Special Issue "Machine Learning for Hydro-Systems"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 7899

Special Issue Editors

Prof. Dr. Joong Hoon Kim
E-Mail Website
Guest Editor
School of Civil, Environmental and Architectural Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Korea
Interests: machine learning; optimization algorithms; hydroinformatics; water distribution systems; urban drainage systems; smart water grids
Special Issues, Collections and Topics in MDPI journals
Dr. Donghwi Jung
E-Mail Website
Guest Editor
School of Civil, Environmental and Architectural Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Korea
Interests: water distribution system modelling and control; event detection and diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning (ML) is the science of making computers learn and act without explicit instructions and programming, but with patterns and inference extracted from data instead. Applied in various science and engineering domains, ML is now pervasive in the field of water engineering. Currently, traditional hydroinformatics methods (regression, classification, and clustering) are being replaced with new ML techniques such as deep neural networks (DNNs), which are mostly accompanied by big data of special features (e.g., unstructured or spatio-temporal) obtained with advances in measurement and sensor technologies.

This Special Issue intends to include papers introducing novel ML approaches for tackling problems in hydro-systems, that is, water supply/distribution systems, urban drainage networks, and river networks. We especially expect to facilitate new DNN models which can effectively and efficiently resolve problems and issues in the domain with unstructured water data. Studies on spatio-temporal hydrological and water demand data processing would be also welcome if an ML technique is used.

We hope this Special Issue can: (1) serve as a reference point from which readers can review progress, recent trends, and emerging issues; and (2) shed light on the right future directions of ML studies for water.

Prof. Dr. Joong Hoon Kim
Dr. Donghwi Jung
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. 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 2200 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 (ML) techniques for water supply/distribution systems, urban drainage networks, and river networks
  • Deep neural networks (DNNs)
  • Spatio-temporal hydrological and water demand data processing
  • Unstructured water data
  • State-of-the-art reviews on ML and DNN approaches for hydro-systems.

Published Papers (7 papers)

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Research

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Article
Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks
Water 2021, 13(11), 1547; https://doi.org/10.3390/w13111547 - 31 May 2021
Cited by 5 | Viewed by 1040
Abstract
Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with [...] Read more.
Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN. Full article
(This article belongs to the Special Issue Machine Learning for Hydro-Systems)
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Article
Deep Learning Based Approach to Classify Saline Particles in Sea Water
Water 2021, 13(9), 1251; https://doi.org/10.3390/w13091251 - 29 Apr 2021
Cited by 7 | Viewed by 999
Abstract
Water is an essential resource that facilitates the existence of human life forms. In recent years, the demand for the consumption of freshwater has substantially increased. Seawater contains a high concentration of salt particles and salinity, making it unfit for consumption and domestic [...] Read more.
Water is an essential resource that facilitates the existence of human life forms. In recent years, the demand for the consumption of freshwater has substantially increased. Seawater contains a high concentration of salt particles and salinity, making it unfit for consumption and domestic use. Water treatment plants used to treat seawater are less efficient and reliable. Deep learning systems can prove to be efficient and highly accurate in analyzing salt particles in seawater with higher efficiency that can improve the performance of water treatment plants. Therefore, this work classified different concentrations of salt particles in water using convolutional neural networks with the implementation of transfer learning. Salt salinity concentration images were captured using a designed Raspberry Pi based model and these images were further used for training purposes. Moreover, a data augmentation technique was also employed for the state-of-the-art results. Finally, a deep learning neural network was used to classify saline particles of varied concentration range images. The experimental results show that the proposed approach exhibited superior outcomes by achieving an overall accuracy of 90% and f-score of 87% in classifying salt particles. The proposed model was also evaluated using other evaluation metrics such as precision, recall, and specificity, and showed robust results. Full article
(This article belongs to the Special Issue Machine Learning for Hydro-Systems)
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Article
Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach
Water 2021, 13(8), 1109; https://doi.org/10.3390/w13081109 - 17 Apr 2021
Viewed by 945
Abstract
Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand [...] Read more.
Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions. Full article
(This article belongs to the Special Issue Machine Learning for Hydro-Systems)
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Article
Application of LSTM Networks for Water Demand Prediction in Optimal Pump Control
Water 2021, 13(5), 644; https://doi.org/10.3390/w13050644 - 28 Feb 2021
Cited by 5 | Viewed by 1176
Abstract
Every morning, water suppliers need to define their pump schedules for the next 24 h for drinking water production. Plans must be designed in such a way that drinking water is always available and the amount of unused drinking water pumped into the [...] Read more.
Every morning, water suppliers need to define their pump schedules for the next 24 h for drinking water production. Plans must be designed in such a way that drinking water is always available and the amount of unused drinking water pumped into the network is reduced. Therefore, operators must accurately estimate the next day’s water consumption profile. In real-life applications with standard consumption profiles, some expert system or vector autoregressive models are used. Still, in recent years, significant improvements for time series prediction have been achieved through special deep learning algorithms called long short-term memory (LSTM) networks. This paper investigates the applicability of LSTM models for water demand prediction and optimal pump control and compares LSTMs against other methods currently used by water suppliers. It is shown that LSTMs outperform other methods since they can easily integrate additional information like the day of the week or national holidays. Furthermore, the online- and transfer-learning capabilities of the LSTMs are investigated. It is shown that LSTMs only need a couple of days of training data to achieve reasonable results. As the focus of the paper is on the real-world application of LSTMs, data from two different water distribution plants are used for benchmarking. Finally, it is shown that the LSTMs significantly outperform the system currently in operation. Full article
(This article belongs to the Special Issue Machine Learning for Hydro-Systems)
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Article
Determination of Internal Elevation Fluctuation from CCTV Footage of Sanitary Sewers Using Deep Learning
Water 2021, 13(4), 503; https://doi.org/10.3390/w13040503 - 15 Feb 2021
Viewed by 903
Abstract
The slope of sewer pipes is a major factor for transporting sewage at designed flow rates. However, the gradient inside the sewer pipe changes locally for various reasons after construction. This causes flow disturbances requiring investigation and appropriate maintenance. This study extracted the [...] Read more.
The slope of sewer pipes is a major factor for transporting sewage at designed flow rates. However, the gradient inside the sewer pipe changes locally for various reasons after construction. This causes flow disturbances requiring investigation and appropriate maintenance. This study extracted the internal elevation fluctuation from closed-circuit television investigation footage, which is required for sanitary sewers. The principle that a change in water level in sewer pipes indirectly indicates a change in elevation was applied. The sewage area was detected using a convolutional neural network, a type of deep learning technique, and the water level was calculated using the geometric principles of circles and proportions. The training accuracy was 98%, and the water level accuracy compared to random sampling was 90.4%. Lateral connections, joints, and outliers were removed, and a smoothing method was applied to reduce data fluctuations. Because the target sewer pipes are 2.5 m concrete reinforced pipes, the joint elevation was determined every 2.5 m so that the internal slope of the sewer pipe would consist of 2.5 m linear slopes. The investigative method proposed in this study is effective with high economic feasibility and sufficient accuracy compared to the existing sensor-based methods of internal gradient investigation. Full article
(This article belongs to the Special Issue Machine Learning for Hydro-Systems)
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Article
Empirical Analysis and Countermeasures of the Irrigation Efficiency Paradox in the Shenwu Irrigation Area, China
Water 2020, 12(11), 3142; https://doi.org/10.3390/w12113142 - 10 Nov 2020
Cited by 2 | Viewed by 878
Abstract
Water-saving in agriculture is critical for building a water-conserving society. However, the application of high-efficiency water-saving technology in agriculture may create a paradox of irrigation efficiency. Efficiency improvement in agricultural water utilization may not lead to the expected agricultural water-saving. In this paper, [...] Read more.
Water-saving in agriculture is critical for building a water-conserving society. However, the application of high-efficiency water-saving technology in agriculture may create a paradox of irrigation efficiency. Efficiency improvement in agricultural water utilization may not lead to the expected agricultural water-saving. In this paper, a rebound intensity model of the irrigation efficiency paradox is established and combined with remote sensing measurement to verify the irrigation efficiency paradox caused by expanding the irrigation area in the Shenwu Irrigation Area, China. Based on ideas in the principal–agent theory and stakeholder theory, it is concluded that the essence of the irrigation efficiency paradox is the conflict of interests among stakeholders with asymmetric information due to inadequate regulatory capacity. A dual principal–agent model is formulated to optimize the conflict among heterogeneous stakeholders in the paradox. The results show that the paradox should be restrained by a suitable distribution mechanism of water-saving gains, improved irrigation water metering, and enhanced water-use monitoring. Full article
(This article belongs to the Special Issue Machine Learning for Hydro-Systems)
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Review

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Review
Machine Learning and Urban Drainage Systems: State-of-the-Art Review
Water 2021, 13(24), 3545; https://doi.org/10.3390/w13243545 - 11 Dec 2021
Cited by 1 | Viewed by 1070
Abstract
In the last decade, machine learning (ML) technology has been transforming daily lives, industries, and various scientific/engineering disciplines. In particular, ML technology has resulted in significant progress in neural network models; these enable the automatic computation of problem-relevant features and rapid capture of [...] Read more.
In the last decade, machine learning (ML) technology has been transforming daily lives, industries, and various scientific/engineering disciplines. In particular, ML technology has resulted in significant progress in neural network models; these enable the automatic computation of problem-relevant features and rapid capture of highly complex data distributions. We believe that ML approaches can address several significant new and/or old challenges in urban drainage systems (UDSs). This review paper provides a state-of-the-art review of ML-based UDS modeling/application based on three categories: (1) operation (real-time operation control), (2) management (flood-inundation prediction) and (3) maintenance (pipe defect detection). The review reveals that ML is utilized extensively in UDSs to advance model performance and efficiency, extract complex data distribution patterns, and obtain scientific/engineering insights. Additionally, some potential issues and future directions are recommended for three research topics defined in this study to extend UDS modeling/applications based on ML technology. Furthermore, it is suggested that ML technology can promote developments in UDSs. The new paradigm of ML-based UDS modeling/applications summarized here is in its early stages and should be considered in future studies. Full article
(This article belongs to the Special Issue Machine Learning for Hydro-Systems)
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