Chemosphere, Journal Year: 2024, Volume and Issue: 351, P. 141217 - 141217
Published: Jan. 20, 2024
Language: Английский
Citations
20Exploring forest fire susceptibility and management strategies in Western Himalaya: Integrating ensemble machine learning and explainable AI for accurate prediction and comprehensive analysis
Environmental Technology & Innovation, Journal Year: 2024, Volume and Issue: 35, P. 103655 - 103655
Published: May 5, 2024
Forest fires pose a significant threat to ecosystems and socio-economic activities, necessitating the development of accurate predictive models for effective management mitigation. In this study, we present novel machine learning approach combined with Explainable Artificial Intelligence (XAI) techniques predict forest fire susceptibility in Nainital district. Our innovative methodology integrates several robust — AdaBoost, Gradient Boosting Machine (GBM), XGBoost Random Deep Neural Network (DNN) as meta-model stacking framework. This not only utilises individual strengths these models, but also improves overall prediction performance reliability. By using XAI techniques, particular SHAP (SHapley Additive exPlanations) LIME (Local Interpretable Model-agnostic Explanations), improve interpretability provide insights into decision-making processes. results show effectiveness ensemble model categorising different zones: very low, moderate, high high. particular, identified extensive areas susceptibility, precision, recall F1 values underpinning their effectiveness. These achieved ROC AUC above 0.90, performing exceptionally well an 0.94. The are remarkably inclusion confidence intervals most important metrics all emphasises robustness reliability supports practical use management. Through summary plots, analyze global variable importance, revealing annual rainfall Evapotranspiration (ET) key factors influencing susceptibility. Local analysis consistently highlights importance rainfall, ET, distance from roads across models. study fills research gap by providing comprehensive interpretable modelling that our ability effectively manage risk is consistent environmental protection sustainable goals.
Language: Английский
Citations
18Comprehensive Assessment of E. coli Dynamics in River Water Using Advanced Machine Learning and Explainable AI
Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 106816 - 106816
Published: Jan. 1, 2025
Language: Английский
Citations
1Innovative strategies for pollution assessment in Northern Bangladesh: Mapping pollution areas and tracing metal(loid)s sources in various soil types
PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0311270 - e0311270
Published: Feb. 3, 2025
This study assessed the risks of soil pollution by heavy metals in Chilmari Upazila, northern Bangladesh, using static environmental resilience (Pi) model soil. Geostatistical modeling and self-organizing maps (SOM) identified areas spatial patterns, while a positive matrix factorization (PMF) revealed sources. The results showed that average concentrations Cr, Pb As were well above background levels. Agricultural industrial soils mainly contaminated with according to Nemerow Pollution Index (NPI), Ecological Risk (ER) Pi Index. Over 70% sites co-contamination was particularly high. A one-way ANOVA significant correlations between Pb, Cu Zn levels human activities. PMF analysis effluents, agrochemicals lithogenic sources main contributors contamination 16%, 41% 43%, respectively. SOM three distinct patterns (Pb-Zn, Cr-Cu-Ni Co-Mn-As), which are consistent results. These emphasize need for stringent measures reduce emissions remediate order improve quality food security.
Language: Английский
Citations
1Hydrogeochemical evolution processes, groundwater quality, and non-carcinogenic risk assessment of nitrate-enriched groundwater to human health in different seasons in the Hawler (Erbil) and Bnaslawa Urbans, Iraq
Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(17), P. 26182 - 26203
Published: March 18, 2024
Abstract The main objectives of this research are to assess groundwater, a primary source drinking water in the urban areas Hawler (Erbil) and Bnaslawa northern Iraq, non-carcinogenic human health risks nitrate contamination associated with quality. For purpose, twenty-seven groundwater samples were collected from wells hydrogeochemical characteristics quality for both natural anthropogenic purposes during wet (May 2020) dry (September seasons. During seasons, NO 3 − ranged 14.00 61.00 mg/L 12.00 60.00 mg/L, an average value 35.70 29.00 respectively. Approximately 25.92% exceeded permissible limit WHO (2011) standard. ratios /Na + vs. Cl SO 4 2− indicate effect agricultural activities wastewater leaking cesspools or septic tanks on entropy weighted index method ranked 62.5% 75% as not recommended drinking, remaining moderately suitable risk assessment displayed that 29.6% 25.9% adults, 48% 30% children, 48.1% infants exposed increased concentrations groundwater. Due high water, levels vary infant > child adults. findings obtained study can assist policymakers better understanding properties terms safety, thereby facilitating management resources take necessary measures.
Language: Английский
Citations
8Ecological risk assessment, source identification and spatial distribution of organic contaminants in terms of mucilage threat in streams of Çanakkale Strait Basin (Türkiye)
Chemosphere, Journal Year: 2024, Volume and Issue: 353, P. 141546 - 141546
Published: March 1, 2024
Language: Английский
Citations
7ChatGPT and the future of impact assessment
Environmental Science & Policy, Journal Year: 2024, Volume and Issue: 157, P. 103779 - 103779
Published: May 16, 2024
Language: Английский
Citations
6A decision-making framework for landfill site selection in Saudi Arabia using explainable artificial intelligence and multi-criteria analysis
Environmental Technology & Innovation, Journal Year: 2023, Volume and Issue: 33, P. 103464 - 103464
Published: Dec. 7, 2023
The rapid urbanisation in Abha, Saudi Arabia, especially the mountainous landscape, makes it necessary to identify optimal locations for environmentally friendly building complexes. This study introduces a transparent decision making framework landfill site selection that combines multi-criteria making, fuzzy set theory, GIS and eXplainable Artificial Intelligence (XAI). We focused on complex interplay of geophysical, geoecological socio-economic parameters used analytical hierarchy process (AHP) parameter weighting address challenges urban centre developing country. An index map, called Landfill Site Potential Index (LSPI), was generated, integrating all key indicate suitable zones sites. LSPI classified into different suitability zones, ranging from very high low suitability, using Self-Organising Maps (SOM) combination with k-means clustering. use XAI models, particular an optimised bagging ensemble model, provided crucial insights factors influencing suitability. mean value 0.681 categorised five (7.25%) (20.60%). Within most zone, ten sub-zones were defined prioritised development model showed accuracy, SHAP LIME analyses providing deeper understanding global local determinants not only provides comprehensive decision-making waste management countries, but also improves critical selection.
Language: Английский
Citations
16Interpreting optimised data-driven solution with explainable artificial intelligence (XAI) for water quality assessment for better decision-making in pollution management
Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(30), P. 42948 - 42969
Published: June 17, 2024
Language: Английский
Citations
5Optimizing coastal groundwater quality predictions: A novel data mining framework with cross-validation, bootstrapping, and entropy analysis
Journal of Contaminant Hydrology, Journal Year: 2024, Volume and Issue: 269, P. 104480 - 104480
Published: Dec. 10, 2024
Language: Английский
Citations
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