Flood inundation assessment of UNESCO World Heritage Sites using remote sensing and spatial metrics in Hoi An City, Vietnam

Ecological Informatics, Год журнала: 2023, Номер 79, С. 102427 - 102427

Опубликована: Дек. 11, 2023

Flooding is the most frequent and damaging threat to The United Nations Educational, Scientific, Cultural Organization (UNESCO) World Heritage Sites, has been exacerbated by climate change. Hoi An Ancient Town, one of world's cultural heritage sites in Vietnam, facing inundation risks from increasing extreme flood events due anthropogenic natural processes. This study combines Geospatial Information System (GIS), remote sensing data, landscape ecology metrics assess risk City. analysis includes (1) classification land-use/land-cover (LULC) using Sentinel-2 data; (2) computation a index hazard characteristics, physical demographic exposures, socioeconomic vulnerabilities, together with adaptive capacity spatial metrics, Fuzzy-Analytic Hierarchy Process (AHP) method determine weight each ranking; (3) determination for UNESCO Sites City surrounding areas. By comparing model outputs historical locations (N = 330), finds that >75% past floods occurred at high or very high-risk areas forecasted model. Results verified true incidents show hotspots are concentrated city's sites, where human changes have impacted structure. mapping obtained synthesis analysis, encompasses hazard, exposure, vulnerability, offering holistic knowledge help reduce manage Sites. can be applicable other similar characteristics.

Язык: Английский

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Spatial Mapping of Flood Susceptibility Using Decision Tree–Based Machine Learning Models for the Vembanad Lake System in Kerala, India

Journal of Water Resources Planning and Management, Год журнала: 2023, Номер 149(10)

Опубликована: Авг. 4, 2023

Floods have claimed the lives of countless people and caused significant property damage in many countries, putting their livelihoods jeopardy. The Vembanad lake system (VLS) Kerala, India, has faced adverse mishappening during 2018, 2019, 2021 floods state due to torrential rainfall. goal this research is construct effective decision tree–based machine learning models such as adaptive boosting (AdaBoost), random forest (RF), gradient machines (GBMs), extreme (XGBoost) for integrating data, processing, generating flood susceptibility maps. There are 18 conditioning parameters considered, which include seven categories 11 numerical data. These categorical data were converted bringing total amount input 61. recursive feature elimination (RFE) was utilized selection technique, a 22 layers chosen feed into generate efficiencies evaluated using receiver operating characteristic (ROC)–area under ROC curve (AUC), F1 score, accuracy, kappa. According results, performance all four demonstrated practical application; however, XGBoost fared well terms model's metrics. For testing set, ROC-AUC values XGBoost, GBM, AdaBoost 0.90, whereas it 0.89 RF. accuracy varied significantly among models, with scoring 0.92, followed by GBM (0.88), RF (0.87), (0.87). As result, map may be early mitigation actions future floods, land-use planners emergency managers, assisting reduction risk regions prone hazard.

Язык: Английский

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Assessing urban pluvial waterlogging resilience based on sewer congestion risk and climate change impacts

Journal of Hydrology, Год журнала: 2023, Номер 626, С. 130230 - 130230

Опубликована: Окт. 2, 2023

Язык: Английский

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Comparative prioritization of sub-watersheds based on Flood Generation potential using physical, hydrological and co-managerial approaches

Water Resources Management, Год журнала: 2022, Номер 36(6), С. 1897 - 1917

Опубликована: Апрель 1, 2022

Язык: Английский

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A GIS-Based Comparative Analysis of Frequency Ratio and Statistical Index Models for Flood Susceptibility Mapping in the Upper Krishna Basin, India

Water, Год журнала: 2022, Номер 14(22), С. 3771 - 3771

Опубликована: Ноя. 20, 2022

The Upper Krishna Basin in Maharashtra (India) is highly vulnerable to floods. This study aimed generate a flood susceptibility map for the basin using Frequency Ratio and Statistical Index models of analysis. hazard inventory was created by 370 locations plotted ArcGIS 10.1 software. 259 (70%) were selected randomly as training samples analysis models, validation purposes, remaining 111 (30%) used. Flood analyses performed based on 12 conditioning factors. These elevation, slope, aspect, curvature, Topographic Wetness Index, Stream Power rainfall, distance from river, stream density, soil types, land use, road. model revealed that 38% area high- very-high-flood-susceptibility class. precision confirmed receiver operating characteristic under curve value method. showed 66.89% success rate 68% prediction model. However, provided an 82.85% 83.23% rate. comparative most suitable mapping flood-prone areas Basin. results obtained this research can be helpful disaster mitigation preparedness

Язык: Английский

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