Flood risk and shelter suitability mapping using geospatial technique for sustainable urban flood management: a case study in Palembang city, South Sumatera, Indonesia DOI Creative Commons
Muhammad Rendana, Wan Mohd Razi Idris, Sahibin Abdul Rahim

et al.

Geology Ecology and Landscapes, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 11

Published: April 24, 2023

The populous city of Palembang is one the most flood-prone cities in Indonesian region. After some decades, magnitude, duration, and frequency floods have increased. Thus, this study aimed to develop flood risk shelter suitability maps using Analytic Hierarchy Process (AHP) Geographical Information System (GIS) integration. Several flood-related factors that used such as elevation, population, slope, land cover, distance from a river, drainage density, road, settlement, soil type. Results found map area was divided into three classes; 30.3% at high risk, while 60.5% moderate 9.2% low risk. Moreover, assessments revealed approximately 4.1% shelters were highly suitable, 19.4% moderately lowly 16.1% very suitable. highest areas predominantly on northwest north sides which higher elevation (ranging 13–41 m) farther river. They could be assumed good choices for shelters.

Language: Английский

Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations DOI
Halit Enes Aydin, Muzaffer Can İban

Natural Hazards, Journal Year: 2022, Volume and Issue: 116(3), P. 2957 - 2991

Published: Dec. 20, 2022

Language: Английский

Citations

86
Assessing urban flooding risk in response to climate change and urbanization based on shared socio-economic pathways DOI
Mo Wang,

Xiaoping Fu,

Dongqing Zhang

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 880, P. 163470 - 163470

Published: April 17, 2023

Language: Английский

Citations

60
Assessing the scale effect of urban vertical patterns on urban waterlogging: An empirical study in Shenzhen DOI

Yuqin Huang,

Jinyao Lin, Xiaoyu He

et al.

Environmental Impact Assessment Review, Journal Year: 2024, Volume and Issue: 106, P. 107486 - 107486

Published: March 8, 2024

Language: Английский

Citations

41
Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm DOI
Jatan Debnath,

Jimmi Debbarma,

Amal Debnath

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(2)

Published: Jan. 4, 2024

Language: Английский

Citations

20
Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm DOI Creative Commons
Md. Abdullah Al Mamun, Mou Rani Sarker, Md Abdur Rouf Sarkar

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 4, 2024

Abstract Droughts pose a severe environmental risk in countries that rely heavily on agriculture, resulting heightened levels of concern regarding food security and livelihood enhancement. Bangladesh is highly susceptible to hazards, with droughts further exacerbating the precarious situation for its 170 million inhabitants. Therefore, we are endeavouring highlight identification relative importance climatic attributes estimation seasonal intensity frequency Bangladesh. With period forty years (1981–2020) weather data, sophisticated machine learning (ML) methods were employed classify 35 agroclimatic regions into dry or wet conditions using nine parameters, as determined by Standardized Precipitation Evapotranspiration Index (SPEI). Out 24 ML algorithms, four best methods, ranger, bagEarth, support vector machine, random forest (RF) have been identified prediction multi-scale drought indices. The RF classifier Boruta algorithms shows water balance, precipitation, maximum minimum temperature higher influence occurrence across trend spatio-temporal analysis indicates, has decreased over time, but return time increased. There was significant variation changing spatial nature intensity. Spatially, shifted from northern central southern zones Bangladesh, which had an adverse impact crop production rural urban households. So, this precise study important implications understanding how mitigate impacts. Additionally, emphasizes need better collaboration between relevant stakeholders, such policymakers, researchers, communities, local actors, develop effective adaptation strategies increase monitoring meticulous management

Language: Английский

Citations

17
Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas DOI Creative Commons
Youssef Bammou, Brahim Benzougagh, Brahim Igmoullan

et al.

Natural Hazards, Journal Year: 2024, Volume and Issue: 120(8), P. 7787 - 7816

Published: March 21, 2024

Abstract This study explores and compares the predictive capabilities of various ensemble algorithms, including SVM, KNN, RF, XGBoost, ANN, DT, LR, for assessing flood susceptibility (FS) in Houz plain Moroccan High Atlas. The inventory map past flooding was prepared using binary data from 2012 events, where “1” indicates a flood-prone area “0” non-flood-prone or extremely low area, with 762 indicating areas. 15 different categorical factors were determined selected based on importance multicollinearity tests, slope, elevation, Normalized Difference Vegetation Index, Terrain Ruggedness Stream Power Land Use Cover, curvature plane, profile, aspect, flow accumulation, Topographic Position soil type, Hydrologic Soil Group, distance river rainfall. Predicted FS maps Tensift watershed show that, only 10.75% mean surface predicted as very high risk, 19% 38% estimated respectively. Similarly, Haouz plain, exhibited an average 21.76% very-high-risk zones, 18.88% 18.18% low- very-low-risk zones applied algorithms met validation standards, under curve 0.93 0.91 learning stages, Model performance analysis identified XGBoost model best algorithm zone mapping. provides effective decision-support tools land-use planning risk reduction, across globe at semi-arid regions.

Language: Английский

Citations

16
Threats of climate change and land use patterns enhance the susceptibility of future floods in India DOI
Subodh Chandra Pal, Indrajit Chowdhuri, Biswajit Das

et al.

Journal of Environmental Management, Journal Year: 2021, Volume and Issue: 305, P. 114317 - 114317

Published: Dec. 24, 2021

Language: Английский

Citations

76
A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications DOI Open Access
Hakan Başağaoğlu, Debaditya Chakraborty,

Cesar Do Lago

et al.

Water, Journal Year: 2022, Volume and Issue: 14(8), P. 1230 - 1230

Published: April 11, 2022

This review focuses on the use of Interpretable Artificial Intelligence (IAI) and eXplainable (XAI) models for data imputations numerical or categorical hydroclimatic predictions from nonlinearly combined multidimensional predictors. The AI considered in this paper involve Extreme Gradient Boosting, Light Categorical Extremely Randomized Trees, Random Forest. These can transform into XAI when they are coupled with explanatory methods such as Shapley additive explanations local interpretable model-agnostic explanations. highlights that IAI capable unveiling rationale behind while discovering new knowledge justifying AI-based results, which critical enhanced accountability AI-driven predictions. also elaborates importance domain interventional modeling, potential advantages disadvantages hybrid non-IAI predictive unequivocal balanced decisions, choice performance versus physics-based modeling. concludes a proposed framework to enhance interpretability explainability applications.

Language: Английский

Citations

55
Strengthened tropical cyclones and higher flood risk under compound effect of climate change and urbanization across China's Greater Bay Area DOI
Zifeng Deng,

Zhaoli Wang,

Xushu Wu

et al.

Urban Climate, Journal Year: 2022, Volume and Issue: 44, P. 101224 - 101224

Published: July 1, 2022

Language: Английский

Citations

51
Assessment of groundwater potential modeling using support vector machine optimization based on Bayesian multi-objective hyperparameter algorithm DOI

Duong Tran Anh,

Manish Pandey, Varun Narayan Mishra

et al.

Applied Soft Computing, Journal Year: 2022, Volume and Issue: 132, P. 109848 - 109848

Published: Nov. 25, 2022

Language: Английский

Citations

51