Characterizing fluvial geomorphological change using Google Earth Engine (GEE) to support sustainable flood management in the rural municipality of El Faid

Caiani, Enrico G.; Khattabi, Abdellatif; Lahssini, Saïd

doi:10.1007/s12517-022-09674-3

Cited by 5 publications

(1 citation statement)

References 51 publications

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“…Thus, a combination of MNDWI and NDVI can be used that assess not only the open water flooding but also the productivity response to flooding, according to the vegetation response. This combination of indices has been already used by several authors for monitoring flooding areas (Azedou et al, 2022;Mehmood et al, 2021;Solana et al, 2021b, among others). However, most of these studies focused on the detection of open water surfaces and disregarded the FV class, which can lead to an underestimation of the extent of inundation.…”

Section: Discussionmentioning

confidence: 99%

Cartografía multitemporal y estrategias para la prevención de inundaciones en una cuenca con predominio agrícola de Argentina

Solana,

Romanelli,

Quiroz Londoño

2023

CURIHAM

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En este estudio se propone un método semiautomático para la cartografía de la extensión y frecuencia de las inundaciones en una cuenca hidrográfica con predominio agrícola, seleccionándose la cuenca del Río Quequén Grande como caso de estudio. Mediante la combinación de índices diferenciales normalizadoscalculados a partir de imágenes Landsat y la aplicación del método de umbralización desarrollado por Otsu en el entorno de Google Earth Engine (GEE), se definieron dos categorías para las inundaciones: superficies de inundación abiertas (OFS) y vegetación inundada (FV). El análisis de la frecuencia histórica de las inundaciones permitió la propuesta de estrategias de prevención a las inundaciones dirigidas a ser implementadas en cada clase de frecuencia de inundación definida, siendo esencial para la mitigación de inundaciones en cuencas hidrográficas con predominio agrícola.

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Section: Discussionmentioning

confidence: 99%

Cartografía multitemporal y estrategias para la prevención de inundaciones en una cuenca con predominio agrícola de Argentina

Solana,

Romanelli,

Quiroz Londoño

2023

CURIHAM

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Assessing Vegetation Change Patterns in Talassemtane National Park (Morocco) Using Temporal Segmentation Algorithm and Cloud Computing Techniques

Azedou,

Amine,

Lahssini

2024

Recent Advances in Environmental Science From the Euro-Mediterranean and Surrounding Regions (4th Edition)

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Investigating the Role of the Key Conditioning Factors in Flood Susceptibility Mapping Through Machine Learning Approaches

Al-Kindi,

Alabri

2024

Earth Syst Environ

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This study harnessed the formidable predictive capabilities of three state-of-the-art machine learning models—extreme gradient boosting (XGB), random forest (RF), and CatBoost (CB)—applying them to meticulously curated datasets of topographical, geological, and environmental parameters; the goal was to investigate the intricacies of flood susceptibility within the arid riverbeds of Wilayat As-Suwayq, which is situated in the Sultanate of Oman. The results underscored the exceptional discrimination prowess of XGB and CB, boasting impressive area under curve (AUC) scores of 0.98 and 0.91, respectively, during the testing phase. RF, a stalwart contender, performed commendably with an AUC of 0.90. Notably, the investigation revealed that certain key variables, including curvature, elevation, slope, stream power index (SPI), topographic wetness index (TWI), topographic roughness index (TRI), and normalised difference vegetation index (NDVI), were critical in achieving an accurate delineation of flood-prone locales. In contrast, ancillary factors, such as annual precipitation, drainage density, proximity to transportation networks, soil composition, and geological attributes, though non-negligible, exerted a relatively lesser influence on flood susceptibility. This empirical validation was further corroborated by the robust consensus of the XGB, RF and CB models. By amalgamating advanced deep learning techniques with the precision of geographical information systems (GIS) and rich troves of remote-sensing data, the study can be seen as a pioneering endeavour in the realm of flood analysis and cartographic representation within semiarid fluvial landscapes. The findings advance our comprehension of flood vulnerability dynamics and provide indispensable insights for the development of proactive mitigation strategies in regions that are susceptible to hydrological perils.

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Characterizing fluvial geomorphological change using Google Earth Engine (GEE) to support sustainable flood management in the rural municipality of El Faid

Cited by 5 publications

References 51 publications

Cartografía multitemporal y estrategias para la prevención de inundaciones en una cuenca con predominio agrícola de Argentina

Cartografía multitemporal y estrategias para la prevención de inundaciones en una cuenca con predominio agrícola de Argentina

Assessing Vegetation Change Patterns in Talassemtane National Park (Morocco) Using Temporal Segmentation Algorithm and Cloud Computing Techniques

Investigating the Role of the Key Conditioning Factors in Flood Susceptibility Mapping Through Machine Learning Approaches

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