An integrated approach for evaluating the flash flood risk and potential erosion using the hydrologic indices and morpho-tectonic parameters

El-Magd, Sherif Ahmed Abu; Orabi, H.; Ali, Sk Ajim; Parvin, Farhana; Pham, Quoc Bao

doi:10.1007/s12665-021-10013-0

Cited by 23 publications

(15 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Bannari et al (2017) and Ahmad et al (2019), these parameters are associated with soil erosion, flow accumulation, sediment deposition, and particle detachment onto the channel. The combination of hydrological indices and morphometric parameters allows for the assessment of potential erosion and flash floods (Abu El-Magd et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Modelo predictivo para la estimación de volumen de sedimentos captado por sistemas lama-bordo en la Mixteca Alta de Oaxaca, México

Santiago-Mejía,

Fernández-Reynoso,

López-Pérez

et al. 2024

Rev Cha Se Cie For y del Amb

View full text Add to dashboard Cite

Introducción: Los sistemas lama-bordo (SLB) se construyen en cauces naturales y favorecen la retención de sedimentos y humedad del escurrimiento para el desarrollo de la actividad agrícola.Objetivo: Obtener un modelo de predicción de volumen de suelo captado por los SLB en forma permanente, a partir de las características morfométricas e hidrológicas de las microcuencas donde estos han prevalecido en la Mixteca Alta de Oaxaca, México.Materiales y métodos: El estudio se hizo en 27 sitios donde se determinaron parámetros morfométricos e índices hidrológicos que permitieron obtener el modelo de predicción de volumen mediante regresión lineal múltiple tipo backward stepwise.Resultados y discusión: Los sistemas estudiados se encontraron en microcuencas bajo condiciones que favorecen el transporte de sedimentos: forma alargada, áreas menores de 2 km2 con corrientes de 1° y 2° orden, en altitudes superiores a los 2 000 m y pendientes mayores de 10 %. Doce parámetros morfométricos y tres índices hidrológicos caracterizan dichas microcuencas y explican las condiciones físicas que permiten su establecimiento, pero solo cuatro (área de la microcuenca, pendiente media de la corriente principal, índice topográfico de humedad e índice de transporte de sedimentos) explican el volumen de sedimento acumulado (R2 = 0.85, P < 0.001).Conclusiones: El modelo evaluado para la estimación de volumen es confiable para su aplicación en sitios bajo condiciones similares.

show abstract

Section: Introductionmentioning

confidence: 99%

Modelo predictivo para la estimación de volumen de sedimentos captado por sistemas lama-bordo en la Mixteca Alta de Oaxaca, México

Santiago-Mejía,

Fernández-Reynoso,

López-Pérez

et al. 2024

Rev Cha Se Cie For y del Amb

View full text Add to dashboard Cite

show abstract

“…Machine learning (ML) is part of algorithmic and heuristic approaches that are designed to understand correlations in specific datasets through intuitive training. Various researchers and scientists have reported ML approaches toward analysis and forecast studies for hydrology, floods, and landslides analysis and prediction (Abu El-Magd et al 2021a , b , c ; Al-Abadi 2018 ; Ali et al 2013 ; Khosravi et al 2019 ; Rahmati et al 2019 ; Shahabi et al 2020 ; Zhao et al 2019 ). Flood predictions were performed with many ML techniques in order to evolve a flood management system.…”

Section: Introductionmentioning

confidence: 99%

“…Flood predictions were performed with many ML techniques in order to evolve a flood management system. Many investigations and studies have been carried out on flood assessment and modeling using hydrological studies, physical modeling, GIS, and remote sensing (Pradhan et al 2014 ; Liu et al 2019 ; Abu El-Magd et al 2021b ). However, data-driven prediction and forecasting using ML models are promising tools as they are easier to apply with minimal inputs.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, ML algorithms have been steadily improving, demonstrating their ability for flood forecasting with a reasonable rate of outperforming traditional approaches. Furthermore, the ML was reported as an effective tool for prediction and forecasting including numerous studies integrating ML algorithms, e.g., artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGB), boosted regression tree (BRT), K-nearest neighbor (KNN), and general linear model (GLM) (Sulaiman and Wahab 2017 ; Abu El-Magd et al 2021b ; Abu El-Magd 2022 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid-based Bayesian algorithm and hydrologic indices for flash flood vulnerability assessment in coastal regions: machine learning, risk prediction, and environmental impact

El-Magd

Maged

Farhat

2022

Environ Sci Pollut Res

Self Cite

View full text Add to dashboard Cite

Natural hazards and severe weather events are a matter of serious threat to humans, economic activities, and the environment. Flash floods are one of the extremely devastating natural events around the world. Consequently, the prediction and precise assessment of flash flood-prone areas are mandatory for any flood mitigation strategy. In this study, a new hybrid approach of machine learning (ML) algorithm and hydrologic indices opted to detect impacted and highly vulnerable areas. The obtained models were trained and validated using a total of 189 locations from Wadi Ghoweiba and surrounding area (case study). Various controlling factors including varied datasets such as stream transport index (STI), stream power index (SPI), lithological units, topographic wetness index (TWI), slope angle, stream density (SD), curvature, and slope aspect (SA) were utilized via hyper-parameter optimization setting to enhance the performance of the proposed model prediction. The hybrid machine learning (HML) model, developed by combining naïve Bayes (NïB) approach and hydrologic indices, was successfully implemented and utilized to investigate flash flood risk, sediment accumulation, and erosion predictions in the studied site. The synthesized new hybrid model demonstrated a model accuracy of 90.8% compared to 87.7% of NïB model, confirming the superior performance of the obtained model. Furthermore, the proposed model can be successfully employed in large-scale prediction applications.

show abstract

“…The latter, as an alliance of Europe's leading research universities, was initially established by 12 top European research universities in 2002. Egypt's Knowledge Base ranked fourth[82][83][84]. The fifth ranked institution was the National Center for Scientific Research in France[48,[85][86][87][88].…”

mentioning

confidence: 99%

Meta-Analysis and Visualization of the Literature on Early Identification of Flash Floods

et al. 2022

View full text Add to dashboard Cite

Flash flood is one of the extremely destructive natural disasters in the world. In recent years, extreme rainfall events caused by global climate change have increased, and flash flood disasters are becoming the main types of natural disasters in the world. Due to the characteristics of strong suddenness, complex disaster-causing factors, great difficulty in prediction and forecast, and the lack of historical data, it is difficult to effectively prevent and control flash flood disaster. The early identification technology of flash floods is not only the basis of flash flood disaster prediction and early warning, but also an effective means of flash flood prevention and control. The paper makes a meta-analysis and visual analysis of 475 documents collected by the Web of Science Document Platform in the past 31 years by comprehensively using Citespace, Vosviewer, Origin, etc. We systematically summarize the research progress and development trend of early identification technology of flash flood disasters from five key research subfields: (1) precipitation, (2) sediment, (3) sensitivity analysis, (4) risk assessment, (5) uncertainty analysis. In addition, we analyze and discuss the main problems encountered in the current research of several subfields and put forward some suggestions to provide references for the prevention and control of flash flood disasters.

show abstract

An integrated approach for evaluating the flash flood risk and potential erosion using the hydrologic indices and morpho-tectonic parameters

Cited by 23 publications

References 74 publications

Modelo predictivo para la estimación de volumen de sedimentos captado por sistemas lama-bordo en la Mixteca Alta de Oaxaca, México

Modelo predictivo para la estimación de volumen de sedimentos captado por sistemas lama-bordo en la Mixteca Alta de Oaxaca, México

Hybrid-based Bayesian algorithm and hydrologic indices for flash flood vulnerability assessment in coastal regions: machine learning, risk prediction, and environmental impact

Meta-Analysis and Visualization of the Literature on Early Identification of Flash Floods

Contact Info

Product

Resources

About