How accurate are the machine learning models in improving monthly rainfall prediction in hyper arid environment?

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 633, P. 131040 - 131040

Published: March 11, 2024

Language: Английский

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Modeling streamflow in non-gauged watersheds with sparse data considering physiographic, dynamic climate, and anthropogenic factors using explainable soft computing techniques

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 631, P. 130846 - 130846

Published: Feb. 7, 2024

Language: Английский

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A critical review for the application of cutting-edge digital visualisation technologies for effective urban flood risk management

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 99, P. 104958 - 104958

Published: Sept. 24, 2023

Cutting-edge digital visualisation tools (CDVT) are playing an increasingly important role in improving urban flood risk management. However, there is a paucity of comprehensive research examining their across all stages To address, this study conducts integrated critical review to identify the application CDVT and assess contribution prevention, mitigation, preparation, response, recovery The results show that virtual reality, augmented twin technologies primary used visualisation, with reality being most frequently used. focus studies has been primarily on preparation mitigation stages. need investigate these entire water cycle. Furthermore, potential for greater adoption twin, especially simulating inundation evacuation routes. Integrating real-time data, data-driven modeling, can significantly improve forecasting. This benefits stakeholders public by enhancing early warning systems, preparedness, resilience, leading more effective management reduced impacts communities.

Language: Английский

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Stakeholder analysis in the application of cutting-edge digital visualisation technologies for urban flood risk management: A critical review

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 236, P. 121426 - 121426

Published: Sept. 4, 2023

Cutting-edge flood visualisation technologies are becoming increasingly important in managing urban risks, particularly from the perspective of stakeholders who play a crucial role controlling and reducing risks associated with events. This review study provides comprehensive overview stakeholder analysis this context, highlighting gaps current research paving way for future investigations. For purpose, scientific literature critical conducted based on identified relevant works to map mutual context. categorises cutting-edge into four groups - virtual reality, augmented mixed digital twin explores their adoption engaging various across five key stages risk management: prevention, mitigation, preparation, response, recovery. Results show that existing has primarily concentrated support water utilities communication general public. However, there is noticeable gap regarding engagement such as policy-makers, researchers, insurance providers. Furthermore, highlights disparities involvement damage assessment studies, lack representation policy-makers researchers. Finally, introduces concept overlooked interconnected impacts they have, which received relatively little attention previous research.

Language: Английский

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Prediction of spatial-temporal flood water level in agricultural fields using advanced machine learning and deep learning approaches

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

Published: Jan. 17, 2025

Language: Английский

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A critical review of digital technology innovations for early warning of water-related disease outbreaks associated with climatic hazards

International Journal of Disaster Risk Reduction, Journal Year: 2023, Volume and Issue: 100, P. 104151 - 104151

Published: Nov. 28, 2023

Water-related climatic disasters pose a significant threat to human health due the potential of disease outbreaks, which are exacerbated by climate change. Therefore, it is crucial predict their occurrence with sufficient lead time allow for contingency plans reduce risks population. Opportunities address this challenge can be found in rapid evolution digital technologies. This study conducted critical analysis recent publications investigating advanced technologies and innovations forecasting, alerting, responding water-related extreme events, particularly flooding, often linked disaster-related outbreaks. The results indicate that certain innovations, such as portable local sensors integrated web-based platforms new era predicting developing control strategies establishing early warning systems. Other technologies, augmented reality, virtual social media, more effective monitoring flood spread, disseminating before/during event information, issuing warnings or directing emergency responses. also identified collection translation reliable data into information major systems adoption disaster management. Augmented twin should further explored valuable tools better providing communicating complex on development response wider range audiences, non-experts. help increase community engagement designing operating impact disasters.

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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Enhancing Flood Risk Mitigation by Advanced Data-Driven Approach

Heliyon, Journal Year: 2024, Volume and Issue: 10(18), P. e37758 - e37758

Published: Sept. 1, 2024

Language: Английский

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Enhancing urban flood forecasting in drainage systems using dynamic ensemble-based data mining

Water Research, Journal Year: 2023, Volume and Issue: 247, P. 120791 - 120791

Published: Oct. 27, 2023

This study presents a novel approach for urban flood forecasting in drainage systems using dynamic ensemble-based data mining model which has yet to be utilised properly this context. The proposed method incorporates an event identification technique and rainfall feature extraction develop weak learner models. These models are then stacked create time-series ensemble decision tree algorithm confusion matrix-based blending method. was compared other commonly used real-world system the UK. results show that achieves higher hit rate benchmark models, with of around 85% vs 70 % next 3 h forecasting. Additionally, smart can accurately classify various timesteps or non-flood events without significant lag times, resulting fewer false alarms, reduced unnecessary risk management actions, lower costs real-time early warning applications. findings also demonstrate two features, "antecedent precipitation history" "seasonal time occurrence rainfall," significantly enhance accuracy ranging from 60 10 lead 15 min h.

Language: Английский

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Geographic heterogeneity of activation functions in urban real-time flood forecasting: Based on seasonal trend decomposition using Loess-Temporal Convolutional Network-Gated Recurrent Unit model

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 636, P. 131279 - 131279

Published: May 7, 2024

Language: Английский

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