Forecasting Multi-Step-Ahead Street-Scale nuisance flooding using seq2seq LSTM surrogate model for Real-Time applications in a Coastal-Urban city

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132697 - 132697

Опубликована: Янв. 1, 2025

Язык: Английский

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Assessment of Urban Flood Disaster Responses and Causal Analysis at Different Temporal Scales Based on Social Media Data and Machine Learning Algorithms

International Journal of Disaster Risk Reduction, Год журнала: 2025, Номер 117, С. 105170 - 105170

Опубликована: Янв. 5, 2025

Язык: Английский

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Exploring machine learning potential for climate change risk assessment

Earth-Science Reviews, Год журнала: 2021, Номер 220, С. 103752 - 103752

Опубликована: Июль 27, 2021

Язык: Английский

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Impacts of building configurations on urban stormwater management at a block scale using XGBoost

Sustainable Cities and Society, Год журнала: 2022, Номер 87, С. 104235 - 104235

Опубликована: Окт. 2, 2022

Язык: Английский

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Urban flood susceptibility mapping based on social media data in Chengdu city, China

Sustainable Cities and Society, Год журнала: 2022, Номер 88, С. 104307 - 104307

Опубликована: Ноя. 17, 2022

Increase in urban flood hazards has become a major threat to cities, causing considerable losses of life and the economy. To improve pre-disaster strategies mitigate potential losses, it is important make susceptibility assessments carry out spatiotemporal analyses. In this study, we used standard deviation ellipse (SDE) analyze spatial pattern floods find area interest (AOI) based upon related social media data that were collected Chengdu city, China. We as response variable selected 10 flood-influencing factors independent variables. estimated model using Naïve Bayes (NB) method. The results show events are concentrated northeast-central part especially around city center. Results checked by Receiver Operating Characteristic (ROC) curve, showing under curve (AUC) was equal 0.8299. This validation result confirmed can predict with satisfactory accuracy. map center provides realistic reference for monitoring early warning.

Язык: Английский

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Urban storm water drainage system optimization using a sustainability index and LID/BMPs

Sustainable Cities and Society, Год журнала: 2021, Номер 76, С. 103500 - 103500

Опубликована: Окт. 29, 2021

Язык: Английский

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Logging-data-driven permeability prediction in low-permeable sandstones based on machine learning with pattern visualization: A case study in Wenchang A Sag, Pearl River Mouth Basin

Journal of Petroleum Science and Engineering, Год журнала: 2022, Номер 214, С. 110517 - 110517

Опубликована: Апрель 19, 2022

Язык: Английский

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Greening the city: Thriving for biodiversity and sustainability

The Science of The Total Environment, Год журнала: 2022, Номер 817, С. 153032 - 153032

Опубликована: Янв. 7, 2022

Язык: Английский

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Urban flood risk differentiation under land use scenario simulation

iScience, Год журнала: 2023, Номер 26(4), С. 106479 - 106479

Опубликована: Март 23, 2023

The frequent urban floods have seriously affected the regional sustainable development in recent years. It is significant to understand characteristics of flood risk and reasonably predict under different land use scenarios. This study used random forest multi-criteria decision analysis models assess spatiotemporal Zhengzhou City, China, from 2005 2020, proposed a robust method coupling Bayesian network patch-generating simulation future probability. We found that City presented an upward trend its spatial pattern was "high middle low surrounding areas". In addition, patterns scenario would be more conducive reducing risk. Our results can provide theoretical support for scientifically optimizing improve management.

Язык: Английский

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High temporal resolution urban flood prediction using attention-based LSTM models

Journal of Hydrology, Год журнала: 2023, Номер 620, С. 129499 - 129499

Опубликована: Апрель 10, 2023

Язык: Английский

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