AIMC Topic: Water Movements

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A fuzzy TOPSIS-based approach for prioritizing low-impact development methods in high-density residential areas.

Water science and technology : a journal of the International Association on Water Pollution Research Jan 1, 2024
The study successfully implemented six low-impact development (LID) methods to manage surface runoff in urban areas: green roof, infiltration trench, bio retention cell, rain barrel, green roof combined with infiltration trench, and rain barrel combi...
Cities Models, Theoretical Conservation of Natural Resources Rain Iran Fuzzy Logic Water Movements
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Enhancing rainfall-runoff model accuracy with machine learning models by using soil water index to reflect runoff characteristics.

Water science and technology : a journal of the International Association on Water Pollution Research Jan 1, 2024
The advancement of data-driven models contributes to the improvement of estimating rainfall-runoff models due to their advantages in terms of data requirements and high performance. However, data-driven models that rely solely on rainfall data have l...
Rain Water Movements Environmental Monitoring Soil Models, Theoretical Water Neural Networks, Computer Machine Learning
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Machine learning approaches for adequate prediction of flow resistance in alluvial channels with bedforms.

Water science and technology : a journal of the International Association on Water Pollution Research Jan 1, 2024
In natural rivers, flow conditions are mainly dependent on flow resistance and type of roughness. The interactions among flow and bedforms are complex in nature as bedform dynamics primarily regulate the flow resistance. Manning's equation is the mos...
Rivers Water Movements Models, Theoretical Machine Learning
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Hybrid wavelet-gene expression programming and wavelet-support vector machine models for rainfall-runoff modeling.

Water science and technology : a journal of the International Association on Water Pollution Research Dec 1, 2022
It is critical to use research methods to collect and regulate surface water to provide water while avoiding damage. Following accurate runoff prediction, principled planning for optimal runoff is implemented. In recent years, there has been an incre...
Water Movements Support Vector Machine Rivers Gene Expression Computer Simulation Water
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Predictive models for wastewater flow forecasting based on time series analysis and artificial neural network.

Water science and technology : a journal of the International Association on Water Pollution Research Jul 1, 2019
Wastewater flow forecasting is key for proper management of wastewater treatment plants (WWTPs). However, to predict the amount of incoming wastewater in WWTPs, wastewater engineers face challenges arising from numerous complexities and uncertainties...
Water Movements Forecasting Wastewater Models, Statistical Neural Networks, Computer Canada
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Estimation of hydraulic jump characteristics of channels with sudden diverging side walls via SVM.

Water science and technology : a journal of the International Association on Water Pollution Research Oct 1, 2017
Sudden diverging channels are one of the energy dissipaters which can dissipate most of the kinetic energy of the flow through a hydraulic jump. An accurate prediction of hydraulic jump characteristics is an important step in designing hydraulic stru...
Models, Theoretical Base Sequence Sanitary Engineering Support Vector Machine Algorithms Water Movements
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Hybrid wavelet-support vector machine approach for modelling rainfall-runoff process.

Water science and technology : a journal of the International Association on Water Pollution Research Jan 1, 2016
Because of the importance of water resources management, the need for accurate modeling of the rainfall-runoff process has rapidly grown in the past decades. Recently, the support vector machine (SVM) approach has been used by hydrologists for rainfa...
Models, Theoretical Support Vector Machine Water Resources Neural Networks, Computer Rain Hydrology Rivers Water Movements
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Determination of number of check dams by artificial neural networks in arid regions of Iran.

Water science and technology : a journal of the International Association on Water Pollution Research Jan 1, 2015
An artificial neural network (ANN) model with six hydrological factors including time of concentration (TC), curve number, slope, imperviousness, area and input discharge as input parameters and number of check dams (NCD) as output parameters was dev...
Environmental Monitoring Water Movements Iran Neural Networks, Computer Desert Climate Models, Theoretical
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