Lake Water Level Forecasting Using LSTM and GRU: A Deep Learning Approach DOI

Yuxin Du,

Jing Fan, Ari Happonen

et al.

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 197 - 216

Published: Jan. 1, 2024

Language: Английский

A systematic review of trustworthy artificial intelligence applications in natural disasters DOI Creative Commons
A. S. Albahri, Yahya Layth Khaleel, Mustafa Abdulfattah Habeeb

et al.

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

Citations

42
An interpretable hybrid deep learning model for flood forecasting based on Transformer and LSTM DOI Creative Commons
Wenzhong Li,

Chengshuai Liu,

Yingying Xu

et al.

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 54, P. 101873 - 101873

Published: June 27, 2024

Language: Английский

Citations

19
Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism DOI
Marjan Kordani, Mohammad Reza Nikoo, Mahmood Fooladi

et al.

Journal of Hydrologic Engineering, Journal Year: 2024, Volume and Issue: 29(6)

Published: Sept. 14, 2024

Language: Английский

Citations

11
Enhancing Flood Risk Mitigation by Advanced Data-Driven Approach DOI Creative Commons

Ali S. Chafjiri,

Mohammad Gheibi, Benyamin Chahkandi

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(18), P. e37758 - e37758

Published: Sept. 1, 2024

Language: Английский

Citations

6
IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon DOI
Vitrice Ruben Folifack Signing, Jacob Mbarndouka Taamté, Michaux Kountchou

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(7)

Published: June 15, 2024

Language: Английский

Citations

5
Mountain Flood Level Forecasting in Small Watersheds Based on Recurrent Neural Networks and Multi-Dimensional Data DOI Creative Commons
Songsong Wang, Ouguan Xu

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 82706 - 82719

Published: Jan. 1, 2024

Language: Английский

Citations

4
ML4FF: A machine-learning framework for flash flood forecasting applied to a Brazilian watershed DOI
Jaqueline A. J. P. Soares, Luan Carlos de Sena Monteiro Ozelim, Luiz Bacelar

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132674 - 132674

Published: Jan. 1, 2025

Language: Английский

Citations

0
Enhancing Long-Term Flood Forecasting with SageFormer: A Cascaded Dimensionality Reduction Approach Based on Satellite-Derived Data DOI Creative Commons
Fatemeh Ghobadi, Amir Saman Tayerani Charmchi,

Doosun Kang

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 365 - 365

Published: Jan. 22, 2025

Floods, increasingly exacerbated by climate change, are among the most destructive natural disasters globally, necessitating advancements in long-term forecasting to improve risk management. Traditional models struggle with complex dependencies of hydroclimatic variables and environmental conditions, thus limiting their reliability. This study introduces a novel framework for enhancing flood accuracy integrating geo-spatiotemporal analyses, cascading dimensionality reduction, SageFormer-based multi-step-ahead predictions. The efficiently processes satellite-derived data, addressing curse focusing on critical long-range spatiotemporal dependencies. SageFormer captures inter- intra-dependencies within compressed feature space, making it particularly effective forecasting. Performance evaluations against LSTM, Transformer, Informer across three data fusion scenarios reveal substantial improvements accuracy, especially data-scarce basins. integration hydroclimate attention-based networks reduction demonstrates significant over traditional approaches. proposed combines advanced deep learning, both interpretability precision capturing By offering straightforward reliable approach, this advances remote sensing applications hydrological modeling, providing robust tool mitigating impacts extremes.

Language: Английский

Citations

0
Comparative analysis of deep learning and machine learning techniques for forecasting new malaria cases in Cameroon’s Adamaoua region DOI Creative Commons

Esaie Naroum,

Ebenezer Maka Maka,

Hamadjam Abboubakar

et al.

Intelligence-Based Medicine, Journal Year: 2025, Volume and Issue: unknown, P. 100220 - 100220

Published: Feb. 1, 2025

Language: Английский

Citations

0
Exploring a long short-term memory for mountain flood forecasting based on watershed-internal knowledge graph and large language model DOI Creative Commons
Songsong Wang, Ouguan Xu

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(3), P. e0318644 - e0318644

Published: March 13, 2025

The water levels associated with mountain floods exhibit rapid fluctuations within small watersheds, necessitating extensive data on various factors influencing such disasters to facilitate real-time forecasting. This study investigates the application of Long Short-Term Memory (LSTM) networks for flood forecasting, designing a watershed-internal Knowledge Graph (KG) and Large Language Model (LLM) that encompass watershed relationships internal information structures. We have developed hydrological KG Qixi Reservoir Qiaodongcun forecasting points located in Zhejiang Province, China, systematically organize conservancy data, identify significant disaster-related factors, optimize input determine most effective combination levels. Additionally, we implemented Recurrent Neural Networks (RNN) Gated Units (GRU) comparative analysis LSTM. findings indicate LSTM model, when integrated LLM, can effectively incorporate critical elements level changes, accuracy LLM-KG-LSTM model is enhanced by 3% compared standard series outperforms both RNN GRU models, Our method will guide future research from perspective focusing algorithms relationship between multi-dimensional disaster algorithm parallelism.

Language: Английский

Citations

0