Exploring 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, Год журнала: 2024, Номер 35, С. 103655 - 103655

Опубликована: Май 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.

Язык: Английский

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Surface water quality evaluation of Mahanadi and its Tributary Katha Jodi River, Cuttack District, Odisha, using WQI, PLSR, SRI, and geospatial techniques

Applied Water Science, Год журнала: 2025, Номер 15(2)

Опубликована: Янв. 23, 2025

Язык: Английский

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Comprehensive Assessment of E. coli Dynamics in River Water Using Advanced Machine Learning and Explainable AI

Process Safety and Environmental Protection, Год журнала: 2025, Номер unknown, С. 106816 - 106816

Опубликована: Янв. 1, 2025

Язык: Английский

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Evaluating Cooling Effect of Blue-green Infrastructure on Urban Thermal Environment in a Metropolitan City: Using Geospatial and Machine Learning Techniques

Sustainable Cities and Society, Год журнала: 2024, Номер 113, С. 105666 - 105666

Опубликована: Июль 14, 2024

Язык: Английский

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Artificial Intelligence in Hydrology: Advancements in Soil, Water Resource Management, and Sustainable Development

Sustainability, Год журнала: 2025, Номер 17(5), С. 2250 - 2250

Опубликована: Март 5, 2025

Hydrology relates to many complex challenges due climate variability, limited resources, and especially, increased demands on sustainable management of water soil. Conventional approaches often cannot respond the integrated complexity continuous change inherent in system; hence, researchers have explored advanced data-driven solutions. This review paper revisits how artificial intelligence (AI) is dramatically changing most important facets hydrological research, including soil land surface modeling, streamflow, groundwater forecasting, quality assessment, remote sensing applications resources. In AI techniques could further enhance accuracy texture analysis, moisture estimation, erosion prediction for better management. Advanced models also be used as a tool forecast streamflow levels, therefore providing valuable lead times flood preparedness resource planning transboundary basins. quality, AI-driven methods improve contamination risk enable detection anomalies, track pollutants assist treatment processes regulatory practices. combined with open new perspectives monitoring resources at spatial scale, from forecasting storage variations. paper’s synthesis emphasizes AI’s immense potential hydrology; it covers latest advances future prospects field ensure

Язык: Английский

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Groundwater quality prediction and risk assessment in Kerala, India: A machine-learning approach

Journal of Environmental Management, Год журнала: 2024, Номер 370, С. 122616 - 122616

Опубликована: Сен. 25, 2024

Язык: Английский

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Interpreting 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, Год журнала: 2024, Номер 31(30), С. 42948 - 42969

Опубликована: Июнь 17, 2024

Язык: Английский

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Quantifying seasonal variations in pollution sources with machine learning-enhanced positive matrix factorization

Ecological Indicators, Год журнала: 2024, Номер 166, С. 112543 - 112543

Опубликована: Авг. 29, 2024

Язык: Английский

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Assessing the impact of rainfall, topography, and human disturbances on nutrient levels using integrated machine learning and GAMs models in the Choctawhatchee River Watershed

Journal of Environmental Management, Год журнала: 2025, Номер 375, С. 124361 - 124361

Опубликована: Янв. 31, 2025

Язык: Английский

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Enhancing Evacuation Shelter Suitability in Compound Hazard-Prone Regions with a Bayesian Optimized Convolutional Neural Network Approach

International Journal of Disaster Risk Reduction, Год журнала: 2025, Номер unknown, С. 105306 - 105306

Опубликована: Фев. 1, 2025

Язык: Английский

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