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Data Science in Water Conservancy Engineering
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Data Science in Water Conservancy Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (10 November 2024) | Viewed by 5535

Special Issue Editors

Prof. Dr. Jun Feng

E-Mail Website
Guest Editor
Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 211100, China
Interests: data management; spatiotemporal indexing and search methods; knowledge engineering; domain data mining; intelligent water conservancy
Dr. Yirui Wu

E-Mail Website
Guest Editor
College of Computer Science and Software Engineering, Hohai University, Nanjing 211100, China
Interests: service computing; artificial intelligence; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaodong Li

E-Mail Website
Guest Editor
College of Computer and Information, Hohai University, Nanjing 211100, China
Interests: data mining; mining algorithms and analysis methods of structured and unstructured data; research on knowledge graph; knowledge extraction and representation; knowledge reasoning technology; reinforcement learning; learning algorithm design and performance optimization method research

Special Issue Information

Dear Colleagues,

With the development of water conservancy engineering and the construction of infrastructure, water resources are properly managed and protected, contributing to the economic growth. It is noted that the social progress has exposed the drawbacks of current water conservancy engineering, and the management efficiency of water resources is low. The key reason lies in the lack of involving both expert experience and data intelligent to overcome the backwardness of current management technology. Therefore, water conservancy engineering needs intelligent management technology. As a promising field of data statistics, data science has shown considerable potential in the collection, analysis and utilization of water conservancy data. Recent research shows that there are various data science methods that have been used to meet the relevant needs of other fields, and have shown excellent performance. How to apply existing data science methods in water conservancy engineering, or realize new data science technologies more suitable for water conservancy scenarios, is of great significance for water conservancy data management.

Combined with applied mathematics, statistics, pattern recognition, machine learning and other methods, data science can predict, interpret and make decisions on water conservancy data by studying the “data world”. In addition, reliable data science methods should be customized or have an interpretable theoretical basis to promote the continuous progress and leapfrog development of water conservancy engineering. This Research Topic focuses on data science in water conservancy engineering, including data collection, data analysis, data decision-making and other related technological innovations. In order to connect novel data science with water conservancy engineering, and stimulate the potential of data science in water conservancy, this research welcomes researchers and practitioners from academia and industry to explore more new applications and technological innovations.

The topics of interest for this Special Issue include, but are not limited to:

  1. Data mining in water conservancy engineering;
  2. Data governance technology in water conservancy engineering;
  3. Big data analytics in water conservancy engineering;
  4. Data-driven technology in water conservancy engineering;
  5. Knowledge-aware technology in water conservancy engineering;
  6. Explainable data science in water conservancy engineering;
  7. Visualization of data science in water conservancy engineering;
  8.  Data science applications in water conservancy engineering.

Prof. Dr. Jun Feng
Dr. Yirui Wu
Dr. Xiaodong Li
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

  • smart water conservancy
  • data science
  • data mining and governance
  • big data analytics
  • explainable data science
  • data driven
  • knowledge-aware
  • visualization and applications

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Published Papers (3 papers)

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Research

21 pages, 8952 KiB  
Article
Study on the Fluctuating Load Characteristics of the Submerged Radial Gate in the High-Head Flood Discharge Outlet
by Xiudi Lu, Yakun Liu, Shoulin Tan, Wei Bao, Yangliang Lu and Xinmeng Zhao
Appl. Sci. 2024, 14(17), 7470; https://doi.org/10.3390/app14177470 - 23 Aug 2024
Viewed by 850
Abstract
The fluctuating pressure acting on the radial gate in the high-head flood discharge outlet is the main excitation source of flow-induced vibration. Therefore, this paper delves into the distribution characteristics of fluctuating pressure on the panel of the high-head submerged radial gate based [...] Read more.
The fluctuating pressure acting on the radial gate in the high-head flood discharge outlet is the main excitation source of flow-induced vibration. Therefore, this paper delves into the distribution characteristics of fluctuating pressure on the panel of the high-head submerged radial gate based on hydraulic model tests. Hydraulic tests were first conducted to obtain the distribution patterns of time-averaged pressure and the root mean square (RMS) of fluctuating pressure on the radial gate panel. Secondly, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and HHT method was employed to identify the causes of the fluctuating pressure on the gate panel. Finally, the ICEEMDAN-SSA (the sparrow search algorithm)–LSTM (long short-term memory) method was utilized to achieve accurate prediction of the fluctuating pressure on the gate panel. The results show that the time-averaged pressure in the middle of the gate panel is higher than that at the top and near the bottom edge, which differs significantly from the static pressure distribution. The RMS of the fluctuating pressure near the bottom edge is higher than that in the middle and at the top. The fluctuating pressure acting on the gate panel in the time domain can be regarded as a stationary process. The fluctuating pressure on the gate panel is caused by the combined diffusion and random mixing of multi-scale vortices in the turbulent eddy structure. The ICEEMDAN-SSA-LSTM combined method significantly improves the prediction accuracy of fluctuating pressure on the gate panel compared to the LSTM and ICEEMDAN-LSTM methods. Full article
(This article belongs to the Special Issue Data Science in Water Conservancy Engineering)
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19 pages, 4568 KiB  
Article
Pore Water Pressure Prediction Based on Machine Learning Methods—Application to an Earth Dam Case
by Lu An, Daniel Dias, Claudio Carvajal, Laurent Peyras, Pierre Breul, Orianne Jenck and Xiangfeng Guo
Appl. Sci. 2024, 14(11), 4749; https://doi.org/10.3390/app14114749 - 31 May 2024
Cited by 2 | Viewed by 1361
Abstract
Pore water pressure (PWP) response is significant for evaluating the earth dams’ stability, and PWPs are, therefore, generally monitored. However, due to the soil heterogeneity and its non-linear behavior within earths, the PWP is usually difficult to estimate and predict accurately in order [...] Read more.
Pore water pressure (PWP) response is significant for evaluating the earth dams’ stability, and PWPs are, therefore, generally monitored. However, due to the soil heterogeneity and its non-linear behavior within earths, the PWP is usually difficult to estimate and predict accurately in order to detect a pathology or anomaly in the behavior of an embankment dam. This study endeavors to tackle this challenge through the application of diverse machine learning (ML) techniques in estimating the PWP within an existing earth dam. The methods employed include random forest (RF) combined with simulated annealing (SA), multilayer perceptron (MLP), standard recurrent neural networks (RNNs), and gated recurrent unit (GRU). The prediction capability of these techniques was gauged using metrics such as the coefficient of determination (R2), mean square error (MSE), and CPU training time. It was found that all the considered ML methods could give satisfactory results for the PWP estimation. Upon comparing these methods within the case study, the findings suggest that, in this study, multilayer perceptron (MLP) gives the most accurate PWP prediction, achieving the highest coefficient of determination (R2 = 0.99) and the lowest mean square error (MSE = 0.0087) metrics. A sensitivity analysis is then presented to evaluate the models’ robustness and the hyperparameter’s influence on the performance of the prediction model. Full article
(This article belongs to the Special Issue Data Science in Water Conservancy Engineering)
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21 pages, 4558 KiB  
Article
Data-Driven and Knowledge-Guided Heterogeneous Graphs and Temporal Convolution Networks for Flood Forecasting
by Pingping Shao, Jun Feng, Yirui Wu, Wenpeng Wang and Jiamin Lu
Appl. Sci. 2023, 13(12), 7191; https://doi.org/10.3390/app13127191 - 15 Jun 2023
Cited by 3 | Viewed by 1744
Abstract
Data-driven models have been successfully applied to flood prediction. However, the nonlinearity and uncertainty of the prediction process and the possible noise or outliers in the data set will lead to incorrect results. In addition, data-driven models are only trained from available datasets [...] Read more.
Data-driven models have been successfully applied to flood prediction. However, the nonlinearity and uncertainty of the prediction process and the possible noise or outliers in the data set will lead to incorrect results. In addition, data-driven models are only trained from available datasets and do not involve scientific principles or laws during the model training process, which may lead to predictions that do not conform to physical laws. To this end, we propose a flood prediction method based on data-driven and knowledge-guided heterogeneous graphs and temporal convolutional networks (DK-HTAN). In the data preprocessing stage, a low-rank approximate decomposition algorithm based on a time tensor was designed to interpolate the input data. Adding an attention mechanism to the heterogeneous graph module is beneficial for introducing prior knowledge. A self-attention mechanism with temporal convolutional network was introduced to dynamically calculate spatiotemporal correlation characteristics of flood data. Finally, we propose physical mechanism constraints for flood processes, adjusted and optimized data-driven models, corrected predictions that did not conform to physical mechanisms, and quantified the uncertainty of predictions. The experimental results on the Qijiang River Basin dataset show that the model has good predictive performance in terms of interval prediction index (PI), RMSE, and MAPE. Full article
(This article belongs to the Special Issue Data Science in Water Conservancy Engineering)
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