Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco

Remote Sensing, Год журнала: 2024, Номер 16(5), С. 858 - 858

Опубликована: Фев. 29, 2024

Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., synthetic aperture radar (SAR) images inventory preparation integrated four machine learning models (Random Forest: RF, Classification Regression Trees: CART, Support Vector Machine: SVM, Extreme Gradient Boosting: XGBoost) predict Metlili watershed, Morocco. Initially, 12 independent variables (elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power distance from streams, roads, lithology, rainfall, land use/land cover, normalized vegetation index) were as conditioning factors. The dataset was divided into 70% 30% training validation purposes using popular library, scikit-learn (i.e., train_test_split) Python programming language. Additionally, area under curve (AUC) evaluate performance models. accuracy results showed that XGBoost predicted with AUC values 0.807, 0.780, 0.756, 0.727, respectively. However, RF model performed better at prediction compared other applied. As per model, 22.49%, 16.02%, 12.67%, 18.10%, 31.70% watershed are estimated being very low, moderate, high, highly susceptible flooding, Therefore, integration data could have promising predicting similar environments.

Язык: Английский

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Leveraging machine learning and open-source spatial datasets to enhance flood susceptibility mapping in transboundary river basin

International Journal of Digital Earth, Год журнала: 2024, Номер 17(1)

Опубликована: Фев. 9, 2024

Floods pose devastating effects on the resiliency of human and natural systems. flood risk management challenges are typically complicated in transboundary river basin due to conflicting objectives between multiple countries, lack systematic approaches data monitoring sharing, limited collaboration developing a unified system for hazard prediction communication. An open-source, low-cost modeling framework that integrates open-source models can help improve our understanding susceptibility inform design equitable strategies. This study datasets machine -learning techniques quantify across data-scare basin. The analysis focuses Gandak River Basin, spanning China, Nepal, India, where damaging recurring floods serious concern. is assessed using four widely used learning techniques: Long-Short-Term-Memory, Random Forest, Artificial Neural Network, Support Vector Machine. Our results exhibit improved performance Network Machine predicting maps, revealing higher vulnerability southern plains. demonstrates remote sensing prediction, mapping, environment.

Язык: Английский

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GIS-based flood susceptibility mapping using bivariate statistical model in Swat River Basin, Eastern Hindukush region, Pakistan

Frontiers in Environmental Science, Год журнала: 2023, Номер 11

Опубликована: Июль 6, 2023

Frequent flooding can greatly jeopardize local people’s lives, properties, agriculture, economy, etc. The Swat River Basin (SRB), in the eastern Hindukush region of Pakistan, is a major flood-prone basin with long history devastating floods and substantial socioeconomic physical damages. Here we produced flood susceptibility map SRB, using frequency ratio (FR) bivariate statistical model. A database was created that comprised inventory as dependent variable causative factors (slope, elevation, curvature, drainage density, topographic wetness index, stream power land use cover, normalized difference vegetation rainfall) independent variables association between them were quantified. Data collected remote sensing sources, field surveys, available literature, all studied resampled to 30 m resolution spatially distributed. results show about 26% areas are very high highly susceptible flooding, 19% moderate, whereas 55% low SRB. Overall, southern SRB compared their northern counterparts, while slope, curvature vital susceptibility. Our model’s success prediction rates 91.6% 90.3%, respectively, based on ROC (receiver operating characteristic) curve. findings this study will lead better management control risk region. study’s assist decision-makers make appropriate sustainable strategies for mitigation future damage

Язык: Английский

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Improving the simulations of the hydrological model in the karst catchment by integrating the conceptual model with machine learning models

The Science of The Total Environment, Год журнала: 2024, Номер 926, С. 171684 - 171684

Опубликована: Март 18, 2024

Hydrological modelling can be complex in nonhomogeneous catchments with diverse geological, climatic, and topographic conditions. In this study, an integrated conceptual model including the snow module machine learning approaches was implemented for daily rainfall-runoff mostly karst Ljubljanica catchment, Slovenia, which has heterogeneous characteristics is potentially exposed to extreme events that make process more challenging crucial. regard, CemaNeige Génie Rural à 6 paramètres Journalier (CemaNeige GR6J) combined models, namely wavelet-based support vector regression (WSVR) multivariate adaptive spline (WMARS) enhance performance. performance of models comprehensively investigated, considering their ability forecast runoff. Although GR6J yielded a very good performance, it overestimated low flows. The WSVR WMARS poorer than hybrid models. approach improved by revealing linkage between variables runoff model, provided accurate results Accordingly, forecasting maximum flows up 40 % 61 %, minimum 73 72 compared stand-alone where hydrological complicated.

Язык: Английский

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SHAP-powered insights into spatiotemporal effects: Unlocking explainable Bayesian-neural-network urban flood forecasting

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 131, С. 103972 - 103972

Опубликована: Июнь 25, 2024

Given the increased incidence of pluvial floods due to climate change and urbanization, demand for highly efficient accurate modeling within urban drainage systems has intensified, making machine learning deep techniques increasingly popular. Nonetheless, these data-driven approaches face challenges in adequately capturing interpreting dynamic process-evolving features, especially spatiotemporal effects emanating from manholes during waterlogging events. To address issues, this study proposes a general framework that extracts using spatial Durbin model, integrates such with four models (i.e., artificial neural network, Bayesian network (BNN), light gradient boosting machine, long short-term memory network), clarifies decision-making processes best model by employing Shapley Additive Explanations (SHAP) method. The results indicate (1) BNN (BNNST) not only outperforms other benchmark but also provides forecasts quantifiable uncertainties; (2) compared original enhance models' understanding flooding dynamics, thereby improving predictive precision; (3) comprise roughly 14 % contributions BNNST's output, as interpreted SHAP-based explanations; (4) incorporating interpretability into technique underscores trustworthiness explanations at varying confidence levels, deepening processes.

Язык: Английский

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Integrating deep learning, satellite image processing, and spatial-temporal analysis for urban flood prediction

Journal of Hydrology, Год журнала: 2024, Номер 639, С. 131508 - 131508

Опубликована: Июнь 14, 2024

Язык: Английский

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Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets

Water, Год журнала: 2024, Номер 16(13), С. 1904 - 1904

Опубликована: Июль 3, 2024

Machine learning (ML) applications in hydrology are revolutionizing our understanding and prediction of hydrological processes, driven by advancements artificial intelligence the availability large, high-quality datasets. This review explores current state ML hydrology, emphasizing utilization extensive datasets such as CAMELS, Caravan, GRDC, CHIRPS, NLDAS, GLDAS, PERSIANN, GRACE. These provide critical data for modeling various parameters, including streamflow, precipitation, groundwater levels, flood frequency, particularly data-scarce regions. We discuss type methods used significant successes achieved through those models, highlighting their enhanced predictive accuracy integration diverse sources. The also addresses challenges inherent applications, heterogeneity, spatial temporal inconsistencies, issues regarding downscaling LSH, need incorporating human activities. In addition to discussing limitations, this article highlights benefits utilizing high-resolution compared traditional ones. Additionally, we examine emerging trends future directions, real-time quantification uncertainties improve model reliability. place a strong emphasis on citizen science IoT collection hydrology. By synthesizing latest research, paper aims guide efforts leveraging large techniques advance enhance water resource management practices.

Язык: Английский

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Leveraging GIS-based AHP, remote sensing, and machine learning for susceptibility assessment of different flood types in peshawar, Pakistan

Journal of Environmental Management, Год журнала: 2024, Номер 371, С. 123094 - 123094

Опубликована: Ноя. 2, 2024

Язык: Английский

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Improving flood-prone areas mapping using geospatial artificial intelligence (GeoAI): A non-parametric algorithm enhanced by math-based metaheuristic algorithms

Journal of Environmental Management, Год журнала: 2025, Номер 375, С. 124238 - 124238

Опубликована: Янв. 29, 2025

Язык: Английский

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A novel multi-strategy hydrological feature extraction (MHFE) method to improve urban waterlogging risk prediction, a case study of Fuzhou City in China

The Science of The Total Environment, Год журнала: 2023, Номер 904, С. 165834 - 165834

Опубликована: Июль 28, 2023

Язык: Английский

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