AIMC Topic: Hydrology

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Quantifying urban land cover imperviousness as input for flood simulation using machine learning: South African case study.

Water science and technology : a journal of the International Association on Water Pollution Research May 16, 2025
The imperviousness of urban surfaces is an important parameter in simulating urban hydrological responses, but quantifying imperviousness can be challenging and time-consuming. In response, this study presents a new framework to efficiently estimate ...
Machine Learning Computer Simulation South Africa Floods Hydrology Cities Models, Theoretical
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Assessing climate change and human impacts on runoff and hydrological droughts in the Yellow River Basin using a machine learning-enhanced hydrological modeling approach.

Journal of environmental management Mar 26, 2025
Analyzing the impacts of climate change (CC) and human activities (HA) on hydrological events is important for water resource management. This study quantifies the impacts of CC and HA on runoff and hydrological drought characteristics (HDC) in the Y...
Hydrology Climate Change Droughts Machine Learning China Environmental Monitoring Rivers Models, Theoretical Humans
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Uncovering water conservation patterns in semi-arid regions through hydrological simulation and deep learning.

PloS one Mar 20, 2025
Under the increasing pressure of global climate change, water conservation (WC) in semi-arid regions is experiencing unprecedented levels of stress. WC involves complex, nonlinear interactions among ecosystem components like vegetation, soil structur...
Droughts Hydrology Conservation of Water Resources Deep Learning Climate Change Computer Simulation Ecosystem Spatio-Temporal Analysis Desert Climate
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High-resolution groundwater storage anomalies in the Middle and Lower Yangtze River Basin of China using machine learning fusion of in-situ wells, satellite gravity and hydrological model.

Journal of environmental management Jan 29, 2025
Groundwater plays a key role in the water cycle and is used to meet industrial, agricultural, and domestic water demands. High-resolution modeling of groundwater storage is often challenging due to the limitations of observation techniques and mathem...
Algorithms China Groundwater Neural Networks, Computer Machine Learning Gravitation Environmental Monitoring Hydrology Rivers
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Comparing machine learning approaches for estimating soil saturated hydraulic conductivity.

PloS one Nov 14, 2024
Characterization of near (field) saturated hydraulic conductivity (Kfs) of the soil environment is among the crucial components of hydrological modeling frameworks. Since the associated laboratory/field experiments are time-consuming and labor-intens...
Environment Soil Neural Networks, Computer Water Movements Models, Theoretical Machine Learning Hydrology
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Comprehensive assessment of cascading dams-induced hydrological alterations in the lancang-mekong river using machine learning technique.

Journal of environmental management Nov 11, 2024
The development of cascading hydropower dams in river basins has significantly altered natural flow regimes in recent decades. This study investigates hydrological alterations caused by cascading hydropower dams in the Lancang-Mekong River Basin (LMR...
Machine Learning Environmental Monitoring Climate Change Hydrology Rivers Humans
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Assessing long-term water storage dynamics in Afghanistan: An integrated approach using machine learning, hydrological models, and remote sensing.

Journal of environmental management Oct 21, 2024
Assessment of Terrestrial Water Storage (TWS) components is crucial for understanding regional climate and water resources, particularly in arid and semi-arid regions like Afghanistan. Given the scarcity of ground-based data, this study leverages rem...
Hydrology Water Resources Afghanistan Models, Theoretical Remote Sensing Technology Machine Learning Climate Water
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Integrated water resource management in the Segura Hydrographic Basin: An artificial intelligence approach.

Journal of environmental management Oct 1, 2024
Managing resources effectively in uncertain demand, variable availability, and complex governance policies is a significant challenge. This paper presents a paradigmatic framework for addressing these issues in water management scenarios by integrati...
Water Resources Water Supply Hydrology Models, Theoretical Spain Artificial Intelligence Algorithms
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Interpretable machine learning guided by physical mechanisms reveals drivers of runoff under dynamic land use changes.

Journal of environmental management Jul 26, 2024
Human activities continuously impact water balances and cycling in watersheds, making it essential to accurately identify the responses of runoff to dynamic changes in land use types. Although machine learning models demonstrate promise in capturing ...
Environmental Monitoring Rivers Rain Agriculture Models, Theoretical Humans Hydrology Machine Learning
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Assessing current and future available resources to supply urban water demands using a high-resolution SWAT model coupled with recurrent neural networks and validated through the SIMPA model in karstic Mediterranean environments.

Environmental science and pollution research international Jul 24, 2024
Hydrological simulation in karstic areas is a hard task due to the intrinsic intricacy of these environments and the common lack of data related to their geometry. Hydrological dynamics of karstic sites in Mediterranean semiarid regions are difficult...
Models, Theoretical Hydrology Groundwater Spain Neural Networks, Computer Environmental Monitoring Water Supply
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