Appraisal of Urban Waterlogging and Extent Damage Situation after the Devastating Flood

Water Resources Management, Год журнала: 2024, Номер 38(12), С. 4911 - 4931

Опубликована: Июнь 8, 2024

Язык: Английский

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The Implementation of Multimodal Large Language Models for Hydrological Applications: A Comparative Study of GPT-4 Vision, Gemini, LLaVa, and Multimodal-GPT

Hydrology, Год журнала: 2024, Номер 11(9), С. 148 - 148

Опубликована: Сен. 11, 2024

Large Language Models (LLMs) combined with visual foundation models have demonstrated significant advancements, achieving intelligence levels comparable to human capabilities. This study analyzes the latest Multimodal LLMs (MLLMs), including Multimodal-GPT, GPT-4 Vision, Gemini, and LLaVa, a focus on hydrological applications such as flood management, water level monitoring, agricultural discharge, pollution management. We evaluated these MLLMs hydrology-specific tasks, testing their response generation real-time suitability in complex real-world scenarios. Prompts were designed enhance models’ inference capabilities contextual comprehension from images. Our findings reveal that Vision exceptional proficiency interpreting data, providing accurate assessments of severity quality. Additionally, showed potential various applications, drought prediction, streamflow forecasting, groundwater wetland conservation. These can optimize resource management by predicting rainfall, evaporation rates, soil moisture levels, thereby promoting sustainable practices. research provides valuable insights into advanced AI addressing challenges improving decision-making

Язык: Английский

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Hydrological and hydrodynamic modelling for flood management: A case study of the Yamuna River Basin in Delhi

Journal of Hydrology Regional Studies, Год журнала: 2024, Номер 56, С. 101960 - 101960

Опубликована: Сен. 17, 2024

Язык: Английский

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A novel framework for evidence-based assessment of flood resilience integrating multi-source evidence: A case study of the Yangtze River Economic Belt, China

Ecological Indicators, Год журнала: 2024, Номер 167, С. 112705 - 112705

Опубликована: Окт. 1, 2024

Язык: Английский

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Urban flood management from the lens of social media data using machine learning algorithms

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132750 - 132750

Опубликована: Янв. 1, 2025

Язык: Английский

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Urban Road Anomaly Monitoring Using Vision–Language Models for Enhanced Safety Management

Applied Sciences, Год журнала: 2025, Номер 15(5), С. 2517 - 2517

Опубликована: Фев. 26, 2025

Abnormal phenomena on urban roads, including uneven surfaces, garbage, traffic congestion, floods, fallen trees, fires, and accidents, present significant risks to public safety infrastructure, necessitating real-time monitoring early warning systems. This study develops Urban Road Anomaly Visual Large Language Models (URA-VLMs), a generative AI-based framework designed for the of diverse road anomalies. The InternVL was selected as foundational model due its adaptability this purpose. URA-VLMs features dedicated modules anomaly detection, flood depth estimation, level assessment, utilizing multi-step prompting retrieval-augmented generation (RAG) precise adaptive analysis. A comprehensive dataset 3034 annotated images depicting various scenarios developed evaluate models. Experimental results demonstrate system’s effectiveness, achieving an overall detection accuracy 93.20%, outperforming state-of-the-art models such InternVL2.5 ResNet34. By facilitating decision-making, AI approach offers scalable robust solution that contributes smarter, safer environment.

Язык: Английский

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An XGBoost-SHAP framework for identifying key drivers of urban flooding and developing targeted mitigation strategies

Ecological Indicators, Год журнала: 2025, Номер 175, С. 113579 - 113579

Опубликована: Май 17, 2025

Язык: Английский

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Machine learning and genetic algorithm for mapping soil available phosphorus in coastal provinces in Southeast China

Ecological Indicators, Год журнала: 2024, Номер 166, С. 112294 - 112294

Опубликована: Июнь 27, 2024

Язык: Английский

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Assessing the impacts of urban functional form on anthropogenic carbon emissions: A case study of 31 major cities in China

Ecological Indicators, Год журнала: 2024, Номер 167, С. 112700 - 112700

Опубликована: Окт. 1, 2024

Язык: Английский

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Generating Rainfall IDF Curves Using IMD Reduction Formula and Choosing the Best Distribution for Babylon City, Iraq

Engineering Reports, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 23, 2024

ABSTRACT Intensity‐duration‐frequency (IDF) models are considered one of the most important tools used in water resources projects, as well design and planning hydraulic structures such sewerage channels, bridges, culverts, road networks. This study aims to generate IDF curves for Iraqi city Babylon based on Indian Meteorological Department (IMD) empirical reduction formula choose optimal distribution that gives greatest rainfall intensity among three distributions this research (generalized extreme value, Log‐Pearson type III, Gumbel). examined daily data collected from Authority Meteorology Seismic Monitoring a period 32 years, 1991 2022. The IMD was calculate shorter durations (5, 10, 20, 30, 60, 120, 360, 720, 1440 min) custom return periods (2, 5, 25, 50, 100 years). To determine goodness fit distributions, Easy Fit 5.6 program applied with tests (the χ 2 test, Anderson–Darling Kolmogorov–Smirnov test). results showed all were acceptable both storm decreased increasing duration rainstorm. It also increases during large periods. Based criteria AIC BIC, LP‐3 chosen best simulate City using IDM formula.

Язык: Английский

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