Explainable artificial intelligence in disaster risk management: Achievements and prospective futures

International Journal of Disaster Risk Reduction, Journal Year: 2023, Volume and Issue: 98, P. 104123 - 104123

Published: Nov. 1, 2023

Disasters can have devastating impacts on communities and economies, underscoring the urgent need for effective strategic disaster risk management (DRM). Although Artificial Intelligence (AI) holds potential to enhance DRM through improved decision-making processes, its inherent complexity "black box" nature led a growing demand Explainable AI (XAI) techniques. These techniques facilitate interpretation understanding of decisions made by models, promoting transparency trust. However, current state XAI applications in DRM, their achievements, challenges they face remain underexplored. In this systematic literature review, we delve into burgeoning domain XAI-DRM, extracting 195 publications from Scopus ISI Web Knowledge databases, selecting 68 detailed analysis based predefined exclusion criteria. Our study addresses pertinent research questions, identifies various hazard types, components, methods, uncovers limitations these approaches, provides synthesized insights explainability effectiveness decision-making. Notably, observed significant increase use 2022 2023, emphasizing interpretability. Through rigorous methodology, offer key directions that serve as guide future studies. recommendations highlight importance multi-hazard analysis, integration early warning systems digital twins, incorporation causal inference methods strategy planning effectiveness. This serves beacon researchers practitioners alike, illuminating intricate interplay between revealing profound solutions revolutionizing management.

Language: Английский

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Influences of wildfire on the forest ecosystem and climate change: A comprehensive study

Environmental Research, Journal Year: 2023, Volume and Issue: 240, P. 117537 - 117537

Published: Oct. 30, 2023

Language: Английский

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Comparison of diverse machine learning algorithms for forest fire susceptibility mapping in Antalya, Türkiye

Advances in Space Research, Journal Year: 2024, Volume and Issue: 74(2), P. 647 - 667

Published: April 16, 2024

Language: Английский

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Delineation of urban growth boundary based on FLUS model under the perspective of land use evaluation in hilly mountainous areas

Journal of Mountain Science, Journal Year: 2024, Volume and Issue: 21(5), P. 1647 - 1662

Published: May 1, 2024

Language: Английский

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Enhancing the Performance of Machine Learning and Deep Learning-Based Flood Susceptibility Models by Integrating Grey Wolf Optimizer (GWO) Algorithm

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(14), P. 2595 - 2595

Published: July 16, 2024

Flooding is a recurrent hazard occurring worldwide, resulting in severe losses. The preparation of flood susceptibility map non-structural approach to management before its occurrence. With recent advances artificial intelligence, achieving high-accuracy model for mapping (FSM) challenging. Therefore, this study, various intelligence approaches have been utilized achieve optimal accuracy modeling address challenge. By incorporating the grey wolf optimizer (GWO) metaheuristic algorithm into models—including neural networks (RNNs), support vector regression (SVR), and extreme gradient boosting (XGBoost)—the objective generate maps evaluate variation performance. tropical Manimala River Basin India, severely battered by flooding past, has selected as test site. This 15 conditioning factors such aspect, enhanced built-up bareness index (EBBI), slope, elevation, geomorphology, normalized difference water (NDWI), plan curvature, profile soil adjusted vegetation (SAVI), stream density, texture, power (SPI), terrain ruggedness (TRI), land use/land cover (LULC) topographic wetness (TWI). Thus, six are produced applying RNN, SVR, XGBoost, RNN-GWO, SVR-GWO, XGBoost-GWO models. All models exhibited outstanding (AUC above 0.90) performance, performance ranks following order: RNN-GWO (AUC: 0.968) > 0.961) SVR-GWO 0.960) RNN 0.956) XGBoost 0.953) SVR 0.948). It was discovered that hybrid GWO optimization improved three RNN-GWO-based shows 8.05% MRB very susceptible floods. found SPI, LULC, TWI top five influential factors.

Language: Английский

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SHAP-Driven Explainable Artificial Intelligence Framework for Wildfire Susceptibility Mapping Using MODIS Active Fire Pixels: An In-Depth Interpretation of Contributing Factors in Izmir, Türkiye

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(15), P. 2842 - 2842

Published: Aug. 2, 2024

Wildfire susceptibility maps play a crucial role in preemptively identifying regions at risk of future fires and informing decisions related to wildfire management, thereby aiding mitigating the risks potential damage posed by wildfires. This study employs eXplainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP), map Izmir Province, Türkiye. Incorporating fifteen conditioning factors spanning topography, climate, anthropogenic influences, vegetation characteristics, machine learning (ML) models (Random Forest, XGBoost, LightGBM) were used predict wildfire-prone areas using freely available active fire pixel data (MODIS Active Fire Collection 6 MCD14ML product). The evaluation trained ML showed that Random Forest (RF) model outperformed XGBoost LightGBM, achieving highest test accuracy (95.6%). All classifiers demonstrated strong predictive performance, but RF excelled sensitivity, specificity, precision, F-1 score, making it preferred for generating conducting SHAP analysis. Unlike prevailing approaches focusing solely on global feature importance, this fills critical gap employing summary dependence plots comprehensively assess each factor’s contribution, enhancing explainability reliability results. analysis reveals clear associations between such as wind speed, temperature, NDVI, slope, distance villages with increased susceptibility, while rainfall streams exhibit nuanced effects. spatial distribution classes highlights areas, flat coastal near settlements agricultural lands, emphasizing need enhanced awareness preventive measures. These insights inform targeted management strategies, highlighting importance tailored interventions like firebreaks management. However, challenges remain, including ensuring selected factors’ adequacy across diverse regions, addressing biases from resampling spatially varied data, refining broader applicability.

Language: Английский

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Comparative Analysis of Machine Learning-Based Predictive Models for Fine Dead Fuel Moisture of Subtropical Forest in China

Forests, Journal Year: 2024, Volume and Issue: 15(5), P. 736 - 736

Published: April 23, 2024

The moisture content of fine dead surface fuel in forests is a crucial metric for assessing its combustibility and plays pivotal role the early warning, occurrence, spread forest fires. Accurate prediction on critical challenge fire management. Previous research has been mainly focused coniferous cold temperate zones, but there less attention given to understanding dynamics subtropical forests, which limits development regional warning models. Here, we consider coupled influence multiple meteorological, terrain, stand, other characteristic factors within evergreen broadleaved region southern China. ability five machine learning algorithms predict assessed, key affecting model accuracy are identified. Results show that when single meteorological factor used as forecasting model, than combined with factors. However, improved after addition stand terrain best combination all feature including meteorology, terrain. overall ordered follows: random > extreme gradient boosting support vector stepwise linear regression k-nearest neighbor. Canopy density factors, slope position altitude average relative air humidity light intensity previous 15 days content. Our results provide scientific guidance variability warnings.

Language: Английский

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Research on the distribution and influencing factors of fine mode aerosol optical depth (AODf) in China

Atmospheric Environment, Journal Year: 2024, Volume and Issue: 334, P. 120721 - 120721

Published: Oct. 1, 2024

Language: Английский

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Assessing Landslide Susceptibility Using Machine Learning and Remote Sensing Data: A Case Study of Southeastern Constantine, Algeria

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 143 - 155

Published: Jan. 1, 2025

Language: Английский

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Assessment of forest fire vulnerability prediction in Indonesia: Seasonal variability analysis using machine learning techniques

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 138, P. 104435 - 104435

Published: Feb. 28, 2025

Language: Английский

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