AIMC Topic: Water Movements

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A Novel Interannual Rainfall Runoff Equation Derived from Ol'Dekop's Model Using Artificial Neural Networks.

Sensors (Basel, Switzerland)
In water resources management, modeling water balance factors is necessary to control dams, agriculture, irrigation, and also to provide water supply for drinking and industries. Generally, conceptual and physical models present challenges to find mo...

A Hybrid Water Balance Machine Learning Model to Estimate Inter-Annual Rainfall-Runoff.

Sensors (Basel, Switzerland)
Watershed climatic diversity poses a hard problem when it comes to finding suitable models to estimate inter-annual rainfall runoff (IARR). In this work, a hybrid model (dubbed MR-CART) is proposed, based on a combination of MR (multiple regression) ...

Development of cluster analysis methodology for identification of model rainfall hyetographs and its application at an urban precipitation field scale.

The Science of the total environment
Despite growing access to precipitation time series records at a high temporal scale, in hydrology, and particularly urban hydrology, engineers still design and model drainage systems using scenarios of rainfall temporal distributions predefined by m...

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 Byråns V...

A rainwater control optimization design approach for airports based on a self-organizing feature map neural network model.

PloS one
To address the problems of high overflow rate of pipe network inspection well and low drainage efficiency, a rainwater control optimization design approach based on a self-organizing feature map neural network model (SOFM) was proposed in this paper....

Artificial intelligence based approaches to evaluate actual evapotranspiration in wetlands.

The Science of the total environment
Wetlands are extraordinary ecosystems and important climate regulators that also contribute to reduce natural disaster risk. Unfortunately, wetlands are declining much faster than forests. The safeguarding of the wetlands also needs knowledge of the ...

Valuation methodology of laminar erosion potential using fuzzy inference systems in a Brazilian savanna.

Environmental monitoring and assessment
This study presents an approach on the evaluation of potential laminar erosion in the Ribeirão Sucuri Grande watershed. It is located in the northeast of the state of Goiás, Brazil, a conservation area under strong anthropogenic pressure. A Mamdani f...

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...

An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland.

Environmental monitoring and assessment
A predictive model for streamflow has practical implications for understanding the drought hydrology, environmental monitoring and agriculture, ecosystems and resource management. In this study, the state-or-art extreme learning machine (ELM) model w...

Evidence of Water Quality Degradation in Lower Mekong Basin Revealed by Self-Organizing Map.

PloS one
To reach a better understanding of the spatial variability of water quality in the Lower Mekong Basin (LMB), the Self-Organizing Map (SOM) was used to classify 117 monitoring sites and hotspots of pollution within the basin identified according to wa...