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Hydrology

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Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models.

Environmental science and pollution research international
Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise to differing values with no theoretical or empirical basis to establish a validated baseline or to develop a comparison basis between the modeling resul...

Priorization of River Restoration by Coupling Soil and Water Assessment Tool (SWAT) and Support Vector Machine (SVM) Models in the Taizi River Basin, Northern China.

International journal of environmental research and public health
Identifying priority zones for river restoration is important for biodiversity conservation and catchment management. However, limited data due to the difficulty of field collection has led to research to better understand the ecological status withi...

Modeling daily suspended sediment load using improved support vector machine model and genetic algorithm.

Environmental science and pollution research international
Prediction of sediment volume and sediment load is always one of the important issues for decision-makers of watershed basins. The present study investigated the daily suspended sediment load in a watershed basin using the improved support vector mac...

Performance assessment of artificial neural networks and support vector regression models for stream flow predictions.

Environmental monitoring and assessment
Water resources planning, development, and management need reliable forecasts of river flows. In past few decades, an important dimension has been introduced in the prediction of the hydrologic phenomenon through artificial intelligence-based modelin...

Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill.

Environmental science and pollution research international
Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environ...

Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration.

Environmental science and pollution research international
Accurate estimation of reference evapotranspiration (ET) is profoundly crucial in crop modeling, sustainable management, hydrological water simulation, and irrigation scheduling, since it accounts for more than two-thirds of global precipitation loss...

Exploring the multiscale hydrologic regulation of multipond systems in a humid agricultural catchment.

Water research
Assessing the hydrologic processes over scales ranging from single wetland to regional is critical to understand the hydrologically-driven ecosystem services especially nutrient buffering of wetlands. Here, we present a novel approach to quantify the...

A comprehensive review of deep learning applications in hydrology and water resources.

Water science and technology : a journal of the International Association on Water Pollution Research
The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water...

Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing.

PloS one
Reference evapotranspiration (ETo) is a fundamental parameter for hydrological studies and irrigation management. The Penman-Monteith method is the standard to estimate ETo and requires several meteorological elements. In developing countries, the nu...

Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models.

Environmental science and pollution research international
Accurate and timely monitoring of streamflow and its variation is crucial for water resources management in watersheds. This study aimed at evaluating the performance of two process-driven conceptual rainfall-runoff models (HBV: Hydrologiska ByrÄns V...