Analysis and forecasting of meteorological drought using PROPHET and SARIMA models deploying machine learning technique for southwestern region of Bangladesh

The southwestern Bangladesh is predominantly agrarian region, and recurrence of drought impacts directly on crops, reducing farmers' incomes and threatening national food security. This study analyzes and forecasts meteorological droughts for southwestern using Probabilistic Forecasting with Structural Time Series (PROPHET) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. PROPHET excels at capturing long-term, non-linear trends, while SARIMA efficiently models seasonal variations. The essential climatic variables (e.g., temperature, rainfall, soil moisture) from Jashore, Jhenaidah, Kushtia, and Chuadanga were collected from 1994 to 2018 to enhance drought prediction accuracy up to 2050. In both cases, the calculation was performed by deploying the Machine Learning Techniques. Both models showed that drought intensity varies spatiotemporally and that frequent drought occurrences are common in all districts. Almost 50% of the projected years (2019-2050) will be considered drought years (≥6 dry months in a year). 2049 and 2050 are nearly dry years in all districts. Considering the total months and years of drought, Chuadanga is followed by the Jashore, Jhenaidah, and Kushtia districts. Moreover, a strong correlation was found between the predicted and observed drought, whereas the R 2 values were 0.83, 0.75, 0.88, and 0.76 for Jashore, Jhenaidah, Kushtia, and Chuadanga, respectively. Hence, these models would provide robust forecasts, helping to identify increasing drought severity in the region. Model validation using key performance metrics demonstrates their reliability in supporting water resource management. The findings underscore the importance of proactive drought mitigation strategies and suggest that future research should incorporate additional climate variables to improve prediction accuracy. • The southwestern part of Bangladesh is a predominantly drought-prone region. • The PROPHET and SARIMA models are used in forecasting the drought. • The result shows a spatiotemporal variation of drought intensity over the region. • Regarding the drought intensity, Chuadanga is followed by the Jashore, Jhenaidah, and Kushtia.

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Publication Details

Journal
Environmental and Sustainability Indicators
Published
2025-06-19
DOI
https://doi.org/10.1016/j.indic.2025.100761
Citations
3
Primary Topic
Hydrology and Drought Analysis
Type
article
Field-Weighted Citation Impact
3.32
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article

Analysis and forecasting of meteorological drought using PROPHET and SARIMA models deploying machine learning technique for southwestern region of Bangladesh

Shaikh Shamim Hasan, Md. Alamgir Hossain, Bai Ling, Md. Moklesur Rahman et al.
3 citations
Environmental and Sustainability Indicators
Hydrology and Drought Analysis
3.32
article

Analysis and forecasting of meteorological drought using PROPHET and SARIMA models deploying machine learning technique for southwestern region of Bangladesh

Shaikh Shamim Hasan, Md. Alamgir Hossain, Bai Ling, Md. Moklesur Rahman, Azhar Mahmud
article en
3 citations

Abstract

The southwestern Bangladesh is predominantly agrarian region, and recurrence of drought impacts directly on crops, reducing farmers' incomes and threatening national food security. This study analyzes and forecasts meteorological droughts for southwestern using Probabilistic Forecasting with Structural Time Series (PROPHET) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. PROPHET excels at capturing long-term, non-linear trends, while SARIMA efficiently models seasonal variations. The essential climatic variables (e.g., temperature, rainfall, soil moisture) from Jashore, Jhenaidah, Kushtia, and Chuadanga were collected from 1994 to 2018 to enhance drought prediction accuracy up to 2050. In both cases, the calculation was performed by deploying the Machine Learning Techniques. Both models showed that drought intensity varies spatiotemporally and that frequent drought occurrences are common in all districts. Almost 50% of the projected years (2019-2050) will be considered drought years (≥6 dry months in a year). 2049 and 2050 are nearly dry years in all districts. Considering the total months and years of drought, Chuadanga is followed by the Jashore, Jhenaidah, and Kushtia districts. Moreover, a strong correlation was found between the predicted and observed drought, whereas the R 2 values were 0.83, 0.75, 0.88, and 0.76 for Jashore, Jhenaidah, Kushtia, and Chuadanga, respectively. Hence, these models would provide robust forecasts, helping to identify increasing drought severity in the region. Model validation using key performance metrics demonstrates their reliability in supporting water resource management. The findings underscore the importance of proactive drought mitigation strategies and suggest that future research should incorporate additional climate variables to improve prediction accuracy. • The southwestern part of Bangladesh is a predominantly drought-prone region. • The PROPHET and SARIMA models are used in forecasting the drought. • The result shows a spatiotemporal variation of drought intensity over the region. • Regarding the drought intensity, Chuadanga is followed by the Jashore, Jhenaidah, and Kushtia.

Environmental and Sustainability IndicatorsVol. 27
United States Army Corps of Engineers (US), Chinese Academy of Sciences (CN), Institute of Tibetan Plateau Research (CN), Bangladesh Agricultural Research Institute (BD)
Climate action
Openalex Percentile: Top 8%
Hydrology and Drought Analysis
3.32
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