Machine learning-based weather forecasting models are of paramount importance for almost all sectors of human activity. However, incorrect weather forecasts can have serious consequences on a global scale. The Adamawa region of Cameroon has suffered the consequences of erroneous forecasts, resulting in enormous losses and material damage. Missing or abnormal values are one of the problems that can contribute to the inaccuracy of weather forecasting models. Our objective is to provide a systematic view of how anomalies can affect the results of weather forecasting models and to compare imputation methods such as LinearRegressor, BayesianRidge, ExtraTreeRegressor (ETR), KNeighborsRegressor (KNR), DecisionTreeRegressor, KNNImputer, and MICE. The performance criteria used to evaluate these methods are RMSE and execution time. For the dataset, we generated four datasets with a specific anomaly rate. The abnormal values were transformed into missing values. Next, all the missing values were imputed using these different imputation methods, and then compared. Finally, the cleaned data was used by Machine learning-based forecasting models to generate forecast data. The results show that the imputation of anomalies in weather data is done in a reduced time, allowing for good quality weather data while improving the accuracy of weather forecasting models.
References
[1]
Morales-Hernández, A., Nápoles, G., Jastrzebska, A., Salgueiro, Y. and Vanhoof, K. (2022) Online Learning of Windmill Time Series Using Long Short-Term Cognitive Networks. ExpertSystemswithApplications, 205, Article ID: 117721. https://doi.org/10.1016/j.eswa.2022.117721
[2]
OMM (2021) Atlas de la mortalité et des pertes économiques dues à des phénomènes météorologiques, climatiques et hydrologiques extrêmes (1970-2019). https://www.uncclearn.org/wp-content/uploads/library/1267_Atlas_of_Mortality_FR.pdf
[3]
AbdulRaheem, M., Awotunde, J.B., Adeniyi, A.E., Oladipo, I.D. and Adekola, S.O. (2022) Weather Prediction Performance Evaluation on Selected Machine Learning Algorithms. IAESInternationalJournalofArtificialIntelligence (IJ-AI), 11, 1535-1544. https://doi.org/10.11591/ijai.v11.i4.pp1535-1544
[4]
Fente, D.N. and Kumar Singh, D. (2018) Weather Forecasting Using Artificial Neural Network. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, 20-21 April 2018, 1757-1761. https://doi.org/10.1109/icicct.2018.8473167
[5]
Jain, G. and Mallick, B. (2016) A Review on Weather Forecasting Techniques. IJARCCE, 5, 177-180. https://doi.org/10.17148/ijarcce.2016.51237
[6]
World Bank (2015) Creating an Atmosphere of Cooperation in Sub-Saharan Africa by Strengthening Weather, Climate and Hydrological Services. https://www.worldbank.org/en/news/speech/2015/06/02/transforming-weather-climate-and-hydrological-services-in-africa
[7]
Lee, M., Moon, S., Kim, Y. and Moon, B. (2014) Correcting Abnormalities in Meteorological Data by Machine Learning. 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), San Diego, 5-8 October 2014, 888-893. https://doi.org/10.1109/smc.2014.6974024
[8]
Planchon, V. (2005) Traitement des valeurs aberrantes: Concepts actuels et tend-ances générales. Biotechnology, Agronomy, Society and Environment, 9, 19-34.
[9]
Salvador, G., Luengo, J. and Herrera, F. (2015) Data Preprocessing in Data Mining. Springer. https://doi.org/10.1007/978-3-319-10247-4
[10]
Vaumi, J.P.T. (2019) Système d’analyse de données pour la prévision des inondations dans les pays en voie de développement. PhD. Thesis, University of Ngaoundéré.
[11]
Strigaro, D., Cannata, M. and Antonovic, M. (2019) Boosting a Weather Monitoring System in Low Income Economies Using Open and Non-Conventional Systems: Data Quality Analysis. Sensors, 19, Article 1185. https://doi.org/10.3390/s19051185
[12]
ONACC (2019) Etat des lieux du réseau d’observation météorologique dans la Région de l’Adamaoua. https://files.aho.afro.who.int/afahobckpcontainer/production/files/Profil_ONACC_ADAMAOUA_.pdf
[13]
Dadhich, S., Pathak, V., Mittal, R. and Doshi, R. (2021) Chapter 10 Machine Learning for Weather Forecasting. In: Hiran, K.K., Khazanchi, D., Vyas, A.K. and Padmanaban, S., Eds., MachineLearningforSustainableDevelopment, De Gruyter, 161-174. https://doi.org/10.1515/9783110702514-010
[14]
Ajina, A., K G, J.C., Bhat, D.N. and Saxena, K. (2023) Prediction of Weather Forecasting Using Artificial Neural Networks. JournalofAppliedResearchandTechnology, 21, 205-211. https://doi.org/10.22201/icat.24486736e.2023.21.2.1698
Zhang, M., Guo, J., Li, X. and Jin, R. (2020) Data-Driven Anomaly Detection Approach for Time-Series Streaming Data. Sensors, 20, Article 5646. https://doi.org/10.3390/s20195646
[17]
Heinrigs, P. (2020) Incidences sécuritaires du changement climatique au Sahel: Perspectives politiques. https://www.wathi.org/incidences-securitaires-du-changement-climatique-au-sahel-perspectives-politiques/
[18]
Bridge-Nduwimana, C.B., Ouaazizi, A.E. and Benyakhlef, M. (2025) A New Data Imputation Technique for Efficient Used Car Price Forecasting. InternationalJournalofElectricalandComputerEngineering (IJECE), 15, 2364-2371. https://doi.org/10.11591/ijece.v15i2.pp2364-2371
[19]
Amit, K. (2023) Machine Learning Based Solution for Asymmetric Information in Prediction of Used Car Prices. International Conference on Intelligent Vision and Computing, 409-420. https://www.researchgate.net/publication/370423022_Machine_Learning_Based_Solution_for_Asymmetric_Information_in_Prediction_of_Used_Car_Prices
[20]
Arefin, M.N. and Masum, A.K.M. (2024) A Probabilistic Approach for Missing Data Imputation. Complexity, 2024, Article ID: 4737963. https://doi.org/10.1155/2024/4737963
[21]
Li, J., Guo, S., Ma, R., He, J., Zhang, X., Rui, D., et al. (2024) Comparison of the Effects of Imputation Methods for Missing Data in Predictive Modelling of Cohort Study Datasets. BMCMedicalResearchMethodology, 24, Article No. 41. https://doi.org/10.1186/s12874-024-02173-x
[22]
Ponce-Jara, M.A., Velásquez-Figueroa, C., Tonato-Peralta, D. and Paredes-Morillo, G. (2020) Diseño de una estación meteorológica automática para registrar las variables solar y eólica. RevistaArbitradaInterdisciplinariaKoinonía, 5, 937-957. https://doi.org/10.35381/r.k.v5i2.1072
[23]
Kulkarni, A. and Mukhopadhyay, D. (2018) Internet of Things Based Weather Forecast Monitoring System. IndonesianJournalofElectricalEngineeringandComputerScience, 9, 555-557. https://doi.org/10.11591/ijeecs.v9.i3.pp555-557
[24]
NiuBoL (2025) Meteorological Station Equipment. https://www.niubol.com/All-products/meteorological-station-equipment.html
[25]
Larraondo, P.R. (2018) Application of Machine Learning Techniques to Weather Forecasting. Ph.D. Thesis, University of the Basque Country UPV/EHU. https://addi.ehu.es/bitstream/handle/10810/32532/TESIS_ROZAS_LARRAONDO_PABLO.pdf
[26]
Aditya, T. (2023) The Role of Data Processing in Machine Learning. https://niveussolutions.com/role-of-data-processing-in-machine-learning/
[27]
Zemicheal, T. and Dietterich, T.G. (2019) Anomaly Detection in the Presence of Missing Values for Weather Data Quality Control. Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies, Accra, 3-5 July 2019, 65-73. https://doi.org/10.1145/3314344.3332490
[28]
Kaya, Ş.M., İşler, B., Abu-Mahfouz, A.M., Rasheed, J. and AlShammari, A. (2023) An Intelligent Anomaly Detection Approach for Accurate and Reliable Weather Forecasting at IoT Edges: A Case Study. Sensors, 23, Article 2426. https://doi.org/10.3390/s23052426
[29]
Mestre, G., Ruano, A., Duarte, H., Silva, S., Khosravani, H., Pesteh, S., et al. (2015) An Intelligent Weather Station. Sensors, 15, 31005-31022. https://doi.org/10.3390/s151229841
[30]
Kothapalli, S. and Totad, S.G. (2017) A Real-Time Weather Forecasting and Analysis. 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai, 21-22 September 2017, 1567-1570. https://doi.org/10.1109/icpcsi.2017.8391974
[31]
Parashar, A. (2019) IoT Based Automated Weather Report Generation and Prediction Using Machine Learning. 2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT), Jaipur, 28-29 September 2019, 339-344. https://doi.org/10.1109/icct46177.2019.8968782
[32]
Hewage, P., Behera, A., Trovati, M., Pereira, E., Ghahremani, M., Palmieri, F., et al. (2020) Temporal Convolutional Neural (TCN) Network for an Effective Weather Forecasting Using Time-Series Data from the Local Weather Station. SoftComputing, 24, 16453-16482. https://doi.org/10.1007/s00500-020-04954-0
[33]
Sobhani, M., Hong, T. and Martin, C. (2020) Temperature Anomaly Detection for Electric Load Forecasting. InternationalJournalofForecasting, 36, 324-333. https://doi.org/10.1016/j.ijforecast.2019.04.022
[34]
Bâra, A., Văduva, A.G. and Oprea, S. (2024) Anomaly Detection in Weather Phenomena: News and Numerical Data-Driven Insights into the Climate Change in Romania’s Historical Regions. InternationalJournalofComputationalIntelligenceSystems, 17, Article No. 134. https://doi.org/10.1007/s44196-024-00536-2
[35]
Little, R.J. and Rubin, D.B. (2019) Analyse statistique avec données manquantes. 3rd Edition, Wiley.
[36]
Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Mphago, B. and Tabona, O. (2021) A Survey on Missing Data in Machine Learning. JournalofBigData, 8, Article No. 140. https://doi.org/10.1186/s40537-021-00516-9
[37]
Bruyant, A., Guemann, M. and Malgoyre, A. (2023) Étude épidémiologique des amputations majeures des membres supérieur et inférieur en France. Kinésithérapie, laRevue, 23, 3-12. https://doi.org/10.1016/j.kine.2022.05.014
[38]
MINEPAT (2018) Elaboration du schema regional d’amenagement et de developpement durable du territoire de l’adamaoua: Atlas cartographique de l’adamaoua. Le groupement AGORA Consulting, CID, MINEPAT: Cameroon, 1-45. https://minepat.gov.cm/wp-content/uploads/2024/09/1.1.-SRADDT-AD-Diagnostic-territorial-Atlas-cartographique.pdf
[39]
Sadio, F.H. (2012) Le tourisme dans le departement de la vina (Adamaoua-Cameroun): Mythe ou Réalité? Ph.D. Thesis, Université de Ngaoundéré, Ngaoundéré. https://www.memoireonline.com/05/20/11825/m_Le-tourisme-dans-le-departement-de-la-vina-adamaoua-cameroun--mythe-ou-realite-9.html
[40]
Michel, T. (2003) Paysage géomorphologique, patrimoine socio-culturel et développement sur les hautes terres de l’Adamaoua au Cameroun. Espacestropicaux, 18, 67-75. https://www.persee.fr/doc/etrop\_1147-3991\_2003\_act\_18\_9\_1108
[41]
Cottrell, G., Cot, M. and Mary, J. (2009) L’imputation multiple des données manquantes aléatoirement: Concepts généraux et présentation d’une méthode Monte-Carlo. Revued’ÉpidémiologieetdeSantéPublique, 57, 361-372. https://doi.org/10.1016/j.respe.2009.04.011
Thomas, B.B. (2024) From Batch to Online Machine Learning. Springer. https://doi.org/10.1007/978-981-99-7007-0
[44]
Farchi, A., Bocquet, M., Laloyaux, P., Bonavita, M. and Malartic, Q. (2021) A Comparison of Combined Data Assimilation and Machine Learning Methods for Offline and Online Model Error Correction. JournalofComputationalScience, 55, Article ID: 101468. https://doi.org/10.1016/j.jocs.2021.101468
