Alternate pathway for regional flood frequency analysis in data-sparse region

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 629, P. 130635 - 130635

Published: Jan. 17, 2024

Language: Английский

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Flood susceptibility mapping using a novel integration of multi-temporal sentinel-1 data and eXtreme deep learning model

Geoscience Frontiers, Journal Year: 2024, Volume and Issue: 15(3), P. 101780 - 101780

Published: Jan. 9, 2024

Flash floods (FFs) are amongst the most devastating hazards in arid regions response to climate change and can cause loss of agricultural land, human lives infrastructure. One major challenges is high-intensity rainfall events affecting low-lying areas that vulnerable FF. Several works this field have been conducted using ensemble machine learning models geohydrological models. However, current advancement eXtreme deep learning, which named factorisation (xDeepFM), for FF susceptibility mapping (FSM) lacking literature. The study introduces a new model employs previously unapplied approach enhance FSM capturing severity floods. proposed has three main objectives: (i) During- after-flood effects assessed through flood detection techniques Sentinel-1 data. (ii) Flood inventory updated remote sensing-based methods. derived implemented next step. (iii) An map generated an xDeepFM model. Therefore, aims apply estimate susceptible 13 factors emirates Fujairah, UAE. performance metrics show recall 0.9488), F1-score 0.9107), precision (0.8756) overall accuracy 90.41%. applied compared with traditional models, specifically neural network (78%), support vector (85.4%) random forest (88.75%). Random achieves high accuracy, due its strong depends on contribution, dataset size quality, available computational resources. Comparatively, efficiently complicated prediction problems having non-collinearity huge datasets. obtained denotes narrow basins, lowland coastal riverbank up 5 km (Fujairah) highly prone FF, whilst alluvial plains Al Dhaid hilly Fujairah low probability. city bounded by high-rise steep hills Gulf Oman, elevate water levels during heavy rainfall. Four synchronised influencing factors, namely, rainfall, elevation, drainage density, distance from geomorphology, account nearly 50% total contributing very susceptibility. This offers platform planners decision makers take timely actions potential mitigating

Language: Английский

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Flood Hazard and Risk Mapping by Applying an Explainable Machine Learning Framework Using Satellite Imagery and GIS Data

Sustainability, Journal Year: 2022, Volume and Issue: 14(6), P. 3251 - 3251

Published: March 10, 2022

Flooding is one of the most destructive natural phenomena that happen worldwide, leading to damage property and infrastructure or even loss lives. The escalation in intensity number flooding events as a result combination climate change anthropogenic factors motivates need adopt real-time solutions for mapping flood hazards risks. In this study, methodological framework proposed enables assessment hazard risk levels severity dynamically by fusing optical remote sensing (Sentinel-1) GIS-based data from region Trieste, Monfalcone Muggia Municipalities. Explainable machine learning techniques were utilised, aiming interpret results hazard. inventory was randomly divided into 70%, used training, 30%, employed testing. Various combinations models evaluated revealed Random Forest model achieved highest F1-score (approx. 0.99), among others utilised generating maps. Furthermore, estimation rule-based approach estimate exposure vulnerability with dynamic

Language: Английский

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Flood susceptibility zonation using advanced ensemble machine learning models within Himalayan foreland basin

Natural Hazards Research, Journal Year: 2022, Volume and Issue: 2(4), P. 363 - 374

Published: June 14, 2022

Floods are considered as one of nature's most destructive fluvio-hydrological extremes because the massive damage to agricultural land, roads and buildings human fatalities. Rapid development unplanned infrastructural conveniences anthropogenic activities, frequency intensity floods have been accelerated in recent years. Therefore, flood susceptibility analysis is an important management approach. Identification areas has performed by applying advanced machine learning (ML) algorithms (random forest (RF), support vector (SVM) extreme gradient boosting (XGBoost)) at lower part Raidak river basin. The maps generated based on 14 different conditioning factors. Models evaluated a conventional way using ROC (receiver operating characteristics) curve. AUC value above 0.80 for all models XGBoost depicts highest efficacy (AUC ​= ​0.92). Friedman test Wilcoxon Signed rank used measure statistical variances among applied models. proficiently show that upper basin less probable region whereas eastern some middle parts high probability. Around 27% area (285.39 sq.km) within highly prone (based model) due fast changing dynamic landscape large scale intervention. outcomes this research will definitely assist local administrators take proper sustainable plans reduction future damages.

Language: Английский

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Flood Risk-Related Research Trends in Latin America and the Caribbean

Water, Journal Year: 2021, Volume and Issue: 14(1), P. 10 - 10

Published: Dec. 22, 2021

Latin America and the Caribbean (LAC), like many other regions in world, are areas that prone to hydrometeorological disasters, which threaten livelihoods cause economic losses. To derive LAC’s status field of flood risk-related research, we conducted a bibliometric analysis region’s publication record using Web Science journal database (WoS). After analysing total 1887 references according inclusion-exclusion criteria, 302 articles published last 20 years were selected. The research period 2000–2020 revealed Mexico, Brazil, certain South American countries such as Chile, Peru, Argentina more productive risk research. Scientific is increasing, most available studies focus on lowland areas. frequently-used keywords generic, there often verbatim copying from title article, shows poor coherence between title, abstract, keywords. This limited diversification little use studies, reducing their visibility negatively impacting citation count level. LAC mainly related assessments, analyses, geomorphological ecosystem vulnerability resilience approaches, statistical geographic information science evaluations. systematic review reveals although has been important two decades, future linked with climatic scenarios key development realistic solutions disaster risks.

Language: Английский

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Climate change, multi-hazards and society: An empirical study on the coastal community of Indian Sundarban

Natural Hazards Research, Journal Year: 2022, Volume and Issue: 2(2), P. 84 - 96

Published: April 23, 2022

Effective mitigation and adaptation methods are critical for addressing multi-hazards in various parts of the world as a result changing climate occurrences. Basically, coastal areas around have been proven to be particularly sensitive at risk recent change, forcing people relocate order survive. In previous 2–3 years, cyclones such Fani, Bulbul, Amphan, Yass wreaked havoc on eastern India's region. The aim this study is look into population Indian Sundarban's perceptions hazards their solutions dealing with growing threat hazards. To measure multi-hazard impact, survey 850 rural households was conducted four different geographical regions (i.e. island, coastal, riverine, inland). Several forms coping techniques discovered, results differ from one place next, demonstrating impact risks studied area. It obvious analysis that, exception island households, other use very limited number mechanisms. When it comes amount strategies used, has discovered that most inhabitants outside islands 1–3 (nearly 56%) using food finance safeguard, but dwellers 4–6 78%) form asset related issues. Reducing consumed, obtaining financing organizations, migrating some primary tactics used region combat negative effects change-related multi-hazards.

Language: Английский

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Topographic Wetness Index as a Proxy for Soil Moisture in a Hillslope Catena: Flow Algorithms and Map Generalization

Land, Journal Year: 2022, Volume and Issue: 11(11), P. 2018 - 2018

Published: Nov. 11, 2022

Topographic wetness index (TWI) is used as a proxy for soil moisture, but how well it performs across varying timescales and methods of calculation not understood. To assess the effectiveness TWI, we examined spatial correlations between in situ volumetric water content (VWC) TWI values over 5 years soils at 42 locations an agroforestry catena Fayetteville, Arkansas, USA. We calculated 546 ways using different flow algorithms digital elevation model (DEM) preparations. found that most performed poorly on DEMs were first filtered or resampled, DEM filtration resampling (collectively called generalization) greatly improved performance. Seasonal variation moisture influenced performance which was best when conditions saturated dry. Pearson correlation coefficients grand mean VWC 5-year measurement period ranged from 0.18 to 0.64 generalized 0.15 0.59 generalized. These results aid management crop fields with variable characteristics.

Language: Английский

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Modelling hydrological factors from DEM using GIS

MethodsX, Journal Year: 2023, Volume and Issue: 10, P. 102062 - 102062

Published: Jan. 1, 2023

Hydrological modelling is a precondition for many scientific researches such as species distribution models, ecological agricultural suitability climatological hydrological flood and flash landslide models etc. Even the topographic control over factors has also been studied. Over time different have developed extensively used. Recently, these used to prepare types of conditional that are widely in hazard floods, landslides Quantitative analysis Digital Elevation Model (DEM) according by engaging Geographic Information Systems (GIS) supports users extract various information about landscapes where most important. Methods namely TWI, TRI, SPI, STI, TPI, stream density distance processing DEM GIS discussed this paper. These common research papers either or measure their relationship with other environmental factors.•Hydrological great importance understanding landscape research, especially geo-environmental mapping.•Physically based methods engaged ArcMap 10.5 software.•Commonly processed using freely available software.

Language: Английский

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A comparative study of Machine Learning and Deep Learning methods for flood forecasting in the Far-North region, Cameroon

Scientific African, Journal Year: 2023, Volume and Issue: 23, P. e02053 - e02053

Published: Dec. 27, 2023

Flood crises are the consequence of climate change and global warming, which lead to an increase in frequency intensity heavy rainfall. Floods are, remain, natural disasters that result huge loss lives material damage. risks threaten all countries globe general. The Far-North region Cameroon has suffered flood on several occasions, resulting significant human lives, infrastructural socio-economic damage, with destruction homes, crops grazing areas, halting economic activities. models used for forecasting this generally physical-based, produce unsatisfactory results. use artificial intelligence based methods order limit its consequences is a way be explored Cameroon. aims present research work design compare performance Machine Learning Deep such as one dimensional Convolutional Neural Network, Long Short Term Memory Multi Layer Perceptron short-term long-term designed take input temperature rainfall time series recorded region. Performance criteria evaluating Nash–SutcliffeEfficiency, Percent Bias, Coefficient Determination Root Mean Squared Error. As results comparison models, best model LSTM , still model. obtained from comparisons have satisfactory good generalization capabilities, reflected by criteria. our can implementation floods warning systems definition effective efficient risk management policies make more resilient crises.

Language: Английский

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Natural Disaster Risk Inequalities in Central America

Papers in Applied Geography, Journal Year: 2022, Volume and Issue: 9(1), P. 36 - 48

Published: May 24, 2022

Central America is affected by geological and hydrometeorological hazards that, together with its high exposure vulnerability, comprise risky scenarios for disasters. This region presents a significant number of casualties economic losses due to disasters every year. We present an analysis the origin extensive risks (high-frequency-low-magnitude occurrences) intensive (low-frequency-high magnitude hazard in from 1990 2015 using disaster databases EM-DAT DesInventar. Findings reveal that Costa Rica reported greatest both (disaster whereas El Salvador, Guatemala, Honduras experienced highest terms injuries lost, as well damaged or destroyed houses risks. Disaster databases, like ones employed this research, provide useful data risk assessment, land use planning, management developing countries. study stresses need exhaustive assessment at local, regional, national scales.

Language: Английский

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