Modeling Hydraulics and River Dynamics Using Numerical Analysis and Soft Computing Methods

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydraulics and Hydrodynamics".

Deadline for manuscript submissions: closed (30 October 2023) | Viewed by 1230

Special Issue Editors


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Guest Editor
Water Engineering Department, Shahid Bahonar University of Kerman, Kerman 76169-14111, Iran
Interests: hydrology; hydroinformatics; hydraulics; soft computing; artificial intelligence; groundwater modeling; hydraulic structures; numerical simulation

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Guest Editor
1. Department of Civil Engineering, Technical University of Lübeck, 23562 Lübeck, Germany
2. Department of Civil Engineering, Ilia State University, 0162 Tbilisi, Georgia
Interests: hydroinformatics; modeling hydro-climatic dynamics using machine learning; data mining in hydrological prediction; rainfall-runoff modeling; analysis of hydro-climatic variables using new trend methods
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Special Issue Information

Dear Colleagues,

The hydraulics and dynamics of water movement in rivers and open channels is of great importance in the diverse realms of hydrology and water sciences. Accordingly, in recent decades, several researchers have considered the accurate and proper simulation of these water bodies. Regarding the significance of the phenomena, researchers are invited to submit their studies related to the numerical modeling and/or soft computing simulation of river dynamics (and pertinent hydro-structures) to this Special Issue. The main outlines and scopes of this issue can be categorized as the following:

  • Behavior and corresponding description of flow in rivers.
  • Modeling flow in compound channels.
  • Floodplain flow modeling.
  • River flow and groundwater flow interaction.
  • Water resources management corresponding to river flow.
  • Simulation of contamination and pollution in rivers.
  • Optimizing stage-discharge relation using advanced methods.
  • Modeling dynamics of estuaries.
  • River structures and their functions.

It should be noted that related studies with an experimental and machine learning modeling background are also welcome for submission to this Special Issue.

Prof. Dr. Mohammad Zounemat-Kermani
Prof. Dr. Ozgur Kisi
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • river flow
  • open channels
  • surface hydrology
  • floodplains
  • river pollution

Published Papers (1 paper)

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Research

18 pages, 4579 KiB  
Article
Estimation of Reference Evapotranspiration in Semi-Arid Region with Limited Climatic Inputs Using Metaheuristic Regression Methods
by Saad Sh. Sammen, Ozgur Kisi, Ahmed Mohammed Sami Al-Janabi, Ahmed Elbeltagi and Mohammad Zounemat-Kermani
Water 2023, 15(19), 3449; https://doi.org/10.3390/w15193449 - 30 Sep 2023
Viewed by 739
Abstract
Different regression-based machine learning techniques, including support vector machine (SVM), random forest (RF), Bagged trees algorithm (BaT), and Boosting trees algorithm (BoT) were adopted for modeling daily reference evapotranspiration (ET0) in a semi-arid region (Hemren catchment basin in Iraq). An assessment [...] Read more.
Different regression-based machine learning techniques, including support vector machine (SVM), random forest (RF), Bagged trees algorithm (BaT), and Boosting trees algorithm (BoT) were adopted for modeling daily reference evapotranspiration (ET0) in a semi-arid region (Hemren catchment basin in Iraq). An assessment of the methods with various input combinations of climatic parameters, including solar radiation (SR), wind speed (WS), relative humidity (RH), and maximum and minimum air temperatures (Tmax and Tmin), indicated that the RF method, especially with Tmax, Tmin, Tmean, and SR inputs, provided the best accuracy in estimating daily ET0 in all stations, while the SVM had the worst accuracy. This work will help water users, developers, and decision makers in water resource planning and management to achieve sustainability. Full article
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