Geomorphology and Flooding

Barrocu, Giovanni; Eslamian, Saeid

doi:10.1201/9781003262640-3

Cited by 3 publications

(3 citation statements)

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“…The onset of a debris flow requires large amounts of loose solid material and steep terrain [1,2]. Rain or snowfall acts as a trigger, pushing water, sand, and stones forward [3].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Numerical Simulation of Debris Flows: A New Exploration of the Hexagonal Cellular Automaton Method

Han,

Fu,

Jiang

et al. 2024

Water

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Debris flow, driven by natural events like heavy rainfall and snowmelt, involves sediment, rocks, and water, posing destructive threats to life and infrastructure. The accurate prediction of its activity range is crucial for prevention and mitigation efforts. Cellular automata circumvent is the cumbersome process of solving partial differential equations, thereby efficiently simulating complex dynamic systems. Given the anisotropic characteristics of square cells in the simulation of dynamic systems, this paper proposes a novel approach, utilizing a hexagonal cellular automaton for the numerical simulation of debris flows, where the direction judgment efficiency increased by 25%. Employing cubic interpolation, the model thereby determines the central elevation of each hexagonal cell. By modifying the flow direction function and stopping conditions, it achieves more accurate predictions of the debris flow run-out extent. This method was applied to the 2010 Yohutagawa debris flow event and the flume test. To evaluate the simulation’s accuracy, the Ω value and Fβ score were used. The Ω value is a comprehensive evaluation factor that takes into account missed or misjudgment areas. On this basis, the Fβ score emphasizes that the missed identification of debris flow areas will bring greater harm. Research indicates that the Ω value showed improvements of 6.47% and 3.96%, respectively, while the Fβ score improved by 3.10% and 4.61%.

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“…The onset of a debris flow requires large amounts of loose solid material and steep terrain [1,2]. Rain or snowfall acts as a trigger, pushing water, sand, and stones forward [3].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Numerical Simulation of Debris Flows: A New Exploration of the Hexagonal Cellular Automaton Method

Han,

Fu,

Jiang

et al. 2024

Water

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“…In addition to triggering factors, increased population density in ood plains, urban expansion, expansion of infrastructure agriculture and shing activities in riparian ood plain areas have increased ood exposure, and consequently, the vulnerability of those areas to ooding (Talukdar and Pal, 2020; Wang et al, 2020). The recent meteorological events have increased the risk of ooding in locations with ood plains due to sudden ooding, faster-moving water, and shorter reaction times (Barrocu and Eslamian, 2022). By 2050, it is anticipated that the danger of uvial oods would have increased globally by around 187 percent due to changes in the triggering variables and degree of exposure, with Asia likely suffering the most (Arnell and Gosling, 2016).…”

Section: Introductionmentioning

confidence: 99%

Modelling flood susceptibility zones using hybrid machine learning models of an agricultural dominant landscape of India

Saini

Mahato

Pandey

et al. 2023

Preprint

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One of the most destructive natural disasters is flood because it destroys a significant amount of property and infrastructure, and often causes death. Due to complexity and ferocity of severe flooding, predicting flood-prone areas is a difficult task. Each year, flooding results in destruction of agriculture, damage to resources, and fatalities in the Asia and the Pacific. Thus, creating flood susceptibility maps at local level is though challenging but inevitable task. In order to implement a flood management plan for the Balrampur district, an agricultural dominant landscape of India, and strengthen its resilience flood susceptibility modeling and mapping is carried out. In the present study, three hybrid machine learning models namely Fuzzy-ANN (Artificial Neural Network), Fuzzy-RBF (Radial Basis Function) and Fuzzy-SVM (Support Vector Machine) with 12 topographic, hydrological and other flood influencing factors were used to determine flood susceptible zones. To ascertain the relationship between the occurrences and flood influencing factors, Correlation Attributes Evaluation (CAE) and multicollinearity diagnostics tests were used. The predictive power of these models was validated and compared using a variety of statistical techniques, including Wilcoxon signed-rank, t-paired tests, and Receiver Operating Characteristic (ROC) curves. Result shows the Fuzzy-RBF model out performed other hybrid machine learning models for modelling flood susceptibility, followed by Fuzzy-ANN and Fuzzy-SVM. Overall, these models have shown promise in identifying flood-prone areas in the basin and other basins around the world. The outcomes of the work would benefit policymakers and government bodies to capture the flood-affected areas for necessary planning, action and implementation.

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“…In the Carpathians, the author examined the effects of extreme rainfalls and floods, paying attention to their frequency at different time intervals and different sizes of the catchments. In the area of tectonic subsidence at the foreland of the Sikkim-Bhutanese Himalayas, the aggradation may enter upstream into the mountain interior (Barrocu & Eslamian, 2022). Ongoing research is in progress to investigate the role of key drivers of geomorphic change such as flood power (Kale, 2008), sequencing of flood events (Magilligan et al, 1998), and spatial variations in valley floor form (Cheetham et al, 2010;Fuller, 2008).…”

Section: Introductionmentioning

confidence: 99%

The relationship between channel width changes with confinement index and flood power of an extreme flood (Case study: Ilam Dam)

2022

JSSSI

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Extreme floods are able to execute river geomorphological variations with a wide and substantial geological changes. Former studies of extreme floods have reported a reach of geomorphic replies from negligible change to catastrophic channel change. This article provides an evaluation of the geomorphic effects of a scarce, great value event that occurred in the Ilam Dam upstearim, on 29 October 2015. Variations in geomorphic replies among reaches are examined in the context of changes in flood power, channel competence and lateral confinement index by the use of field survey and Satellite Images (IRS). In this research which is focused on the plan of spatial units, channel width variations and calculation of peak discharges were used to estimate cross-sectional stream power and unit stream power. The analysis was performed for the widening (width ratio) at reach scale. The total data set includes 38 reaches. Because of the 2015 flood, the largest value of widening was 29 m (Reach 13) and it demonstrated a 100% change in the channel width. Flood power peak was calculated 10631 W m −2 along the rather confined reaches (Reach 31) and was much lower along the unconfined reaches. The tendency of high stream power values, and resultant high erosion rates, within the confined and partly confined reaches is a subordinate of the higher energy slope of the steeper.

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Geomorphology and Flooding

Cited by 3 publications

References 0 publications

Optimizing the Numerical Simulation of Debris Flows: A New Exploration of the Hexagonal Cellular Automaton Method

Optimizing the Numerical Simulation of Debris Flows: A New Exploration of the Hexagonal Cellular Automaton Method

Modelling flood susceptibility zones using hybrid machine learning models of an agricultural dominant landscape of India

The relationship between channel width changes with confinement index and flood power of an extreme flood (Case study: Ilam Dam)

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