Urban flood depth prediction using an improved LSTM model incorporating precipitation forecasting

Natural Hazards, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 29, 2025

Language: Английский

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A surrogate machine learning modeling approach for enhancing the efficiency of urban flood modeling at metropolitan scales

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: 123, P. 106277 - 106277

Published: March 11, 2025

Language: Английский

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Research Progress and Prospects of Urban Flooding Simulation: From Traditional Numerical Models to Deep Learning Approaches

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: unknown, P. 106213 - 106213

Published: Sept. 1, 2024

Language: Английский

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Automated detection of underwater cracks based on fusion of optical and texture information

Engineering Structures, Journal Year: 2024, Volume and Issue: 315, P. 118515 - 118515

Published: June 28, 2024

Language: Английский

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Using Explainable Artificial Intelligence (Xai) to Understand Compound Flooding Arising from Rainstorms and Tides

Published: Jan. 1, 2025

Language: Английский

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Using the TSA-LSTM two-stage model to predict cancer incidence and mortality

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0317148 - e0317148

Published: Feb. 20, 2025

Cancer, the second-leading cause of mortality, kills 16% people worldwide. Unhealthy lifestyles, smoking, alcohol abuse, obesity, and a lack exercise have been linked to cancer incidence mortality. However, it is hard. Cancer lifestyle correlation analysis mortality prediction in next several years are used guide people's healthy lives target medical financial resources. Two key research areas this paper Data preprocessing sample expansion design Using experimental comparison, study chooses best cubic spline interpolation technology on original data from 32 entry points 420 converts annual into monthly solve problem insufficient prediction. Factor possible because sources indicate changing factors. TSA-LSTM Two-stage attention popular tool with advanced visualization functions, Tableau, simplifies paper's study. Tableau's testing findings cannot analyze predict time series data. LSTM utilized by optimization model. By commencing input feature attention, model technique guarantees that encoder converges subset sequence features during output features. As result, model's natural learning trend quality enhanced. The second step, performance maintains We can choose network improve forecasts based real-time performance. Validating source factor using Most cancers overlapping risk factors, excessive drinking, exercise, obesity breast, colorectal, colon cancer. A poor directly promotes lung, laryngeal, oral cancers, according visual tests. expected climb 18-21% between 2020 2025, 2021. Long-term projection accuracy 98.96 percent, smoking may be main causes.

Language: Английский

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U-RNN high-resolution spatiotemporal nowcasting of urban flooding

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133117 - 133117

Published: April 1, 2025

Language: Английский

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SwinFlood: A hybrid CNN-Swin Transformer model for rapid spatiotemporal flood simulation

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133280 - 133280

Published: April 1, 2025

Language: Английский

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AI-Driven Framework for Assessing Visitor Perceptions in Historic Urban Areas Using Social Media

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 22, 2025

As urbanization progresses, preserving and adapting historical districts for sustainable development is crucial. These areas embody significant cultural heritage contribute to economic, social, sustainability. However, research on visitor perceptions, particularly spatial satisfaction, limited, especially in fine-grained analyses using social media data. This study introduces a framework evaluating perceptions Aspect-Based Sentiment Analysis (ABSA) enhanced by BO-DXGBoost model, cascaded system combining two XGBoost models fine-tuned through Bayesian Optimization (BO). The first model identifies aspect categories, while the second analyzes sentiment polarity intensity. Class imbalance addressed ADASYN RF-SMOTE, SHAP analysis visualizes feature influences predictions. provides quantitative insights into of offers robust approach management integration

Language: Английский

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Integrating numerical models with deep learning techniques for flood risk assessment

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 14, 2025

Language: Английский

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