Flood susceptibility mapping through geoinformatics and ensemble learning methods, with an emphasis on the AdaBoost-Decision Tree algorithm, in Mazandaran, Iran

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 17(2), P. 1433 - 1457

Published: Jan. 15, 2024

Language: Английский

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Urban waterlogging susceptibility assessment based on hybrid ensemble machine learning models: A case study in the metropolitan area in Beijing, China

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 630, P. 130695 - 130695

Published: Jan. 23, 2024

Language: Английский

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Artificial Intelligence in Agricultural Mapping: A Review

Agriculture, Journal Year: 2024, Volume and Issue: 14(7), P. 1071 - 1071

Published: July 3, 2024

Artificial intelligence (AI) plays an essential role in agricultural mapping. It reduces costs and time increases efficiency management activities, which improves the food industry. Agricultural mapping is necessary for resource requires technologies farming challenges. The AI applications gives its subsequent use decision-making. This study analyses AI’s current state through bibliometric indicators a literature review to identify methods, resources, geomatic tools, types, their management. methodology begins with bibliographic search Scopus Web of Science (WoS). Subsequently, data analysis establish scientific contribution, collaboration, trends. United States (USA), Spain, Italy are countries that produce collaborate more this area knowledge. Of studies, 76% machine learning (ML) 24% deep (DL) applications. Prevailing algorithms such as Random Forest (RF), Neural Networks (ANNs), Support Vector Machines (SVMs) correlate activities In addition, contributes associated production, disease detection, crop classification, rural planning, forest dynamics, irrigation system improvements.

Language: Английский

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Flash Flood Susceptibility Modelling Using Soft Computing-Based Approaches: From Bibliometric to Meta-Data Analysis and Future Research Directions

Water, Journal Year: 2024, Volume and Issue: 16(1), P. 173 - 173

Published: Jan. 3, 2024

In recent years, there has been a growing interest in flood susceptibility modeling. this study, we conducted bibliometric analysis followed by meta-data to capture the nature and evolution of literature, intellectual structure networks, emerging themes, knowledge gaps Relevant publications were retrieved from Web Science database identify leading authors, influential journals, trending articles. The results indicated that hybrid models most frequently used prediction models. Results show GIS, machine learning, statistical models, analytical hierarchy process central focuses research area. also revealed slope, elevation, distance river are commonly factors present study discussed importance resolution input data, size representation training sample, other lessons learned, future directions field.

Language: Английский

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Integrated GIS and analytic hierarchy process for flood risk assessment in the Dades Wadi watershed (Central High Atlas, Morocco)

Results in Earth Sciences, Journal Year: 2024, Volume and Issue: 2, P. 100019 - 100019

Published: March 18, 2024

Flood risk assessment is crucial for delineating flood hazard zones and formulating effective mitigation strategies. Employing a multi-criteria decision support system, this study focused on assessing Risk Index (FHI) at the Dades Wadi watershed scale. Seven main flood-causing criteria were broadly selected, namely flow accumulation, distance from hydrographic network, drainage network density, land use, slope, rainfall, permeability. The relative importance of each criterion prioritized as per their contribution toward risk, which employed blend Analytical Hierarchy Process (AHP) Geographic Information System (GIS)/Remote Sensing (RS) techniques. significance was determined based to hazard, established through an AHP pair-wise comparison matrix. efficacy model performed with consistency ratio 0.08, indicated that weight confirmed. Among criteria, hydrologic accumulation factor identified most influential (weight: 3.11), while permeability exhibited least prominence 0.58). Approximately 40.36% total area, equivalent around 1319, 89 km2, concentrated within very high flood-risk situated near rivers. In contrast, area approximately 399,943 km2 (56.33%) low zone. validation FHI map encompassed application Receiver Operating Characteristic Curve (ROC) technique, revealing Area Under (AUC) 85%.

Language: Английский

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Global systematical and comprehensive overview of mountainous flood risk under climate change and human activities

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 941, P. 173672 - 173672

Published: May 31, 2024

Language: Английский

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Optimizing flood predictions by integrating LSTM and physical-based models with mixed historical and simulated data

Heliyon, Journal Year: 2024, Volume and Issue: 10(13), P. e33669 - e33669

Published: June 27, 2024

The current flood forecasting models heavily rely on historical measured data, which is often insufficient for robust predictions due to practical challenges such as high measurement costs and data scarcity. This study introduces a novel hybrid approach that synergistically combines the outputs of traditional physical-based with train Long Short-Term Memory (LSTM) networks. Specifically, NAM hydrological model HD hydraulic are employed simulate processes. Focusing Jinhua basin, typical plains river area in China, this research evaluates efficacy LSTM trained measured, mixed, simulated datasets. architecture includes multiple layers, optimized hyperparameters tailored forecasting. Key performance indicators Root Mean Square Error (RMSE), Absolute (MAE), Peak-relative (PRE) assess predictive accuracy models. findings demonstrate mixed datasets simulated-to-measured ratio less than 2:1 consistently achieve superior performance, exhibiting significantly lower RMSE MAE values compared larger ratios. highlights advantage integrating leveraging strengths both types enhance accuracy. Despite its advantages, has limitations, including dependence quality potential computational complexity. However, development marks significant advancement forecasting, offering promising solution efficiency Potential applications include real-time prediction risk management other flood-prone regions, providing framework diverse sources improve

Language: Английский

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Exploring a spatiotemporal hetero graph-based long short-term memory model for multi-step-ahead flood forecasting

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 633, P. 130937 - 130937

Published: Feb. 27, 2024

Language: Английский

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A novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2024, Volume and Issue: 135, P. 103646 - 103646

Published: May 28, 2024

Language: Английский

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Robustness of machine learning algorithms to generate flood susceptibility maps for watersheds in Jordan

Geomatics Natural Hazards and Risk, Journal Year: 2024, Volume and Issue: 15(1)

Published: July 20, 2024

The study examined three machine learning algorithms (MLAs): random forest (RF), support vector (SVM), and artificial neural networks (ANN) for generating flood susceptibility maps in two watersheds Jordan. Both were selected because they represent climatic regimes: desert mountainous areas. Because of a shortage past floods location, physical model was utilized to generate them based on simulations 100-year rainfall. 10,000 randomly used MLAs training testing. During training, thirteen influential factors identified. Out them, the distance stream, elevation, topographic wetness index have shown an overwhelming effect Zarqa Ma'in watershed (they gained 50% IGR), while stream density, elevation Al-Buaida 44% IGR). For mapping, RF outperformed other both thus mapping. classified into five classes, 11% fell high very 5.2% within these classes. In conclusion, able produce efficiently, can form alternative modeling.

Language: Английский

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