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Applied Sciences
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Machine Learning Algorithms for Hydraulic Engineering
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Machine Learning Algorithms for Hydraulic Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (20 October 2021) | Viewed by 20411

Special Issue Editors

Prof. Dr. Francesco Granata

E-Mail Website
Guest Editor
Department of Civil and Mechanical Engineering (DICEM), University of Cassino and Southern Lazio, Via Di Biasio 43, 03043 Cassino, FR, Italy
Interests: hydrology; environmental engineering; civil engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Rudy Gargano

E-Mail Website
Guest Editor
Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Cassino, FR, Italy
Interests: water distribution networks; wastewater hydraulics; hydraulic reliability; water demand model; management of hydraulic infrastructures

Special Issue Information

Dear Colleagues,

As is well known, machine learning (ML) is one of the main branches of artificial intelligence (AI). Its primary objective is to use computational methods to extract information from data. Machine learning has a wide spectrum of practical applications. After the first applications concerning topics such as recognition of manual writing, detection of objects in image processing, voice recognition, medical diagnoses, DNA classification, search engines, and stock market analysis, in recent years machine learning algorithms have been increasingly used in environmental sciences due to their high capability for modelling non-linear phenomena. In particular, these algorithms are already widely used in weather and climate forecasts, as well as in the analysis and modelling of hydrological, ecological, and oceanographic data. In any case, they represent a very powerful tool for dealing with a wide variety of issues in hydraulic engineering.

This Special Issue of Applied Sciences entitled “Machine Learning Algorithms for Hydraulic Engineering” aims to cover recent advances in machine-learning-based modelling for addressing the following topics:

  • River flow forecasting at different time scales;
  • Water balance models for water resources investigations;
  • Global estimates of the land–atmosphere water flux;
  • Water demand prediction;
  • Water quality characterization;
  • Performance forecasting of hydraulic devices (e.g. Valve, CSO, etc.);
  • Support to experimental activities;
  • Long-term predictions of significant wave height;
  • Wave energy extraction systems;

Additional different topics can be proposed by the authors.

Dr. Francesco Granata
Prof. Rudy Gargano
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences 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 2400 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

  • machine learning
  • prediction models
  • synthetic data
  • random forest
  • support vector machine
  • ANN
  • Statistics
  • hydraulic engineering

Published Papers (6 papers)

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Research

15 pages, 6922 KiB  
Article
Tuning ANN Hyperparameters for Forecasting Drinking Water Demand
by Andrea Menapace, Ariele Zanfei and Maurizio Righetti
Appl. Sci. 2021, 11(9), 4290; https://doi.org/10.3390/app11094290 - 10 May 2021
Cited by 18 | Viewed by 2926
Abstract
The evolution of smart water grids leads to new Big Data challenges boosting the development and application of Machine Learning techniques to support efficient and sustainable drinking water management. These powerful techniques rely on hyperparameters making the models’ tuning a tricky and crucial [...] Read more.
The evolution of smart water grids leads to new Big Data challenges boosting the development and application of Machine Learning techniques to support efficient and sustainable drinking water management. These powerful techniques rely on hyperparameters making the models’ tuning a tricky and crucial task. We hence propose an insightful analysis of the tuning of Artificial Neural Networks for drinking water demand forecasting. This study focuses on layers and nodes’ hyperparameters fitting of different Neural Network architectures through a grid search method by varying dataset, prediction horizon and set of inputs. In particular, the architectures involved are the Feed Forward Neural Network, the Long Short Term Memory, the Simple Recurrent Neural Network and the Gated Recurrent Unit, while the prediction interval ranges from 1 h to 1 week. To avoid the problem of the Neural Networks tuning stochasticity, we propose the selection of the median model among several repetitions for each hyperparameter’s configurations. The proposed iterative tuning procedure highlights the change of the required number of layers and nodes depending on Neural Network architectures, prediction horizon and dataset. Significant trends and considerations are pointed out to support Neural Network application in drinking water prediction. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Hydraulic Engineering)
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16 pages, 6764 KiB  
Article
SVM Performance for Predicting the Effect of Horizontal Screen Diameters on the Hydraulic Parameters of a Vertical Drop
by Rasoul Daneshfaraz, Ehsan Aminvash, Amir Ghaderi, John Abraham and Mohammad Bagherzadeh
Appl. Sci. 2021, 11(9), 4238; https://doi.org/10.3390/app11094238 - 07 May 2021
Cited by 19 | Viewed by 2349
Abstract
The present study investigated the application of support vector machine algorithms for predicting hydraulic parameters of a vertical drop equipped with horizontal screens. The study incorporated varying sizes of a rectangular channel. Horizontal screens, in addition to being able to dissipate the destructive [...] Read more.
The present study investigated the application of support vector machine algorithms for predicting hydraulic parameters of a vertical drop equipped with horizontal screens. The study incorporated varying sizes of a rectangular channel. Horizontal screens, in addition to being able to dissipate the destructive energy of the flow, cause turbulence. The turbulence in turn supplies oxygen to the system through the promotion of air–water mixing. To achieve the objectives of the present study, 164 experiments were analyzed under the same experimental conditions using a support vector machine. The approach utilized dimensionless terms that included scenario 1: the relative energy consumption and scenario 2: the relative pool depth. The performance of the models was evaluated with statistical criteria (RMSE, R2 and KGE) and the best model was introduced for each of the parameters. RMSE is the root mean square error, R2 is the correlation coefficient and KGE is the Kling–Gupta criterion. The results of the support vector machine showed that for the first scenario, the third combination with R2 = 0.991, RMSE = 0.00565 and KGE = 0.998 for the training mode and R2 = 0.991, RMSE = 0.00489 and KGE = 0.991 for the testing mode were optimal. For the second scenario, the third combination with R2 = 0.988, RMSE = 0.0395 and KGE = 0.998 for the training mode and R2 = 0.988, RMSE = 0.0389 and KGE = 0.993 for the testing mode were selected. Finally, a sensitivity analysis was performed that showed that the yc/H and D/H parameters are the most effective parameters for predicting relative energy dissipation and relative pool depth, respectively. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Hydraulic Engineering)
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31 pages, 7409 KiB  
Article
Exploring 3D Wave-Induced Scouring Patterns around Subsea Pipelines with Artificial Intelligence Techniques
by Mohammad Najafzadeh and Giuseppe Oliveto
Appl. Sci. 2021, 11(9), 3792; https://doi.org/10.3390/app11093792 - 22 Apr 2021
Cited by 14 | Viewed by 2582
Abstract
Subsea pipelines carry oil or natural gas over long distances of the seabed, but fluid leakage due to a failure of the pipeline can culminate in huge environmental disasters. Scouring process may take place beneath pipelines due to current and/or wave action, causing [...] Read more.
Subsea pipelines carry oil or natural gas over long distances of the seabed, but fluid leakage due to a failure of the pipeline can culminate in huge environmental disasters. Scouring process may take place beneath pipelines due to current and/or wave action, causing pipeline suspension and leading to the risk of pipeline failure. The resulting morphological variations of the seabed propagate not only below and normally to the pipeline but also along the pipeline itself. Therefore, 3D scouring patterns need to be considered. Mainly based on the experimental works at laboratory scale by Cheng and coworkers, in this study, Artificial Intelligent (AI) techniques are employed to present new equations for predicting three dimensional current- and wave-induced scour rates around subsea pipelines. These equations are given in terms of key dimensionless parameters, among which are the Shields’ parameter, the Keulegan–Carpenter number, relative embedment depth, and wave/current angle of attach. Using various statistical benchmarks, the efficiency of AI-models-based regression equations is assessed. The proposed predictive models perform much better than the existing empirical equations from literature. Even more interestingly, they exhibit a clear physical consistence and allow for highlighting the relative importance of the key dimensionless variables governing the scouring patterns. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Hydraulic Engineering)
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16 pages, 4722 KiB  
Article
Energy Loss in Skimming Flow over Cascade Spillways: Comparison of Artificial Intelligence-Based and Regression Methods
by Meysam Nouri, Parveen Sihag, Farzin Salmasi and Ozgur Kisi
Appl. Sci. 2020, 10(19), 6903; https://doi.org/10.3390/app10196903 - 01 Oct 2020
Cited by 10 | Viewed by 3072
Abstract
In this study, the energy dissipation of cascade spillways was studied by conducting a series of laboratory experiments. Five spillways slope angles (α) (10°, 20°, 30°, 40°, and 50°), various step numbers (N) ranging from 4 to 75, and a wide [...] Read more.
In this study, the energy dissipation of cascade spillways was studied by conducting a series of laboratory experiments. Five spillways slope angles (α) (10°, 20°, 30°, 40°, and 50°), various step numbers (N) ranging from 4 to 75, and a wide range of discharges (Q), were considered. Some data-based models were developed to explain the relationships between hydraulic parameters. Multiple linear and nonlinear regression-based equations were developed based on dimensional analysis theory to compute energy dissipation over cascade spillways. For testing the robustness of developed data-based models, M5P, stochastic M5P, and random forest (RF) were used as new artificial intelligence (AI)-based techniques. To relate the input and output variables of energy dissipation, AI-based and regression approaches were developed. It was found that the formulation based on the stochastic M5P approach in solving energy dissipation problems over cascade spillways is more successful than the other regression and AI-based methods. Sensitivity analysis suggests that spillway slope in degrees (α) is the most influential input variable in predicting the relative energy dissipation (%) of the spillway in comparison to other input variables. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Hydraulic Engineering)
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22 pages, 5588 KiB  
Article
Deformation of Air Bubbles Near a Plunging Jet Using a Machine Learning Approach
by Fabio Di Nunno, Francisco Alves Pereira, Giovanni de Marinis, Fabio Di Felice, Rudy Gargano, Massimo Miozzi and Francesco Granata
Appl. Sci. 2020, 10(11), 3879; https://doi.org/10.3390/app10113879 - 03 Jun 2020
Cited by 12 | Viewed by 2788
Abstract
The deformation of air bubbles in a liquid flow field is of relevant interest in phenomena such as cavitation, air entrainment, and foaming. In complex situations, this problem cannot be addressed theoretically, while the accuracy of an approach based on Computational Fluid Dynamics [...] Read more.
The deformation of air bubbles in a liquid flow field is of relevant interest in phenomena such as cavitation, air entrainment, and foaming. In complex situations, this problem cannot be addressed theoretically, while the accuracy of an approach based on Computational Fluid Dynamics (CFD) is often unsatisfactory. In this study, a novel approach to the problem is proposed, based on the combined use of a shadowgraph technique, to obtain experimental data, and some machine learning algorithms to build prediction models. Three models were developed to predict the equivalent diameter and aspect ratio of air bubbles moving near a plunging jet. The models were different in terms of their input variables. Five variants of each model were built, changing the implemented machine learning algorithm: Additive Regression of Decision Stump, Bagging, K-Star, Random Forest and Support Vector Regression. In relation to the prediction of the equivalent diameter, two models provided satisfactory predictions, assessed on the basis of four different evaluation metrics. The third model was slightly less accurate in all its variants. Regarding the forecast of the bubble’s aspect ratio, the difference in the input variables of the prediction models shows a greater influence on the accuracy of the results. However, the proposed approach proves to be promising to address complex problems in the study of multi-phase flows. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Hydraulic Engineering)
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18 pages, 3401 KiB  
Article
Scouring Depth Assessment Downstream of Weirs Using Hybrid Intelligence Models
by Ahmad Sharafati, Masoud Haghbin, Seyed Babak Haji Seyed Asadollah, Nand Kumar Tiwari, Nadhir Al-Ansari and Zaher Mundher Yaseen
Appl. Sci. 2020, 10(11), 3714; https://doi.org/10.3390/app10113714 - 27 May 2020
Cited by 13 | Viewed by 3776
Abstract
Considering the scouring depth downstream of weirs is a challenging issue due to its effect on weir stability. The adaptive neuro-fuzzy inference systems (ANFIS) model integrated with optimization methods namely cultural algorithm, biogeography based optimization (BBO), invasive weed optimization (IWO) and teaching learning [...] Read more.
Considering the scouring depth downstream of weirs is a challenging issue due to its effect on weir stability. The adaptive neuro-fuzzy inference systems (ANFIS) model integrated with optimization methods namely cultural algorithm, biogeography based optimization (BBO), invasive weed optimization (IWO) and teaching learning based optimization (TLBO) are proposed to predict the maximum depth of scouring based on the different input combinations. Several performance indices and graphical evaluators are employed to estimate the prediction accuracy in the training and testing phase. Results show that the ANFIS-IWO offers the highest prediction performance (RMSE = 0.148) compared to other models in the testing phase, while the ANFIS-BBO (RMSE = 0.411) provides the lowest accuracy. The findings obtained from the uncertainty analysis of prediction modeling indicate that the input variables variability has a higher impact on the predicted results than the structure of models. In general, the ANFIS-IWO can be used as a reliable and cost-effective method for predicting the scouring depth downstream of weirs. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Hydraulic Engineering)
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