A systematic review of trustworthy artificial intelligence applications in natural disasters

Computers & Electrical Engineering, Год журнала: 2024, Номер 118, С. 109409 - 109409

Опубликована: Июнь 29, 2024

Artificial intelligence (AI) holds significant promise for advancing natural disaster management through the use of predictive models that analyze extensive datasets, identify patterns, and forecast potential disasters. These facilitate proactive measures such as early warning systems (EWSs), evacuation planning, resource allocation, addressing substantial challenges associated with This study offers a comprehensive exploration trustworthy AI applications in disasters, encompassing management, risk assessment, prediction. research is underpinned by an review reputable sources, including Science Direct (SD), Scopus, IEEE Xplore (IEEE), Web (WoS). Three queries were formulated to retrieve 981 papers from earliest documented scientific production until February 2024. After meticulous screening, deduplication, application inclusion exclusion criteria, 108 studies included quantitative synthesis. provides specific taxonomy disasters explores motivations, challenges, recommendations, limitations recent advancements. It also overview techniques developments using explainable artificial (XAI), data fusion, mining, machine learning (ML), deep (DL), fuzzy logic, multicriteria decision-making (MCDM). systematic contribution addresses seven open issues critical solutions essential insights, laying groundwork various future works trustworthiness AI-based management. Despite benefits, persist In these contexts, this identifies several unused used areas disaster-based theory, collects ML, DL techniques, valuable XAI approach unravel complex relationships dynamics involved utilization fusion processes related Finally, extensively analyzed ethical considerations, bias, consequences AI.

Язык: Английский

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A Parallel-Cascaded Ensemble of Machine Learning Models for Crop Type Classification in Google Earth Engine Using Multi-Temporal Sentinel-1/2 and Landsat-8/9 Remote Sensing Data

Remote Sensing, Год журнала: 2023, Номер 16(1), С. 127 - 127

Опубликована: Дек. 28, 2023

The accurate mapping of crop types is crucial for ensuring food security. Remote Sensing (RS) satellite data have emerged as a promising tool in this field, offering broad spatial coverage and high temporal frequency. However, there still growing need type classification methods using RS due to the intra- inter-class variability crops. In vein, current study proposed novel Parallel-Cascaded ensemble structure (Pa-PCA-Ca) with seven target classes Google Earth Engine (GEE). Pa section consisted five parallel branches, each generating Probability Maps (PMs) different multi-temporal Sentinel-1/2 Landsat-8/9 images, along Machine Learning (ML) models. PMs exhibited correlation within class, necessitating use most relevant information reduce input dimensionality Ca part. Thereby, Principal Component Analysis (PCA) was employed extract top uncorrelated components. These components were then utilized structure, final performed another ML model referred Meta-model. Pa-PCA-Ca evaluated in-situ collected from extensive field surveys northwest part Iran. results demonstrated superior performance achieving an Overall Accuracy (OA) 96.25% Kappa coefficient 0.955. incorporation PCA led OA improvement over 6%. Furthermore, significantly outperformed conventional approaches, which simply stack sources feed them single model, resulting 10% increase OA.

Язык: Английский

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Study of the Preparation Phase of Turkey’s Powerful Earthquake (6 February 2023) by a Geophysical Multi-Parametric Fuzzy Inference System

Remote Sensing, Год журнала: 2023, Номер 15(9), С. 2224 - 2224

Опубликована: Апрель 22, 2023

On 6 February 2023, a powerful earthquake at the border between Turkey and Syria caused catastrophic consequences was, unfortunately, one of deadliest earthquakes recent decades. The moment magnitude was estimated to be 7.8, it localized in Kahramanmaraş region Turkey. This article aims investigate behavior more than 50 different lithosphere–atmosphere–ionosphere (LAI) anomalies obtained from satellite data services time period about six months before discuss possibility predicting mentioned by an early warning system based on various geophysical parameters. In this study, 52 series covering were acquired with: (i) three identical satellites Swarm constellation (Alpha (A), Bravo (B) Charlie (C); analyzed parameters: electron density (Ne) temperature (Te), magnetic field scalar (F) vector (X, Y Z) components); (ii) Google Earth Engine (GEE) platform service (including ozone, water vapor surface temperature), (iii) Giovanni aerosol optical depth (AOD), methane, carbon monoxide ozone); (iv) USGS catalogue daily seismic rate maximum for each day), around location event 1 September 2022 17 these analyzed. results show that number increased since 33 days reached peak, i.e., highest number, day before. findings implementing proposed predictor Mamdani fuzzy inference (FIS) emphasize occurrence could predicted nine due clear increase seismo-LAI anomalies. However, study has still conducted posteriori, knowing earthquake’s epicenter magnitude. Therefore, similar research, we urgency creation systems seismic-prone areas investigating services, such as GEE, other global platforms Swarm. Finally, path toward prediction is long, goal far, but present support idea challenging achieved future.

Язык: Английский

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Oil spills detection from SAR Earth observations based on a hybrid CNN transformer networks

Marine Pollution Bulletin, Год журнала: 2023, Номер 190, С. 114834 - 114834

Опубликована: Март 17, 2023

Язык: Английский

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Urban flood risk assessment using Sentinel-1 on the google earth engine: A case study in Thai Nguyen city, Vietnam

Remote Sensing Applications Society and Environment, Год журнала: 2023, Номер 31, С. 100987 - 100987

Опубликована: Май 6, 2023

Язык: Английский

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Multi-hazard could exacerbate in coastal Bangladesh in the context of climate change

Journal of Cleaner Production, Год журнала: 2024, Номер 457, С. 142289 - 142289

Опубликована: Апрель 23, 2024

Язык: Английский

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Developing a Fuzzy Inference System Based on Multi-Sensor Data to Predict Powerful Earthquake Parameters

Remote Sensing, Год журнала: 2022, Номер 14(13), С. 3203 - 3203

Опубликована: Июль 4, 2022

Predicting the parameters of upcoming earthquakes has always been one most challenging topics in studies related to earthquake precursors. Increasing number sensors and satellites consequently incrementing observable possible precursors different layers lithosphere, atmosphere, ionosphere Earth opened possibility using data fusion methods estimate predict with low uncertainty. In this study, a Mamdani fuzzy inference system (FIS) was proposed implemented five case studies. particular, magnitude Ecuador (16 April 2016), Iran (12 November 2017), Papua New Guinea (14 May 2019), Japan (13 February 2021), Haiti August 2021) were estimated by FIS. The results showed that cases, highest anomalies usually observed period about month before predicted these periods slightly from actual value. Therefore, based on it could be concluded if significant are time series precursors, is likely an FIS within Dobrovolsky area studied location will happen during next month.

Язык: Английский

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Integrating Google Earth Engine and regional ecological corridor modeling for remote sensing-based urban heat island mitigation in Java, Indonesia

Remote Sensing Applications Society and Environment, Год журнала: 2025, Номер unknown, С. 101573 - 101573

Опубликована: Апрель 1, 2025

Язык: Английский

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A Novel Approach for Multi-cluster-Based River Flood Early Warning System Using Fuzzy-Logic-Based Learning and Rule Optimization

Опубликована: Янв. 1, 2025

Язык: Английский

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A COMPARATIVE STUDY OF MACHINE LEARNING CLASSIFIERS FOR CROP TYPE MAPPING USING VEGETATION INDICES

ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, Год журнала: 2023, Номер X-4/W1-2022, С. 79 - 85

Опубликована: Янв. 13, 2023

Abstract. Timely and accurate mapping of crops is crucial for agriculture management, policy-making, food security. Due to the differences in product calendars various crops, it possible classify them by investigating remote sensing Vegetation Indices (VIs) during crop growth season. This study developed a VI-based approach specifying types based on phenological spectral metrics derived from sentinel-2 images. We used six VIs (ARVI, CVI, EVI, LAI, GLI, NDVI) three supervised machine learning methods, including Random Forest (RF), GBoost (GB), K-Nearest Neighborhood (KNN) mapping. Field data consisting wheat, barley, canola, vegetables, bare land class, were collected as testing training set. The classification results evaluated through test samples showing high overall accuracy (OA) satisfactory class accuracies most dominant across different fields despite variability planting harvesting dates. Among utilized mapping, Atmospherically Resistant Index (ARVI) all methods achieved better results. RF, GB, KNN models with ARVI index was 95%, 88%, 90%, respectively.

Язык: Английский

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