Flooding and its relationship with land cover change, population growth, and road density

Geoscience Frontiers, Год журнала: 2021, Номер 12(6), С. 101224 - 101224

Опубликована: Май 5, 2021

Bangladesh experiences frequent hydro-climatic disasters such as flooding. These are believed to be associated with land use changes and climate variability. However, identifying the factors that lead flooding is challenging. This study mapped flood susceptibility in northeast region of using Bayesian regularization back propagation (BRBP) neural network, classification regression trees (CART), a statistical model (STM) evidence belief function (EBF), their ensemble models (EMs) for three time periods (2000, 2014, 2017). The accuracy machine learning algorithms (MLAs), STM, EMs were assessed by considering area under curve—receiver operating characteristic (AUC-ROC). Evaluation levels aforementioned revealed EM4 (BRBP-CART-EBF) outperformed (AUC > 90%) standalone other analyzed. Furthermore, this investigated relationships among cover change (LCC), population growth (PG), road density (RD), relative (RCF) areas period between 2000 2017. results showed very high increased 19.72% 2017, while PG rate 51.68% over same period. Pearson correlation coefficient RCF RD was calculated 0.496. findings highlight significant association floods causative factors. could valuable policymakers resource managers they can improvements management reduction damage risks.

Язык: Английский

---

Hydrogeochemical Evaluation of Groundwater Aquifers and Associated Health Hazard Risk Mapping Using Ensemble Data Driven Model in a Water Scares Plateau Region of Eastern India

Exposure and Health, Год журнала: 2022, Номер 15(1), С. 113 - 131

Опубликована: Апрель 23, 2022

Язык: Английский

---

A comparative assessment of flood susceptibility modelling of GIS-based TOPSIS, VIKOR, and EDAS techniques in the Sub-Himalayan foothills region of Eastern India

Environmental Science and Pollution Research, Год журнала: 2022, Номер 30(6), С. 16036 - 16067

Опубликована: Сен. 30, 2022

Язык: Английский

---

Flash Flood Susceptibility Assessment and Zonation by Integrating Analytic Hierarchy Process and Frequency Ratio Model with Diverse Spatial Data

Water, Год журнала: 2022, Номер 14(19), С. 3069 - 3069

Опубликована: Сен. 29, 2022

Flash floods are the most dangerous kinds of because they combine destructive power a flood with incredible speed. They occur when heavy rainfall exceeds ability ground to absorb it. The main aim this study is generate flash maps using Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) models in river’s floodplain between Jhelum River Chenab rivers. A total eight flood-causative physical parameters considered for study. Six based on remote sensing images Advanced Land Observation Satellite (ALOS), Digital Elevation Model (DEM), Sentinel-2 Satellite, which include slope, elevation, distance from stream, drainage density, flow accumulation, land use/land cover (LULC), respectively. other two soil geology, consist different rock formations, In case AHP, each criteria allotted an estimated weight according its significant importance occurrence floods. end, all were integrated weighted overlay analysis influence value density was given highest weight. shows that 2500 m river has values FR ranging 0.54, 0.56, 1.21, 1.26, 0.48, output zones categorized into very low, moderate, high, high risk, covering 7354, 5147, 3665, 2592, 1343 km2, Finally, results show areas or 6.68% area. Mangla, Marala, Trimmu valleys identified as high-risk area, have been damaged drastically many times by It provides policy guidelines risk managers, emergency disaster response services, urban infrastructure planners, hydrologists, climate scientists.

Язык: Английский

---

Flood susceptible prediction through the use of geospatial variables and machine learning methods

Journal of Hydrology, Год журнала: 2023, Номер 617, С. 129121 - 129121

Опубликована: Янв. 13, 2023

Язык: Английский

---

Enhancing flood risk assessment through integration of ensemble learning approaches and physical-based hydrological modeling

Geomatics Natural Hazards and Risk, Год журнала: 2023, Номер 14(1)

Опубликована: Май 4, 2023

This study aims to examine three machine learning (ML) techniques, namely random forest (RF), LightGBM, and CatBoost for flooding susceptibility maps (FSMs) in the Vietnamese Vu Gia-Thu Bon (VGTB). The results of ML are compared with those rainfall-runoff model, different training dataset sizes utilized performance assessment. Ten independent factors assessed. An inventory map approximately 850 sites is based on several post-flood surveys. randomly split between (70%) testing (30%). AUC-ROC 97.9%, 99.5%, 99.5% CatBoost, RF, respectively. FSMs developed by methods show good agreement terms an extension flood inundation using model. models' showed 10–13% total area be highly susceptible flooding, consistent RRI's map. that downstream areas (both urbanized agricultural) under high very levels susceptibility. Additionally, input datasets tested determine least number data points having acceptable reliability. demonstrate can realistically predict FSMs, regardless samples.

Язык: Английский

---

Enhancing flood susceptibility modeling using multi-temporal SAR images, CHIRPS data, and hybrid machine learning algorithms

The Science of The Total Environment, Год журнала: 2023, Номер 871, С. 162066 - 162066

Опубликована: Фев. 10, 2023

Язык: Английский

---

Application of genetic algorithm in optimization parallel ensemble-based machine learning algorithms to flood susceptibility mapping using radar satellite imagery

The Science of The Total Environment, Год журнала: 2023, Номер 873, С. 162285 - 162285

Опубликована: Фев. 17, 2023

Язык: Английский

---

Mapping the landscape and roadmap of geospatial artificial intelligence (GeoAI) in quantitative human geography: An extensive systematic review

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 128, С. 103734 - 103734

Опубликована: Март 11, 2024

This paper brings a comprehensive systematic review of the application geospatial artificial intelligence (GeoAI) in quantitative human geography studies, including subdomains cultural, economic, political, historical, urban, population, social, health, rural, regional, tourism, behavioural, environmental and transport geography. In this extensive review, we obtain 14,537 papers from Web Science relevant fields select 1516 that identify as studies using GeoAI via scanning conducted by several research groups around world. We outline applications systematically summarising number publications over years, empirical across countries, categories data sources used applications, their modelling tasks different subdomains. find out existing have limited capacity to monitor complex behaviour examine non-linear relationship between its potential drivers—such limits can be overcome models with handle complexity. elaborate on current progress status within each subdomain geography, point issues challenges, well propose directions opportunities for future context sustainable open science, generative AI, quantum revolution.

Язык: Английский

---

Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability

Journal of Hydrology, Год журнала: 2020, Номер 595, С. 125663 - 125663

Опубликована: Окт. 27, 2020

Язык: Английский

---