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Special Issue "Machine Learning Applied to Hydraulic and Hydrological Modelling"

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

Deadline for manuscript submissions: 31 December 2018

Special Issue Editors

Guest Editor
Dr. Vasilis Bellos

School of Rural and Surveying Engineering, National Technical University of Athens, 9 Iroon Polytechniou, 15780, Zografou, Greece
E-Mail
Interests: hydrodynamic modelling; flood modelling; urban catchment studies; integrated modelling; hydrological modelling; model reduction
Guest Editor
Dr. Juan Pablo Carbajal

Eawag, Urban Water Management, Überlandstrasse 133, CH-8600 Dübendorf, Switzerland
Website | E-Mail

Special Issue Information

Dear Colleagues,

The computational power available nowadays allow us to tackle simulation challenges in hydraulic and hydrological modelling at different scales that were impossible a few decades ago. However, even in the current situation, the time needed for these simulations is inadequate for many scientific and engineering applications, such as decision support systems, flood warning systems, design or optimization of hydraulic structures, calibration of model parameters, uncertainty quantification, real-time model-based control, etc.

To address these issues, the development of fast surrogate models to increase the simulation speed seems to be promising strategy: It does not require a huge investment in new hardware and software, and the same tools can be used to solve very different problems. The field of Machine Learning offers a huge library of methods to build surrogate models, many of which have been successfully used in hydraulic and hydrological modelling.

In this Special Issue we would like to invite research works which incorporate Machine Learning techniques in hydraulic and hydrological modelling, such as (but not restricted to):

-   Artificial Science, in which a relation between input and output is learned using only data, also known as data-driven methods.

-   Scientific Numerical Modelling, such as simplified numerical models, model calibration (system identification) or optimization, renormalized models, up (down)scaled models, coarse models, etc.

-   Emulation, where a fast emulator is developed based on training data derived by a slow simulator

Dr. Vasilis Bellos
Dr. Juan Pablo Carbajal
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 papers will be 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. Water is an international peer-reviewed open access monthly 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 1500 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.

Published Papers (5 papers)

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Research

Open AccessArticle Using Adjacent Buoy Information to Predict Wave Heights of Typhoons Offshore of Northeastern Taiwan
Water 2018, 10(12), 1800; https://doi.org/10.3390/w10121800
Received: 2 November 2018 / Revised: 26 November 2018 / Accepted: 6 December 2018 / Published: 7 December 2018
PDF Full-text (1427 KB)
Abstract
In the northeastern sea area of Taiwan, typhoon-induced long waves often cause rogue waves that endanger human lives. Therefore, having the ability to predict wave height during the typhoon period is critical. The Central Weather Bureau maintains the Longdong and Guishandao buoys in
[...] Read more.
In the northeastern sea area of Taiwan, typhoon-induced long waves often cause rogue waves that endanger human lives. Therefore, having the ability to predict wave height during the typhoon period is critical. The Central Weather Bureau maintains the Longdong and Guishandao buoys in the northeastern sea area of Taiwan to conduct long-term monitoring and collect oceanographic data. However, records have often become lost and the buoys have suffered other malfunctions, causing a lack of complete information concerning wind-generated waves. The goal of the present study was to determine the feasibility of using information collected from the adjacent buoy to predict waves. In addition, the effects of various factors such as the path of a typhoon on the prediction accuracy of data from both buoys are discussed herein. This study established a prediction model, and two scenarios were used to assess the performance: Scenario 1 included information from the adjacent buoy and Scenario 2 did not. An artificial neural network was used to establish the wave height prediction model. The research results demonstrated that (1) Scenario 1 achieved superior performance with respect to absolute errors, relative errors, and efficiency coefficient (CE) compared with Scenario 2; (2) the CE of Longdong (0.802) was higher than that of Guishandao (0.565); and (3) various types of typhoon paths were observed by examining each typhoon. The present study successfully determined the feasibility of using information from the adjacent buoy to predict waves. In addition, the effects of various factors such as the path of a typhoon on the prediction accuracy of both buoys were also discussed. Full article
(This article belongs to the Special Issue Machine Learning Applied to Hydraulic and Hydrological Modelling)
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Graphical abstract

Open AccessArticle A Machine Learning Approach to Evaluating the Damage Level of Tooth-Shape Spur Dikes
Water 2018, 10(11), 1680; https://doi.org/10.3390/w10111680
Received: 11 October 2018 / Revised: 10 November 2018 / Accepted: 14 November 2018 / Published: 17 November 2018
PDF Full-text (5234 KB) | HTML Full-text | XML Full-text
Abstract
Little research has been done on the application of machine learning approaches to evaluating the damage level of river training structures on the Yangtze River. In this paper, two machine learning approaches to evaluating the damage level of spur dikes with tooth-shaped structures
[...] Read more.
Little research has been done on the application of machine learning approaches to evaluating the damage level of river training structures on the Yangtze River. In this paper, two machine learning approaches to evaluating the damage level of spur dikes with tooth-shaped structures are proposed: a supervised support vector machine (SVM) model and an unsupervised model combining a Kohonen neural network with an SVM model (KNN-SVM). It was found that the supervised SVM model predicted the damage level of the validation samples with high accuracy, and the unsupervised data-mining KNN-SVM model agreed well with the empirical evaluation result. It is shown that both machine learning approaches could become effective tools to evaluate the damage level of spur dikes and other river training structures. Full article
(This article belongs to the Special Issue Machine Learning Applied to Hydraulic and Hydrological Modelling)
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Open AccessArticle Assessment of Machine Learning Techniques for Monthly Flow Prediction
Water 2018, 10(11), 1676; https://doi.org/10.3390/w10111676
Received: 11 October 2018 / Revised: 9 November 2018 / Accepted: 14 November 2018 / Published: 17 November 2018
PDF Full-text (17399 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Monthly flow predictions provide an essential basis for efficient decision-making regarding water resource allocation. In this paper, the performance of different popular data-driven models for monthly flow prediction is assessed to detect the appropriate model. The considered methods include feedforward neural networks (FFNNs),
[...] Read more.
Monthly flow predictions provide an essential basis for efficient decision-making regarding water resource allocation. In this paper, the performance of different popular data-driven models for monthly flow prediction is assessed to detect the appropriate model. The considered methods include feedforward neural networks (FFNNs), time delay neural networks (TDNNs), radial basis neural networks (RBFNNs), recurrent neural network (RNN), a grasshopper optimization algorithm (GOA)-based support vector machine (SVM) and K-nearest neighbors (KNN) model. For this purpose, the performance of each model is evaluated in terms of several residual metrics using a monthly flow time series for two real case studies with different flow regimes. The results show that the KNN outperforms the different neural network configurations for the first case study, whereas RBFNN model has better performance for the second case study in terms of the correlation coefficient. According to the accuracy of the results, in the first case study with more input features, the KNN model is recommended for short-term predictions and for the second case with a smaller number of input features, but more training observations, the RBFNN model is suitable. Full article
(This article belongs to the Special Issue Machine Learning Applied to Hydraulic and Hydrological Modelling)
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Open AccessArticle Integration of a Parsimonious Hydrological Model with Recurrent Neural Networks for Improved Streamflow Forecasting
Water 2018, 10(11), 1655; https://doi.org/10.3390/w10111655
Received: 11 September 2018 / Revised: 3 November 2018 / Accepted: 9 November 2018 / Published: 14 November 2018
PDF Full-text (3747 KB) | HTML Full-text | XML Full-text
Abstract
This study applied a GR4J model in the Xiangjiang and Qujiang River basins for rainfall-runoff simulation. Four recurrent neural networks (RNNs)—the Elman recurrent neural network (ERNN), echo state network (ESN), nonlinear autoregressive exogenous inputs neural network (NARX), and long short-term memory (LSTM) network—were
[...] Read more.
This study applied a GR4J model in the Xiangjiang and Qujiang River basins for rainfall-runoff simulation. Four recurrent neural networks (RNNs)—the Elman recurrent neural network (ERNN), echo state network (ESN), nonlinear autoregressive exogenous inputs neural network (NARX), and long short-term memory (LSTM) network—were applied in predicting discharges. The performances of models were compared and assessed, and the best two RNNs were selected and integrated with the lumped hydrological model GR4J to forecast the discharges; meanwhile, uncertainties of the simulated discharges were estimated. The generalized likelihood uncertainty estimation method was applied to quantify the uncertainties. The results show that the LSTM and NARX better captured the time-series dynamics than the other RNNs. The hybrid models improved the prediction of high, median, and low flows, particularly in reducing the bias of underestimation of high flows in the Xiangjiang River basin. The hybrid models reduced the uncertainty intervals by more than 50% for median and low flows, and increased the cover ratios for observations. The integration of a hydrological model with a recurrent neural network considering long-term dependencies is recommended in discharge forecasting. Full article
(This article belongs to the Special Issue Machine Learning Applied to Hydraulic and Hydrological Modelling)
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Open AccessArticle Least Squares Support Vector Mechanics to Predict the Stability Number of Rubble-Mound Breakwaters
Water 2018, 10(10), 1452; https://doi.org/10.3390/w10101452
Received: 31 August 2018 / Revised: 11 October 2018 / Accepted: 11 October 2018 / Published: 15 October 2018
PDF Full-text (2341 KB) | HTML Full-text | XML Full-text
Abstract
In coastal engineering, empirical formulas grounded on experimental works regarding the stability of breakwaters have been developed. In recent years, soft computing tools such as artificial neural networks and fuzzy models have started to be employed to diminish the time and cost spent
[...] Read more.
In coastal engineering, empirical formulas grounded on experimental works regarding the stability of breakwaters have been developed. In recent years, soft computing tools such as artificial neural networks and fuzzy models have started to be employed to diminish the time and cost spent in these mentioned experimental works. To predict the stability number of rubble-mound breakwaters, the least squares version of support vector machines (LSSVM) method is used because it can be assessed as an alternative one to diverse soft computing techniques. The LSSVM models have been operated through the selected seven parameters, which are determined by Mallows’ Cp approach, that are, namely, breakwater permeability, damage level, wave number, slope angle, water depth, significant wave heights in front of the structure, and peak wave period. The performances of the LSSVM models have shown superior accuracy (correlation coefficients (CC) of 0.997) than that of artificial neural networks (ANN), fuzzy logic (FL), and genetic programming (GP), that are all implemented in the related literature. As a result, it is thought that this study will provide a practical way for readers to estimate the stability number of rubble-mound breakwaters with more accuracy. Full article
(This article belongs to the Special Issue Machine Learning Applied to Hydraulic and Hydrological Modelling)
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