National-scale flood risk assessment using GIS and remote sensing-based hybridized deep neural network and fuzzy analytic hierarchy process models: a case of Bangladesh DOI
Zakaria Shams Siam, Rubyat Tasnuva Hasan,

Soumik Sarker Anik

et al.

Geocarto International, Journal Year: 2022, Volume and Issue: 37(26), P. 12119 - 12148

Published: April 6, 2022

Assessing flood risk is challenging due to complex interactions among susceptibility, hazard, exposure, and vulnerability parameters. This study presents a novel assessment framework by utilizing hybridized deep neural network (DNN) fuzzy analytic hierarchy process (AHP) models. Bangladesh was selected as case region, where limited studies examined at national scale. The results exhibited that DNN AHP models can produce the most accurate map while comparing 15 different About 20.45% of are zones moderate, high, very high severity. northeastern well areas adjacent Ganges–Brahmaputra–Meghna rivers, have damage potential, significant number people were affected during 2020 event. developed in this would help policymakers formulate comprehensive management system.

Language: Английский

A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications DOI Creative Commons
Yuzhen Zhang, Jingjing Liu, Wenjuan Shen

et al.

Applied Sciences, Journal Year: 2022, Volume and Issue: 12(17), P. 8654 - 8654

Published: Aug. 29, 2022

Machine learning algorithms are increasingly used in various remote sensing applications due to their ability identify nonlinear correlations. Ensemble have been included many practical improve prediction accuracy. We provide an overview of three widely ensemble techniques: bagging, boosting, and stacking. first the underlying principles present analysis current literature. summarize some typical algorithms, which include predicting crop yield, estimating forest structure parameters, mapping natural hazards, spatial downscaling climate parameters land surface temperature. Finally, we suggest future directions for using applications.

Language: Английский

Citations

183
Land use/land cover prediction and analysis of the middle reaches of the Yangtze River under different scenarios DOI
Shengqing Zhang, Peng Yang, Jun Xia

et al.

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 833, P. 155238 - 155238

Published: April 13, 2022

Language: Английский

Citations

142
Impacts of disaster and land-use change on food security and adaptation: Evidence from the delta community in Bangladesh DOI
Afshana Parven, Indrajit Pal, Apichon Witayangkurn

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2022, Volume and Issue: 78, P. 103119 - 103119

Published: June 17, 2022

Language: Английский

Citations

73
Flash Flood Susceptibility Assessment and Zonation by Integrating Analytic Hierarchy Process and Frequency Ratio Model with Diverse Spatial Data DOI Open Access
Aqil Tariq, Jianguo Yan, Bushra Ghaffar

et al.

Water, Journal Year: 2022, Volume and Issue: 14(19), P. 3069 - 3069

Published: Sept. 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.

Language: Английский

Citations

73
The extraordinary Zhengzhou flood of 7/20, 2021: How extreme weather and human response compounding to the disaster DOI

Xiaona Guo,

Jie Cheng, Chenglong Yin

et al.

Cities, Journal Year: 2022, Volume and Issue: 134, P. 104168 - 104168

Published: Dec. 30, 2022

Language: Английский

Citations

70
An XGBoost-SHAP approach to quantifying morphological impact on urban flooding susceptibility DOI Creative Commons
Mo Wang, Yingxin Li, Haojun Yuan

et al.

Ecological Indicators, Journal Year: 2023, Volume and Issue: 156, P. 111137 - 111137

Published: Oct. 29, 2023

Urban flooding risks, often overlooked by conventional methods, can be profoundly affected city configurations. However, explainable Artificial Intelligence could provide insights into how urban configurations flooding. This study, taking entered on Shenzhen City, deploys an XGBoost, integrating SHapley Additive exPlanation and Partial Dependency Plots, to assess morphology influences susceptibility. The models strategies presented in this study aimed adapt extreme storms from the perspective of spatial configuration planning. findings underscore varying impact disaster variables flooding, with morphological attributes becoming highly significant during severe inundations. In analysis, mean building volume emerged as a pivotal parameter, SHAP value 0.0107 m contribution ratio 9.70 %. indicates that should optimized minimize risks. It is recommended Mean Building Volume (MBV) maintained within range 1.25 km3 2.5 km3, Standard Deviation (SDBV) kept below 2.814 km3. By harnessing algorithms, offers intricate relationship between forms flood risk, thereby informing development effective adaptation strategies.

Language: Английский

Citations

65
A review of recent advances in urban flood research DOI Creative Commons
Candace Agonafir, Tarendra Lakhankar,

R. Khanbilvardi

et al.

Water Security, Journal Year: 2023, Volume and Issue: 19, P. 100141 - 100141

Published: July 13, 2023

Due to a changing climate and increased urbanization, an escalation of urban flooding occurrences its aftereffects are ever more dire. Notably, the frequency extreme storms is expected increase, as built environments impede absorption water, threat loss human life property damages exceeding billions dollars heightened. Hence, agencies organizations implementing novel modeling methods combat consequences. This review details concepts, impacts, causes flooding, along with associated endeavors. Moreover, this describes contemporary directions towards flood resolutions, including recent hydraulic-hydrologic models that use modern computing architecture trending applications artificial intelligence/machine learning techniques crowdsourced data. Ultimately, reference utility provided, scientists engineers given outline advances in research.

Language: Английский

Citations

61
Prediction of flash flood susceptibility using integrating analytic hierarchy process (AHP) and frequency ratio (FR) algorithms DOI Creative Commons
Muhammad Majeed, Linlin Lu, Muhammad Mushahid Anwar

et al.

Frontiers in Environmental Science, Journal Year: 2023, Volume and Issue: 10

Published: Jan. 5, 2023

The landscape of Pakistan is vulnerable to flood and periodically affected by floods different magnitudes. aim this study was aimed assess the flash susceptibility district Jhelum, Punjab, using geospatial model Frequency Ratio Analytical Hierarchy Process. Also, considered eight most influential flood-causing parameters are Digital Elevation Model, slop, distance from river, drainage density, Land use/Land cover, geology, soil resistivity (soil consisting rocks formation) rainfall deviation. data collected weather stations in vicinity area. Estimated weight allotted each flood-inducing factors with help AHP FR. Through use overlay analysis, were brought together, value density awarded maximum possible score. According several areas region based on have been classified zones viz, very high risk, moderate low risk. In light results obtained, 4% area that accounts for 86.25 km 2 at risk flood. like Bagham, Sohawa, Domeli, Turkai, Jogi Tillas, Chang Wala, Dandot Khewra located elevation. Whereas Potha, Samothi, Chaklana, Bagrian, Tilla Jogian, Nandna, Rawal high-risk damaged badly history This first its kind conducted Jhelum District provides guidelines disaster management authorities response agencies, infrastructure planners, watershed management, climatologists.

Language: Английский

Citations

51
Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data DOI Creative Commons
Aqil Tariq,

Yan Jiango,

Qingting Li

et al.

Heliyon, Journal Year: 2023, Volume and Issue: 9(2), P. e13212 - e13212

Published: Jan. 26, 2023

The present study is designed to monitor the spatio-temporal changes in forest cover using Remote Sensing (RS) and Geographic Information system (GIS) techniques from 1990 2017. Landsat data (Thematic mapper [TM]), 2000 2010 (Enhanced Thematic Mapper [ETM+]), 2013 2017 (Operational Land Imager/Thermal Infrared Sensor [OLI/TIRS]) were classified into classes termed snow, water, barren land, built-up area, forest, vegetation. method was built multitemporal images machine learning Support Vector Machine (SVM), Naive Bayes Tree (NBT) Kernel Logistic Regression (KLR). According results, area decreased 19,360 km2 (26.0%) 18,784 (25.2%) 2010, while increased 18,640 (25.0%) 26,765 (35.9%) due "One billion tree Project". our findings, SVM performed better than KLR NBT on all three accuracy metrics (recall, precision, accuracy) F1 score >0.89. demonstrated that concurrent reforestation land areas improved methods of sustaining RS GIS everyday forestry organization practices Khyber Pakhtun Khwa (KPK), Pakistan. results beneficial, especially at decision-making level for local or provincial government KPK understanding global scenario regional planning.

Language: Английский

Citations

51
Cost of high-level flooding as a consequence of climate change driver?: A case study of China’s flood-prone regions DOI Creative Commons
Md. Ziaul Islam, Chao Wang

Ecological Indicators, Journal Year: 2024, Volume and Issue: 160, P. 111944 - 111944

Published: March 1, 2024

The extent of flooding in China is more significant than any other country. Our research reveals that approximately 66 % China's landmass submerged by flooding, affecting about 50 the population. Furthermore, financial toll now accounts for 1.42 annual gross domestic product (GDP), which almost 40 times higher corresponding figure United States. We have observed Zhengzhou city Henan province, faced a devastating flood 2021, received amount rainfall, specifically total 552.5 mm within 24-hour period. floods province 2021 caused considerable damage, including impacting nearly 15 million people, resulting 400 deaths, damaging over 10,000 square kilometers agricultural land, causing $19 billion economic losses, and leading to collapse 35,000 households damage various properties. In similar manner, occurred southern 2020 impacted 7.1 individuals across eight provinces resulted 54 fatalities, 6,700 houses, incurred direct loss US$3.33 billion. found rainstorms significantly increased 10 last 60 years China. this paper, we delved into exploring existing published articles, reports, government authoritative legal texts analyze causes impacts flood-prone regions potential mitigation strategies reduce repercussions distressing events. believe study will help policymakers providing new insights while formulating policy high-level flooding.

Language: Английский

Citations

22