A systematic review of trustworthy artificial intelligence applications in natural disasters

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 118, P. 109409 - 109409

Published: June 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.

Language: Английский

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Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(30), P. 42719 - 42749

Published: June 15, 2024

Accurately predicting potential evapotranspiration (PET) is crucial in water resource management, agricultural planning, and climate change studies. This research aims to investigate the performance of two machine learning methods, adaptive network-based fuzzy inference system (ANFIS) deep belief network (DBN), forecasting PET, as well explore hybridizing ANFIS approach with Snake Optimizer (ANFIS-SO) algorithm. The study utilized a comprehensive dataset spanning period from 1983 2020. ANFIS, ANFIS-SO, DBN models were developed, their performances evaluated using statistical metrics, including R

Language: Английский

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S-Transformer: a new deep learning model enhanced by sequential transformer encoders for drought forecasting

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: March 25, 2025

Language: Английский

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Integration of the reptile search algorithm and the adaptive neuro-fuzzy inference system enhances standardized precipitation evapotranspiration index forecasting

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 26, 2025

A novel metaheuristic algorithm called the reptile search (RSA) was introduced in conjunction with artificial neural fuzzy inference system (ANFIS) for estimation of standardized precipitation evapotranspiration index (SPEI). The model tested three different climates: arid and super-cold, semi-arid cold, moderate climate across Iran by combining meteorological indices (minimum temperature, maximum average precipitation, potential evapotranspiration) large-scale signals (North Atlantic Oscillation, Arctic Pacific Decadal Southern Oscillation Index). results ANFIS + RSA were compared those WOA GWO models evaluation. Based on error evaluation criteria, performance is considered appropriate, showing a higher relative accuracy to ANFIS, GWO, WOA. In climates, exhibited highest prediction accuracy, RMSE = 0.28, MAE 0.20, CA 0.19, NASH 0.91. cold model's slightly lower, 0.33, 0.23, 0.85. super-cold remained relatively consistent, 0.24, 0.18, 0.84. Furthermore, promising hybrid can be further evaluated other regions climates assess its overall effectiveness.

Language: Английский

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Beyond Traditional Metrics: Exploring the Potential of Hybrid Algorithms for Drought Characterization and Prediction in the Tromso Region, Norway

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(17), P. 7813 - 7813

Published: Sept. 3, 2024

Meteorological drought, defined as a decrease in the average amount of precipitation, is among most insidious natural disasters. Not knowing when drought will occur (its onset) makes it difficult to predict and monitor it. Scientists face significant challenges accurately predicting monitoring global droughts, despite using various machine learning techniques indices developed recent years. Optimization methods hybrid models are being overcome these create effective policies. In this study, analysis was conducted The Standard Precipitation Index (SPI) with monthly precipitation data from 1920 2022 Tromsø region. Models different input structures were created obtained SPI values. These then analyzed Adaptive Neuro-Fuzzy Inference System (ANFIS) by means optimization methods: Particle Swarm (PSO), Genetic Algorithm (GA), Grey Wolf (GWO), Artificial Bee Colony (ABC), PSO Support Vector Machine (SVM-PSO). Correlation coefficient (r), Root Mean Square Error (RMSE), Nash–Sutcliffe efficiency (NSE), RMSE-Standard Deviation Ratio (RSR) served performance evaluation criteria. results study demonstrated that, while successful all commonly used algorithms except for ANFIS-GWO, best values SPI12 achieved ANFIS-ABC-M04, exhibiting r: 0.9516, NSE: 0.9054, RMSE: 0.3108.

Language: Английский

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Integrating gridded precipitation data and machine learning for enhancing drought prediction in Iraq

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(3)

Published: April 2, 2025

Language: Английский

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Evaluating Performances of LSTM, SVM, GPR, and RF for Drought Prediction in Norway: A Wavelet Decomposition Approach on Regional Forecasting

Water, Journal Year: 2024, Volume and Issue: 16(23), P. 3465 - 3465

Published: Dec. 2, 2024

A serious natural disaster that poses a threat to people and their living spaces is drought, which difficult notice at first can quickly spread wide areas through subtle progression. Numerous methods are being explored identify, prevent, mitigate distinct metrics have been developed. In order contribute the research on measures be taken against Standard Precipitation Evaporation Index (SPEI), one of drought indices has developed accepted in recent years includes more comprehensive definition, was chosen this study. Machine learning deep algorithms, including support vector machine (SVM), random forest (RF), long short-term memory (LSTM), Gaussian process regression (GPR), were used model droughts six regions Norway: Bodø, Karasjok, Oslo, Tromsø, Trondheim, Vadsø. Four architectures employed for goal, as novel approach, models’ output enhanced by using discrete wavelet decomposition/transformation (WT). The outputs evaluated correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE) performance evaluation criteria. When findings analyzed, GPR (W-GPR), acquired after WT, typically produced best results. Furthermore, it discovered that, out all recognized models, M04 had most effective structure. Consequently, successful outcomes obtained with W-SVM-M04 Bodø W-GPR-M04 Oslo region results across (r: 0.9983, NSE: 0.9966 RMSE:0.0539).

Language: Английский

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A novel feature extraction-selection technique for long lead time agricultural drought forecasting

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132332 - 132332

Published: Nov. 1, 2024

Language: Английский

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Drought Prediction Using Advanced Hybrid Machine Learning for Arid and Semi-Arid Environments

KSCE Journal of Civil Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 100025 - 100025

Published: Sept. 1, 2024

Language: Английский

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Development of deep learning approaches for drought forecasting: a comparative study in a cold and semi-arid region

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Dec. 19, 2024

Language: Английский

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