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Modeling and Predicting Climate Parameters in Guinea using Simple Perceptrons: A Study on Temperature and Precipitation

by Katakpe Kossi Kuma, Camara Moustapha N’gonet, Sow Abdoulaye, Kpoghomou Pépé, Makanera Mohamed
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 101
Year of Publication: 2026
Authors: Katakpe Kossi Kuma, Camara Moustapha N’gonet, Sow Abdoulaye, Kpoghomou Pépé, Makanera Mohamed
10.5120/ijca0b68eb639d08

Katakpe Kossi Kuma, Camara Moustapha N’gonet, Sow Abdoulaye, Kpoghomou Pépé, Makanera Mohamed . Modeling and Predicting Climate Parameters in Guinea using Simple Perceptrons: A Study on Temperature and Precipitation. International Journal of Computer Applications. 187, 101 ( May 2026), 41-58. DOI=10.5120/ijca0b68eb639d08

@article{ 10.5120/ijca0b68eb639d08,
author = { Katakpe Kossi Kuma, Camara Moustapha N’gonet, Sow Abdoulaye, Kpoghomou Pépé, Makanera Mohamed },
title = { Modeling and Predicting Climate Parameters in Guinea using Simple Perceptrons: A Study on Temperature and Precipitation },
journal = { International Journal of Computer Applications },
issue_date = { May 2026 },
volume = { 187 },
number = { 101 },
month = { May },
year = { 2026 },
issn = { 0975-8887 },
pages = { 41-58 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number101/modeling-and-predicting-climate-parameters-in-guinea-using-simple-perceptrons-a-study-on-temperature-and-precipitation/ },
doi = { 10.5120/ijca0b68eb639d08 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-05-17T02:28:57+05:30
%A Katakpe Kossi Kuma
%A Camara Moustapha N’gonet
%A Sow Abdoulaye
%A Kpoghomou Pépé
%A Makanera Mohamed
%T Modeling and Predicting Climate Parameters in Guinea using Simple Perceptrons: A Study on Temperature and Precipitation
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 101
%P 41-58
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The climate in Guinea, like many regions around the world, is undergoing significant changes, impacting ecosystems, human societies, and local economies. Accurate forecasting of climate parameters, such as temperature and precipitation, is essential for managing climate-related risks and informing environmental and agricultural policies. This study explores the use of simple perceptrons, a type of artificial neural network, to model and predict temperature and precipitation patterns in Guinea. The data used for training and testing the model comes from historical climate records collected from various meteorological stations across the country over several years. The methodology is based on the classic architecture of simple perceptrons, with a gradient descent learning algorithm and an adapted error function. The results indicate that the model is capable of predicting temperature fluctuations and precipitation levels with satisfactory accuracy, though improvements are needed for better performance, particularly for long-term predictions. When compared to traditional climate modeling methods, the simple perceptron approach shows promising results, while also revealing limitations due to the complexity of the region's climatic phenomena. These findings open the door for future research to explore more advanced neural network structures, such as multilayer networks, and to incorporate additional variables to enhance predictions on a larger scale and for longer time horizons. This study contributes to the growing body of knowledge on applying artificial intelligence techniques to climate forecasting, with a particular focus on the specific climatic conditions of Guinea.

References
  1. R. L. Miller et al., « Mineral dust aerosols in the NASA Goddard Institute for Space Sciences ModelE atmospheric general circulation model », J. Geophys. Res. Atmospheres, vol. 111, no D6, p. 2005JD005796, mars 2006, doi: 10.1029/2005JD005796.
  2. D. A. Randall et al., « Climate Models and Their Evaluation ».
  3. T. Schneider, L. R. Leung, et R. C. Wills, « Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence », Atmospheric Chem. Phys., vol. 24, no 12, p. 7041‑7062, 2024.
  4. A. Lyra, A. Loukas, P. Sidiropoulos, et N. Mylopoulos, « Climatic modeling of seawater intrusion in coastal aquifers: understanding the climate change impacts », Hydrology, vol. 11, no 4, p. 49, 2024.
  5. B. C. Hewitson, J. Daron, R. G. Crane, M. F. Zermoglio, et C. Jack, « Interrogating empirical-statistical downscaling », Clim. Change, vol. 122, no 4, p. 539‑554, févr. 2014, doi: 10.1007/s10584-013-1021-z.
  6. S. Rasp, M. S. Pritchard, et P. Gentine, « Deep learning to represent subgrid processes in climate models », Proc. Natl. Acad. Sci., vol. 115, no 39, p. 9684‑9689, sept. 2018, doi: 10.1073/pnas.1810286115.
  7. T. Weber, A. Corotan, B. Hutchinson, B. Kravitz, et R. Link, « Deep learning for creating surrogate models of precipitation in Earth system models », Atmospheric Chem. Phys., vol. 20, no 4, p. 2303‑2317, 2020.
  8. S. Haykin, Neural networks and learning machines, 3/E. Pearson education india, 2009.
  9. D. P. Solomatine et A. Ostfeld, « Data-driven modelling: some past experiences and new approaches », J. Hydroinformatics, vol. 10, no 1, p. 3‑22, 2008.
  10. K. Hsu, H. V. Gupta, et S. Sorooshian, « Artificial Neural Network Modeling of the Rainfall‐Runoff Process », Water Resour. Res., vol. 31, no 10, p. 2517‑2530, oct. 1995, doi: 10.1029/95WR01955.
  11. M. Ghil et al., « ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES », Rev. Geophys., vol. 40, no 1, févr. 2002, doi: 10.1029/2000RG000092.
  12. Francis P. Bretherton, Catherine A. Smith, et John M. Wallace, « An intercomparison of methods for estimating the influence of the ENSO on global climate », vol. 12, no 3, Journal of Climate, p. 737‑756, 1999.
  13. J. Katzav et W. S. Parker, « The future of climate modeling », Clim. Change, vol. 132, no 4, p. 475‑487, oct. 2015, doi: 10.1007/s10584-015-1435-x.
  14. Otto, Friederike EL, Ferro, Christopher AT, Fricker, Thomas E, et Suckling, Emma B, « On judging the credibility of climate predictions », Springer, vol. 132, no 1, Climatic Change, p. 47---60, 2015.
  15. J. Sillmann et al., « Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities », Weather Clim. Extrem., vol. 18, p. 65‑74, 2017.
  16. Y. LeCun, Y. Bengio, et G. Hinton, « Deep learning », nature, vol. 521, no 7553, p. 436‑444, 2015.
  17. I. Goodfellow, « NIPS 2016 Tutorial: Generative Adversarial Networks », 3 avril 2017, arXiv: arXiv:1701.00160. doi: 10.48550/arXiv.1701.00160.
  18. D. E. Rumelhart, G. E. Hinton, et R. J. Williams, « Learning representations by back-propagating errors », nature, vol. 323, no 6088, p. 533‑536, 1986.
  19. F. Rosenblatt, « The perceptron: a probabilistic model for information storage and organization in the brain. », Psychol. Rev., vol. 65, no 6, p. 386, 1958.
  20. K. Hornik, M. Stinchcombe, et H. White, « Multilayer feedforward networks are universal approximators », Neural Netw., vol. 2, no 5, p. 359‑366, 1989.
  21. G. Cybenko, « Approximation by superpositions of a sigmoidal function », Math. Control Signals Syst., vol. 2, no 4, p. 303‑314, déc. 1989, doi: 10.1007/BF02551274.
  22. G. Cybenko, « Approximations by superpositions of a sigmoidal function », Math. Control Signals Syst., vol. 2, p. 183‑192, 1989.
  23. F. Rosenblatt, « The perceptron: a probabilistic model for information storage and organization in the brain. », Psychol. Rev., vol. 65, no 6, p. 386, 1958.
  24. M. Minsky et S. Papert, Perceptrons: An Introduction to Computational. MIT press, 1988.
  25. S. A. Papert, Perceptrons-an Introduction to Computational Geometry. Mit Press Limited, 2017.
  26. K.-I. Funahashi, « On the approximate realization of continuous mappings by neural networks », Neural Netw., vol. 2, no 3, p. 183‑192, 1989.
  27. W. W. Hsieh et B. Tang, « Applying neural network models to prediction and data analysis in meteorology and oceanography », Bull. Am. Meteorol. Soc., vol. 79, no 9, p. 1855‑1870, 1998.
  28. A. Nair, G. Singh, et U. C. Mohanty, « Prediction of Monthly Summer Monsoon Rainfall Using Global Climate Models Through Artificial Neural Network Technique », Pure Appl. Geophys., vol. 175, no 1, p. 403‑419, janv. 2018, doi: 10.1007/s00024-017-1652-5.
  29. H. Astsatryan et al., « Air temperature forecasting using artificial neural network for Ararat valley », Earth Sci. Inform., vol. 14, no 2, p. 711‑722, juin 2021, doi: 10.1007/s12145-021-00583-9.
  30. M. W. Gardner et S. R. Dorling, « Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences », Atmos. Environ., vol. 32, no 14‑15, p. 2627‑2636, 1998.
  31. H. R. Maier et G. C. Dandy, « Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications », Environ. Model. Softw., vol. 15, no 1, p. 101‑124, 2000.
  32. J. Maqsood, H. Afzaal, A. A. Farooque, F. Abbas, X. Wang, et T. Esau, « Statistical downscaling and projection of climatic extremes using machine learning algorithms », Theor. Appl. Climatol., vol. 153, no 3‑4, p. 1033‑1047, août 2023, doi: 10.1007/s00704-023-04532-y.
  33. R. Balmaceda-Huarte, J. Baño-Medina, M. E. Olmo, et M. L. Bettolli, « On the use of convolutional neural networks for downscaling daily temperatures over southern South America in a climate change scenario », Clim. Dyn., vol. 62, no 1, p. 383‑397, janv. 2024, doi: 10.1007/s00382-023-06912-6.
  34. K. Hsu, H. V. Gupta, et S. Sorooshian, « Artificial Neural Network Modeling of the Rainfall‐Runoff Process », Water Resour. Res., vol. 31, no 10, p. 2517‑2530, oct. 1995, doi: 10.1029/95WR01955.
  35. O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. Mohamed, et H. Arshad, « State-of-the-art in artificial neural network applications: A survey », Heliyon, vol. 4, no 11, 2018, Consulté le: 12 mars 2026. [En ligne]. Disponible sur: https://www.cell.com/heliyon/fulltext/S2405-8440(18)33206-7
  36. S. Cen, W. Chen, S. Chen, L. Wang, Y. Liu, et J. Huangfu, « Weakened influence of El Niño–Southern Oscillation on the zonal shift of the South Asian High after the early 1980s », Int. J. Climatol., vol. 42, no 15, p. 7583‑7597, 2022.
  37. S.-Q. Dotse, « Deep learning–based long short-term memory recurrent neural networks for monthly rainfall forecasting in Ghana, West Africa », Theor. Appl. Climatol., vol. 155, no 4, p. 3033‑3045, avr. 2024, doi: 10.1007/s00704-023-04773-x.
  38. J. Shiri, O. Kisi, H. Yoon, K.-K. Lee, et A. H. Nazemi, « Predicting groundwater level fluctuations with meteorological effect implications—A comparative study among soft computing techniques », Comput. Geosci., vol. 56, p. 32‑44, 2013.
  39. A. T. Ogunrinde, P. G. Oguntunde, J. T. Fasinmirin, et A. S. Akinwumiju, « Application of artificial neural network for forecasting standardized precipitation and evapotranspiration index: A case study of Nigeria », Eng. Rep., vol. 2, no 7, p. e12194, juill. 2020, doi: 10.1002/eng2.12194.
  40. A. Aïzansi, K. Ogunjobi, et F. Ogou, « Monthly rainfall prediction using artificial neural network (case study: Republic of Benin) », Environ. Data Sci., vol. 3, mai 2024, doi: 10.1017/eds.2024.10.
  41. Q. Guo, Z. He, et Z. Wang, « Monthly climate prediction using deep convolutional neural network and long short-term memory », Sci. Rep., vol. 14, no 1, p. 17748, 2024.
  42. Y. Fan, V. Krasnopolsky, H. Van Den Dool, C.-Y. Wu, et J. Gottschalck, « Using artificial neural networks to improve CFS week-3–4 precipitation and 2-m air temperature forecasts », Weather Forecast., vol. 38, no 5, p. 637‑654, 2023.
  43. B. Lim, S. Ö. Arık, N. Loeff, et T. Pfister, « Temporal fusion transformers for interpretable multi-horizon time series forecasting », Int. J. Forecast., vol. 37, no 4, p. 1748‑1764, 2021.
  44. Z. Shen et al., « Current progress in subseasonal-to-decadal prediction based on machine learning », Appl. Comput. Geosci., vol. 24, p. 100201, 2024.
  45. S. Materia et al., « Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives », WIREs Clim. Change, vol. 15, no 6, p. e914, nov. 2024, doi: 10.1002/wcc.914.
  46. F. Kaspar, A. Andersson, M. Ziese, et R. Hollmann, « Contributions to the improvement of climate data availability and quality for Sub-Saharan Africa. Front Clim 3: 815043 ». 2022.
  47. H. Citakoglu, « Comparison of multiple learning artificial intelligence models for estimation of long-term monthly temperatures in Turkey », Arab. J. Geosci., vol. 14, no 20, p. 2131, oct. 2021, doi: 10.1007/s12517-021-08484-3.
  48. N. K. A. Appiah-Badu, Y. M. Missah, L. K. Amekudzi, N. Ussiph, T. Frimpong, et E. Ahene, « Rainfall prediction using machine learning algorithms for the various ecological zones of Ghana », IEEE Access, vol. 10, p. 5069‑5082, 2021.
  49. V. Eyring et al., « Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization », Geosci. Model Dev., vol. 9, no 5, p. 1937‑1958, 2016.
  50. D. Maraun et M. Widmann, Statistical downscaling and bias correction for climate research. Cambridge University Press, 2018.
Index Terms

Computer Science
Information Sciences

Keywords

Climate Prediction Simple Perceptrons Temperature Forecasting Precipitation Modeling Guinea Climate

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