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Station-specific thunderstorm forecasting using climate predictors with seasonal decomposition analysis in Bangladesh’s most vulnerable regions
- Truvace record
- TRV-2026-2545raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
- sha256
- 9256b4f13b67…check it
Thunderstorms pose significant threats to life, agriculture, and infrastructure in Bangladesh, particularly in the northeastern regions of Sylhet, Sreemangal, and Mymensingh. This study develops and rigorously evaluates localized thunderstorm frequency forecasting models for these high-risk stations using 40 years (1985-2024) of monthly data and thirty six modeling approaches encompassing classical time series (SARIMA/SARIMAX, ETS/ETSX), machine learning (ANN/ANNX, SVR/SVRX, XGBoost/XGBoostX), and deep learning…
The gain
In a 40-year station-data study, adding five climate variables to seasonally adjusted machine learning models cut monthly thunderstorm forecast error (MASE) to 0.17-0.22 at Sylhet, Sreemangal and Mymensingh, over 80% below a univariate SARIMA benchmark at Sylhet.
What this doesn’t fix
The authors say forecast skill degrades rapidly with lead time, so the 2025-2028 projections illustrate model behaviour rather than reliable long-term predictions, and the projected rise at Sylhet conflicts with its historical decline.
Sources
1 on file- PLOS Onedoi:10.1371/journal.pone.0358795
Cite this record
v1Truvace Impact Record TRV-2026-2545, v1: “Station-specific thunderstorm forecasting using climate predictors with seasonal decomposition analysis in Bangladesh’s most vulnerable regions.” Truvace, 2026-11-12. /record/TRV-2026-2545 (accessed on the day you copy it). sha256 9256b4f13b67b739…
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