Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal Indonesian Journal of Data and Science The Indonesian Journal of Computer Science
Phiri, Reatlegile
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mathematics

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Medium Range Meteorological Drought Prediction Based on SPEI-3 Using Ensemble Machine Learning and Deep Learning in North West Province, South Africa Phiri, Reatlegile; Bukohwo Michael Esiefarienrhe; Ibidun Christiana Obagbuwa
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.354

Abstract

Meteorological drought monitoring is a pivotal action in everyday humankinds’ activities around the globe. It evaluates atmospheric conditions using weather observation instruments to measure atmospheric variables. Due to the highly sophisticated atmospheric environment, errors in drought monitoring and uncertain observation have been observed. Therefore, this research paper develops a lightweight Machine Learning (ML) and Deep Learning (DL) framework to forecast medium term meteorological drought in North West, South Africa using Standardized Precipitation Evapotranspiration Index at 3 -months (SPEI-3) timescale. This time scale reflects moisture deficits directly impacting agricultural production, early warning decisions and water management. The dataset used in this research study was obtained from South African Weather Services through a formal data request submission and not publicly accessible over a period of 10 years. Furthermore, the dataset consists of 20085 data entries and 11 data columns collected from 10 weather stations. The proposed models include SVR-RF, and, CNN-LSTM-ANN, compared to benchmark models, such as SVR, RF, CNN, LSTM, ANN, CNN-LSTM evaluated using statistical metrics, such as MSE, MAE, and . The results demonstrated irregular drought patterns during the defined period with SPEI-3 values clustered below normal conditions. Similarly, validation results showed that SVR demonstrated strong predictive performance with competitive MSE of 0.28, low MAE of 0.34 and  of 0.86. Although, the proposed CNN-LSTM-ANN and SVR-RF models did not exhibit competitive performance compared to benchmarking models, the result provides valuable comprehension, data collection, distribution, architecture, and computational power
Meteorological Drought Forecast using Deep Learning and Ensemble Machine Learning: A systematic review literature Phiri, Reatlegile; Esiefarienrhe, Bukohwo Michael; Obagbuwa, Ibidun Christiana
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5077

Abstract

Meteorological drought is commonly defined as a prolonged deficiency in precipitation relative to the climatological norm for a given region. However, limitations in robustly quantifying and monitoring drought severity continue to impede decision-making across multiple sectors. Conventional tools, have exhibited substantial limitations in terms of accuracy, spatial–temporal resolution, and generalizability. This paper presents a systematic literature review (SLR) focusing on emerging applications of machine learning (ML) and deep learning (DL) to prediction and monitoring meteorological drought, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. An initial pool of 79 peer-reviewed articles published between 2021 and 2025 were identified. The review process examined the articles based on predefined inclusion and exclusion criteria, 19 studies were ultimately retained for detailed analysis. Quality assessment scores for these studies ranged from 71.4% to 100%. The review highlights the increasing use of hybrid ML and DL models, which combine modeling paradigms, as an effective strategy to improve drought forecasting performance, exhibit strong predictive capabilities and offer a compelling alternative to traditional single-model approaches.
Co-Authors Bukohwo Michael Esiefarienrhe Esiefarienrhe, Bukohwo Michael Ibidun Christiana Obagbuwa Obagbuwa, Ibidun Christiana