A scalable Earth observation-based machine learning framework for high-resolution mapping and uncertainty assessment of climate-sensitive child health vulnerability in Bangladesh.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	A scalable Earth observation-based machine learning framework for high-resolution mapping and uncertainty assessment of climate-sensitive child health vulnerability in Bangladesh.
Συγγραφείς:	Moniruzzaman M; Department of Geography and Environmental Studies, University of Rajshahi, Bangladesh. Electronic address: mzges@ru.ac.bd.
Πηγή:	The Science of the total environment [Sci Total Environ] 2026 Aug 01; Vol. 1042, pp. 181919. Date of Electronic Publication: 2026 Jun 01.
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0330500 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1026 (Electronic) Linking ISSN: 00489697 NLM ISO Abbreviation: Sci Total Environ Subsets: MEDLINE
Imprint Name(s):	Original Publication: Amsterdam, Elsevier.
Ιατρικοί όροι (MeSH):	Child Health/statistics & numerical data , Boosting Machine Learning Algorithms , Climate Change*, Environmental Monitoring/methods ; Humans ; Bangladesh ; Climate ; Predictive Learning Models ; Random Forest ; Uncertainty
Περίληψη:	Bangladesh remains highly vulnerable to climate-induced hazards that disproportionately affect child health, yet spatially explicit assessments of these risks remain limited. This study develops a scalable Earth observation-driven machine learning framework to map climate-sensitive child health vulnerability across Bangladesh at 1-km spatial resolution. Anthropometric and health indicators from the 2022 Bangladesh Demographic and Health Survey (DHS) were integrated with multi-sensor satellite-derived environmental variables to generate high-resolution vulnerability surfaces and quantify prediction uncertainty. Georeferenced data from 674 DHS clusters (n = 8784 children) were combined with environmental covariates, including Land Surface Temperature (MODIS), precipitation (CHIRPS), vegetation indices (NDVI and EVI), and population density. Three supervised learning algorithms-Random Forest, Gradient Boosting Machine, and XGBoost-were trained using 80% of the dataset, with hyperparameters optimized through 5-fold cross-validation. The ensemble framework achieved the strongest predictive performance (R² = 0.683; RMSE = 7.65), outperforming individual models by 3.8%. Thermal stress, rainfall variability, and ecological productivity emerged as the dominant environmental determinants of vulnerability, collectively explaining 62% of model variance. The resulting maps revealed pronounced spatial disparities, with Rangpur exhibiting the highest vulnerability levels (67.3), while comparatively lower scores were observed in parts of Barishal and Sylhet (39.1). Bootstrap-based uncertainty analysis using 1000 iterations produced a mean uncertainty index of 4.8 ± 1.2, with elevated uncertainty concentrated in topographically complex regions. Hotspot analysis identified vulnerable clusters encompassing approximately 6.3 million children under five. The proposed framework provides a transferable approach for precision-oriented climate-health surveillance in data-constrained regions. (Copyright © 2026 Elsevier B.V. All rights reserved.)
Competing Interests:	Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing:	Keywords: Bangladesh; Child health vulnerability; Climate change impacts; Earth Observation; Ensemble machine learning; High-resolution mapping
Entry Date(s):	Date Created: 20260601 Date Completed: 20260620 Latest Revision: 20260625
Update Code:	20260625
DOI:	10.1016/j.scitotenv.2026.181919
PMID:	42224872
Βάση Δεδομένων:	MEDLINE

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Περιγραφή
ISSN:	1879-1026
DOI:	10.1016/j.scitotenv.2026.181919