AIMC Topic: Rivers

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Exploring the influence of hydrological indicators on flow regimes through a data-driven modeling approach in the Huai river basin, China.

Environmental research
Understanding the impact of hydrological indicators on flow regimes is essential for sustainable water resource management. This study presents a data-driven framework integrating eXtreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations...

Wetland dynamics in the Indus River Delta: A Sentinel-2 and machine learning approach.

Journal of environmental management
Coastal wetlands of the Indus River Delta are vital ecological regions that have undergone significant transformations driven by anthropogenic activities and environmental stressors. This study assesses the dynamics of wetlands and reclamation in the...

Machine Learning-Driven Dynamic Measurement of Environmental Indicators in Multiple Scenes and Multiple Disturbances.

Environmental science & technology
Digital city water management systems require extensive data sensing for various environmental indicators, yet measurement accuracy often falls short under diverse extreme conditions. This study proposes a chemical oxygen demand (COD) measurement met...

Distribution, sources, and ecological risks of heavy metal contamination at the sediment-water interface in the Dongjiang Basin based on in situ high-resolution measurements.

Environmental pollution (Barking, Essex : 1987)
As a critical drinking water source for over 40 million people in southern China, the Dongjiang River faces growing ecological threats from sediment-derived heavy metals (HMs: As, Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn). This pioneering study is the firs...

Exploring the contrasting lake CO fluxes and influencing variables in four large shallow subtropical lakes with different hydrological connectivity.

Journal of environmental management
Lake carbon dioxide (CO) evasion is a crucial component of global carbon cycle, yet the influence of environmental factors on CO emissions within different hydrological connectivity remains uncertain. Based on multiple machine learning methods, we in...

Urbanization intensifies deterministic selection of pathogenic bacteria in river networks: Nitrogen-driven niche partitioning and cross-scale risk forecasting through DOM-bacteria interplay.

Environmental research
Urbanization modifies the composition of dissolved organic matter (DOM) and nitrogen nutrients, profoundly affecting river microbial communities. However, the mechanisms driving pathogenic and non-pathogenic bacteria remain unclear. In this study, we...

GeoAI-based soil erosion risk assessment in the Brahmaputra River Basin: a synergistic approach using RUSLE and advanced machine learning.

Environmental monitoring and assessment
Soil erosion is a critical environmental issue in the Brahmaputra River Basin, threatening agricultural productivity, water resources, and ecological balance. This study employs the revised universal soil loss equation (RUSLE) alongside remote sensin...

Prediction of water quality parameters and pollution exceedance analysis in typical rivers of semi-arid regions based on interpretable deep learning models.

Environmental pollution (Barking, Essex : 1987)
Deep learning models that integrate environmental characteristics provide a powerful means for high-precision water quality prediction; however, their black-box nature can limit interpretability and reliability. We proposed an interpretable Attention...

Responses of Microbial Community to Heterogeneous Dissolved Organic Nitrogen Constituents in the Hyporheic Zones of Treated Sewage-Dominated Rivers.

Microbial ecology
The hyporheic zone (HZ) of treated sewage-dominated rivers serves as a critical biogeochemical hotspot for dissolved organic nitrogen (DON) transformation, yet the mechanisms linking DON chemodiversity to microbial community dynamics remain poorly re...

Developing sediment concentration prediction in the Euphrates River catchment, Türkiye, with a honey badger and coati optimization-based hybrid algorithm.

Environmental monitoring and assessment
Estimation of sediment concentration (SC) is of vital importance in terms of siltation and economic life of dams, lakes and aqueducts, reservoir operations, design of water resource structures, monitoring and control of water pollution, and flood man...