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: 20 April 2024 | Viewed by 1860
Special Issue Editors
Interests: data management; spatiotemporal indexing and search methods; knowledge engineering; domain data mining; intelligent water conservancy
Interests: computer vision; artifical intelligence; multimedia computing; intelligent water conservancy
Special Issues, Collections and Topics in MDPI journals
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:
- Data mining in water conservancy engineering;
- Data governance technology in water conservancy engineering;
- Big data analytics in water conservancy engineering;
- Data-driven technology in water conservancy engineering;
- Knowledge-aware technology in water conservancy engineering;
- Explainable data science in water conservancy engineering;
- Visualization of data science in water conservancy engineering;
- Data science applications in water conservancy engineering.
Prof. Dr. Jun Feng
Dr. Yirui Wu
Dr. Xiaodong Li
Guest Editors
Manuscript Submission Information
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Keywords
- smart water conservancy
- data science
- data mining and governance
- big data analytics
- explainable data science
- data driven
- knowledge-aware
- visualization and applications
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Prediction of cement intake based on 3D fracture connectivity
Authors: Zongxian Liu
Affiliation: State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China;
Yalong River Valley Hydropower Development Co., Ltd., Chengdu 610051, China.
Abstract: Cement intake is an important indicator that must be precisely predicted. However, most existing models overlook the vital factor - the connectivity of fractures, which will block the further improvement of prediction accuracy. To this end, a method was developed to predict the cement intake based on 3D fracture connectivity. Firstly, a 3D fracture model based on the discrete fracture network (DFN) method was established. Furthermore, digital drilling and depth first search (DFS) methods were applied to calculate the parameters that can characterize the connectivity of fractures. Finally, a prediction model for cement intake was proposed that combined deep belief network (DBN) with genetic algorithm (GA), and it has been demonstrated to be more effective and have an advantage over the previous methods by a case study.