol. 2018, 22, 476–500. [Google Scholar]
- Da Costa Portella, D.A.P.; de Souza Blanco, L.; de Mello Filho, M.E.T.; dos Santos, J.L.A. A importância da Amazônia na dinâmica climática do centro-sul brasileiro: Influência nas dinâmicas ambientais e socioeconômicas. Ens. Geogr. 2022, 9, 66–86. [Google Scholar]
- Leite-Filho, A.T.; Soares-Filho, B.S.; Davis, J.L.; Abrahão, G.M.; Börner, J. Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon. Nat. Commun. 2021, 12, 2591. [Google Scholar] [CrossRef]
- Da Fonseca, I.F.; Lindoso, D.P.; Bursztyn, M. (Falta de) controle do desmatamento na Amazônia brasileira: Do fortalecimento ao desmantelamento da autoridade governamental (1999–2020). Sustain. Debate 2022, 13, 12–31. [Google Scholar] [CrossRef]
- De Castro, E.M.R.; Castro, C.P. Desmatamento na Amazônia, desregulação socioambiental e financeirização do mercado de terras e de commodities. Novos Cad. NAEA 2022, 25, 1. [Google Scholar] [CrossRef]
- Marengo, J.A.; Souza, C.M., Jr.; Thonicke, K.; Burton, C.; Halladay, K.; Betts, R.A.; Alves, L.M.; Soares, W.R. Changes in climate and land use over the Amazon region: Current and future variability and trends. Front. Earth Sci. 2018, 6, 228. [Google Scholar] [CrossRef]
- Silva, C.M.S.; Freitas, S.R.d.; Gielow, R. Ciclo diário da precipitação estimada através de um radar banda S e pelo algoritmo 3B42_V6 do projeto TRMM durante a estação chuvosa de 1999 no sudoeste da Amazônia. Rev. Bras. Meteorol. 2011, 26, 95–107. [Google Scholar] [CrossRef]
- Alves, L.E.R.; Gomes, H.F.B. Validação da imputação múltipla via predictive mean matching para preenchimento de falhas nos dados pluviométricos da Bacia do Médio São Francisco. Anuário Inst. Geociências 2020, 43, 199–206. [Google Scholar] [CrossRef]
- Bleidorn, M.T.; Pinto, W.D.P.; Schmidt, I.M.; Mendonça, A.S.F.; Reis, J.A.T.D. Methodological approaches for imputing missing data into monthly flows series. Rev. Ambiente Água 2022, 17, e2795. [Google Scholar] [CrossRef]
- Pacheco, B.L.S.; Da Silva Júnior, L.G.; De Oliveira, L.A. Estudo da relação entre temperatura/altitude e precipitação/altitude aplicando-se os métodos de correlação e regressão. Rev. Geonorte 2012, 3, 561–572. [Google Scholar]
- World Meteorological Organization—WMO. Provisional 2023 Edition of the Guide to Instruments and Methods of Observation (WMO-No. 8). Available online: https://community.wmo.int/en/activity-areas/imop (accessed on 19 July 2023).
- Carvalho, J.P.D.; Nakai, A.M.; Monteiro, J.E. Spatio-Temporal modeling of data imputation for daily rainfall series in Homogeneous Zones. Rev. Bras. Meteorol. 2016, 31, 196–201. [Google Scholar] [CrossRef]
- Bizarrias, F.S.; Da Silva, L.F.; Penha, R. Preparação de dados e boas práticas em pesquisas quantitativas. Gestão Proj. GeP 2023, 14, 1–10. [Google Scholar]
- Lira, B.R.P.; Fernandes, L.L.; Ishihara, J.H. Pluviometric behavior and trends in the Legal Amazon from 1986 to 2015. Theor. Appl. Climatol. 2022, 150, 1353–1367. [Google Scholar] [CrossRef]
- Da Costa, C.P.W.; de Souza, E.B.; Alves, L.M.; Meira Filho, L.G.; Ferreira, D.B.S.; Kuhn, P.A.F.; Franco, V.S.; Oliveira, J.V.; Sodré, G.R.C. Avaliação de simulação histórica da precipitação e temperatura na Amazônia Oriental utilizando um modelo de alta resolução. Rev. Bras. Climatol. 2019, 25. [Google Scholar] [CrossRef]
- Ferreira, W.R.S.; Vitorino, M.I.; Souza, E.B.D.; Carmo, A.M.C.D. Sazonalidade da precipitação para a Amazônia usando o modelo REGCM3: Avaliando apenas a forçante do Atlântico Equatorial. Rev. Bras. Meteorol. 2012, 27, 435–446. [Google Scholar] [CrossRef]
- De Lima Rabelo, A.C.A.; De Souza Costa, C.E.A. Influência do desmatamento nas precipitações em unidades de conservação da Amazônia. Obs. De La Econ. Latinoam. 2019, 7, 11. [Google Scholar]
- Torres, B.B.; Ribeiro, N.R.; Rocha Filho, O.D.; Pires, D.S. O estudo da utilização da modelagem matemática aplicada à predição temporal de índice pluviométrico inserido na abordagem de redes neuro-nebulosa. Proc. Ser. Braz. Soc. Comput. Appl. Math. 2020, 7, 1. [Google Scholar]
- De Souza, E.B.; Carmo, A.M.C.; de Moraes, B.C.; Nacif, A.; da Silva Ferreira, D.B.; da Rocha, E.J.P.; Souza, P.J.D.O.P. Sazonalidade da precipitação sobre a Amazônia legal brasileira: Clima atual e projeções futuras usando o modelo REGCM4 (Seasonal precipitation over the Brazilian legal Amazon: Climate current and future projections using REGCM4 model). Rev. Bras. Climatol. 2016, 18. [Google Scholar] [CrossRef]
- Marinho, R.R.; Rivera, I.A. A Precipitação Estimada por satélite na Bacia Do Rio Negro, Noroeste Amazônico (1981–2017). RAEGA-O Espaço Geográfico Análise 2021, 50, 44–61. [Google Scholar] [CrossRef]
- Serifi, A.; Günther, T.; Ban, N. Spatio-temporal downscaling of climate data using convolutional and error-predicting neural networks. Front. Clim. 2021, 3, 656479. [Google Scholar] [CrossRef]
- Collischonn, B.; Collischonn, W.; Tucci, C.E.M. Daily hydrological modeling in the Amazon basin using TRMM rainfall estimates. J. Hydrol. 2008, 360, 207–216. [Google Scholar] [CrossRef]
- Abreu, M.; Souza, A.; Lins, T.M.P.; Júnior, J.F.O.; Oliveira, S.S.; Fernandes, W.; Almeida, L.T.; Torsen, E. Comparison and validation of TRMM satellite precipitation estimates and data observed in Mato Grosso do Sul state, Brazil. Rev. Bras. Climatol. 2020, 27, 566–589. [Google Scholar]
- Mu, Y.; Jones, C. An observational analysis of precipitation and deforestation age in the Brazilian Legal Amazon. Atmos. Res. 2022, 271, 106122. [Google Scholar] [CrossRef]
- Dos Santos NETO, L.A.; Maniesi, V.; Querino, C.A.S.; da Silva, M.J.G.; Brown, V.R. Modelagem hidroclimatologica utilizando redes neurais multi layer perceptron em bacia hidrográfica no sudoeste da Amazônia. Rev. Bras. Climatol. 2020, 26. [Google Scholar] [CrossRef]
- Mendes, D.; Marengo, J.A. Temporal downscaling: A comparison between artificial neural network and autocorrelation techniques over the Amazon Basin in present and future climate change scenarios. Theor. Appl. Climatol. 2010, 100, 413–421. [Google Scholar] [CrossRef]
- Molina, M.J.; O’Brien, T.A.; Anderson, G.; Ashfaq, M.; Bennett, K.E.; Collins, W.D.; Dagon, K.; Restrepo, J.M.; Ullrich, P.A. A Review of Recent and Emerging Machine Learning Applications for Climate Variability and Weather Phenomena. Artif. Intell. Earth Syst. 2023, 2, 1–46. [Google Scholar] [CrossRef]
- Dipietro, R.; Hager, G.D. Deep learning: RNNs and LSTM. In Handbook of Medical Image Computing and Computer Assisted Intervention; Academic Press: Cambridge, MA, USA, 2020; pp. 503–519. [Google Scholar]
- Barco-Jiménez, J.; Eraso-Checa, F.; Pantoja, A.; Caicedo-Bravo, E. Estimation of Global Solar Radiation Using NNARX Neural Networks Based on the UV Index. Tecnura 2021, 25, 41–50. [Google Scholar] [CrossRef]
- De Mendonça, L.M.; de Souza Gomide, I.; de Sousa, J.V.; Blanco, C.J.C. Modelagem chuva-vazão via redes neurais artificiais para simulação de vazões de uma bacia hidrográfica da Amazônia. Rev. Gestão Água América Lat. 2021, 18, 2021. [Google Scholar] [CrossRef]
- E Silva, A.A.; Braga, M.Q.; Ferreira, J.; dos Santos, V.J.; do Carmo Alves, S.; de Oliveira, J.C.; Calijuri, M.L. Anthropic activities and the Legal Amazon: Estimative of impacts on forest and regional climate for 2030. Remote Sens. Appl. Soc. Environ. 2020, 18, 100304. [Google Scholar]
- Cavalcante, R.B.L.; da Silva Ferreira, D.B.; Pontes, P.R.M.; Tedeschi, R.G.; da Costa, C.P.W.; de Souza, E.B. Evaluation of extreme rainfall indices from CHIRPS precipitation estimates over the Brazilian Amazonia. Atmos. Res. 2020, 238, 104879. [Google Scholar] [CrossRef]
- Ab’Saber, A.N. Os Domínios de Natureza No Brasil: Potencialidades Paisagísticasi; Ateliê Editorial: São Paulo, Brazil, 2012; SNUC-Sistema Nacional de Unidades de conservação: Texto da Lei, v. 9, p. 28, 2019. [Google Scholar]
- Nobre, C.A.; Obregón, G.O.; Marengo, J.A.; Fu, R.; Poveda, G. Características do clima amazônico: Aspectos principais. Amaz. Glob. Chang. 2009, 149–162. [Google Scholar] [CrossRef]
- Dubreuil, V.; Fante, K.P.; Planchon, O.; Sant’anna Neto, J.L. Climate change evidence in Brazil from Köppen’s climate annual types frequency. Int. J. Climatol. 2019, 39, 1446–1456. [Google Scholar] [CrossRef]
- Alvares, C.A.; Stape, J.L.; Sentelhas, P.C.; Gonçalves, J.D.M.; Sparovek, G. Köppen’s climate classification map for Brazil. Meteorol. Z. 2013, 22, 711–728. [Google Scholar] [CrossRef]
- Park, N.-W. Spatial downscaling of TRMM precipitation using geostatistics and fine scale environmental variables. Adv. Meteorol. 2013, 2013, 237126. [Google Scholar] [CrossRef]
- Gribbon, K.T.; Bailey, D.G. A novel approach to real-time bilinear interpolation. In Proceedings of the DELTA 2004. Second IEEE International Workshop on Electronic Design, Test and Applications, Perth, WA, Australia, 28–30 January 2004; IEEE: New York, NY, USA, 2004; pp. 126–131. [Google Scholar]
- Jafarian, A.; Basiligheh, N. Artificial neural networks approach to the bivariate interpolation problem. Afr. Mat. 2015, 26, 1187–1197. [Google Scholar] [CrossRef]
- Hung, K.-W.; Wang, K.; Jiang, J. Image interpolation using convolutional neural networks with deep recursive residual learning. Multimed. Tools Appl. 2019, 78, 22813–22831. [Google Scholar] [CrossRef]
- Singh, K.; Seth, A.; Sandhu, H.S.; Samdani, K. A comprehensive review of convolutional neural network based image enhancement techniques. In Proceedings of the 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 29–30 March 2019; IEEE: New York, NY, USA, 2019; pp. 1–6. [Google Scholar]
- Cohen, I.; Huang, Y.; Chen, J.; Benesty, J. Pearson correlation coefficient. In Noise Reduction in Speech Processing; Springer: Berlin/Heidelberg, Germany, 2009; pp. 1–4. [Google Scholar]
- De Myttenaere, A.; Golden, B.; Le Grand, B.; Rossi, F. Mean absolute percentage error for regression models. Neurocomputing 2016, 192, 38–48. [Google Scholar] [CrossRef]
- Hodson, T.O. Root mean square error (RMSE) or mean absolute error (MAE): When to use them or not. Geosci. Model Dev. 2022, 15, 5481–5487. [Google Scholar] [CrossRef]
- Cui, M. Introduction to the k-means clustering algorithm based on the elbow method. Account. Audit. Financ. 2020, 1, 5–8. [Google Scholar]
- Punhani, A.; Faujdar, N.; Mishra, K.K.; Subramanian, M. Binning-based silhouette approach to find the optimal cluster using K-means. IEEE Access 2022, 10, 115025–115032. [Google Scholar] [CrossRef]
- Mckinney, W. Pandas: A foundational Python library for data analysis and statistics. Python High Perform. Sci. Comput. 2011, 14, 1–9. [Google Scholar]
- Bernard, J. Python Data Analysis with Pandas. In Python Recipes Handbook: A Problem-Solution Approach; Apress: Berkeley, CA, USA, 2016; pp. 37–48. [Google Scholar]
- Montenegro-Murillo, D.D.; Pérez-Ortiz, M.A.; Vargas-Franco, V. Using Artificial Neural Networks to predict monthly precipitation for the Cali River basin, Colombia. Dyna 2019, 86, 122–130. [Google Scholar]
- Hauck, T. scikit-learn Cookbook; Packt Publishing: Birmingham, UK, 2014. [Google Scholar]
- Sial, A.H.; Rashdi, S.Y.S.; Khan, A.H. Comparative analysis of data visualization libraries Matplotlib and Seaborn in Python. Int. J. 2021, 10, 45. [Google Scholar]
- Ghojogh, B.; Ghodsi, A. Recurrent neural networks and long short-term memory networks: Tutorial and survey. arXiv 2023, arXiv:2304.11461. [Google Scholar]
- Chao, Z.; Yin, Y.; Han, B.; Chen, X. Research on real-time local rainfall prediction based on MEMS sensors. J. Sens. 2018, 2018, 184713. [Google Scholar] [CrossRef]
- Liao, W.; Yin, Z.; Wang, R.; Lei, X. Rainfall-Runoff Modelling Based on Long Short-Term Memory (Lstm). In Proceedings of the 38th IAHR World Congress, Panama City, Panama, 1–6 September 2019; pp. 1–6. [Google Scholar]
- Dibike, Y.B.; Coulibaly, P. Temporal neural networks for downscaling climate variability and extremes. Neural Netw. 2006, 19, 135–144. [Google Scholar] [CrossRef] [PubMed]
- Maskey, M.L.; Puente, C.E.; Sivakumar, B. Temporal downscaling rainfall and streamflow records through a deterministic fractal geometric approach. J. Hydrol. 2019, 568, 447–461. [Google Scholar] [CrossRef]
- Zhang, L.; Xiu, J.; Yang, Z.; Liu, C. An Optimized Interpolation Model Based on K–means Clustering for Rainfall Calculation. In Proceedings of the 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), Harbin, China, 25–27 December 2020; IEEE: New York, NY, USA, 2020; pp. 1198–1202. [Google Scholar]
- Begum, M.; Das, B.C.; Hossain, M.Z.; Saha, A.; Papry, K.A. An improved Kohonen self-organizing map clustering algorithm for high-dimensional data sets. Indones. J. Electr. Eng. Comput. Sci. 2021, 24, 600–610. [Google Scholar] [CrossRef]
- Chen, Y.; Xu, J. Rainfall-runoff short-term forecasting method based on LSTM. J. Phys. Conf. Ser. 2021, 2025, 12005. [Google Scholar] [CrossRef]
- Pranolo, A.; Mao, Y.; Tang, Y.; Wibawa, A.P. A long short-term memory implemented for rainfall forecasting. In Proceedings of the 2020 6th International Conference on Science in Information Technology (ICSITech), Palu, Indonesia, 21–22 October 2020; IEEE: New York, NY, USA, 2020; pp. 194–197. [Google Scholar]
- De Azevedo Silva, V.; Peixoto, M.; Santos, F.L. Monthly Rainfall Forecast in the municipality of Barra Mansa/RJ using deep learning time series techniques. Holos 2023, 5, 39. [Google Scholar] [CrossRef]
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Monteiro, L.A.F.; do Nascimento, F.I.C.; de Oliveira-Júnior, J.F.; Nunes, D.D.; Mendes, D.; de Gois, G.; Sanches, F.d.O.; Wollmann, C.A.; Watanabe, M.; Gobo, J.P.A. Rainfall Projections for the Brazilian Legal Amazon: An Artificial Neural Networks First Approach. Climate 2024, 12, 187. https://doi.org/10.3390/cli12110187
Monteiro LAF, do Nascimento FIC, de Oliveira-Júnior JF, Nunes DD, Mendes D, de Gois G, Sanches FdO, Wollmann CA, Watanabe M, Gobo JPA. Rainfall Projections for the Brazilian Legal Amazon: An Artificial Neural Networks First Approach. Climate. 2024; 12(11):187. https://doi.org/10.3390/cli12110187
Chicago/Turabian StyleMonteiro, Luiz Augusto Ferreira, Francisco Ivam Castro do Nascimento, José Francisco de Oliveira-Júnior, Dorisvalder Dias Nunes, David Mendes, Givanildo de Gois, Fabio de Oliveira Sanches, Cassio Arthur Wollmann, Michel Watanabe, and João Paulo Assis Gobo. 2024. "Rainfall Projections for the Brazilian Legal Amazon: An Artificial Neural Networks First Approach" Climate 12, no. 11: 187. https://doi.org/10.3390/cli12110187
APA StyleMonteiro, L. A. F., do Nascimento, F. I. C., de Oliveira-Júnior, J. F., Nunes, D. D., Mendes, D., de Gois, G., Sanches, F. d. O., Wollmann, C. A., Watanabe, M., & Gobo, J. P. A. (2024). Rainfall Projections for the Brazilian Legal Amazon: An Artificial Neural Networks First Approach. Climate, 12(11), 187. https://doi.org/10.3390/cli12110187