Application of data mining technology and intelligent information technology in the construction and management of the water conservancy project in Area A

The water conservancy (WC) project is one of the important infrastructure construction projects, and with the continuous expansion of the construction scale, the technical and management requirements in the construction of WC projects are also constantly improving. Because there is a lot of information involved in the construction process of WC projects (a large amount of information can be generated, such as personnel deployment, contract changes, and material supply), the traditional construction management of WC projects is unable to effectively manage this information, resulting in a low utilization rate of the information of these WC projects, which is not conducive to the development of WC project construction and management. Therefore, this paper uses intelligent technology to manage these data, applies information technology to WC project construction and management, and further improves the level of WC information management that is conducive to solving the problem of the low utilization rate of WC information. However, there has been less study of hydrology runoff in WC projects. To address this issue, this paper uses data mining technology (DMT). In Area A, for example, to study the reservoir runoff, a data mining model is established using runoff data collected through a year. This model helps to discover valuable potential knowledge and improve it, solves the prediction error of runoff, helps in better construction and management of WC projects, improves the utilization rate of water resources, hopes to further improve the quality of WC project construction and project management level, and helps in the management of WC projects through a more scientific and professional approach.

The rapid construction of WC projects has also led to the continuous increase of WC project management systems. WC project informatization has become one of the development directions of WC projects today. However, there are still many shortcomings in the construction and management of WC projects. The provision of services is only professional and rarely involves management decision-making. Therefore, few experts have conducted research. Through in-depth research on the relationship between WC project construction and environmental protection, Zhang et al. proposed constructive suggestions on how to scientifically and reasonably plan the construction of WC projects. Finally, a simulation experiment was conducted using an example (Zhang et al. 2021). In order to ensure the sustainable growth of the rural economy, Yuan thought it was necessary to further carry out research on small-scale WC projects to solve the seepage problem in rural areas. Based on the application significance and anti-seepage technology of small-scale rural WC projects, he combined specific examples to specifically discuss the role of high-pressure jet technology, which can provide a reference for the development of WC projects in the later stage (Yuan 2022). Liu believed that large-scale WC projects refer to construction projects that achieve a certain scale in agriculture, power generation, flood control, environmental protection, and shipping (Liu et al. 2019). Sun et al. believed that large-scale WC projects had great economic and social significance, and they used data from nearly 1 billion different water levels during the dam construction process to analyze the radioactive concentration changes in the water of the Three Gorges Region. They found that when assessing the impact of radiation levels on large-scale WC projects, samples collected at different times should be analyzed to make an accurate assessment (Sun et al. 2019). In order to better manage extreme events and water resources under changing climate conditions, a study was conducted, using the data of three stations to quantify the seasonal and annual variations and trends of rainfall and temperature in the past three decades, using a nonparametric test to estimate the magnitude of monotonic trend, and statistical analysis of rainfall series data sets showed that the occurrence of rainfall is unpredictable and irregular and the seasonal and annual rainfall trend is negative (declining) in all three climate stations (El Kasri et al. 2021). The vast majority of scholars have conducted various research works on WC project construction and management, but few scholars have considered applying DMT and intelligent information technology (IIT) to the research topic of this article.

Data mining can be divided into clustering, correlation analysis, classification, and prediction. In recent years, many scholars from other countries have widely applied various technical methods of data mining to various fields, especially in the construction of WC projects. The use of DMT is conducive to the realization of intelligent informatization in WC project construction and management. Shi and Zhu used efficient mining methods to solve the problem of low data mining accuracy for traditional ecological environment monitoring data around WC projects. For this reason, they proposed an efficient mining method for ecological environment monitoring data around WC projects, using attribute correlation to establish a data mining model for the environmental monitoring database around WC projects (Shi & Zhu 2019). The use of architectural landscape design by Li and Wang has effectively improved the management level and efficiency of WC projects, promoting the sustainable development of the WC industry. In WC project management, they adopted a modern architectural landscape design, which conforms to the background of the information era, can optimize the WC project construction and management engineering system, and can solve some problems in WC project management (Li & Wang 2020). Huang et al. believed that DMT can discover and analyze potential risk laws and patterns, and they used association rule algorithms to conduct in-depth association rule mining on attributes such as entity type, project grade, project category, and technical issues in regulatory data. Finally, they put forward suggestions on how to strengthen the quality and safety supervision and management of WC projects based on the actual situation (Huang et al. 2018). Experts and scholars have conducted profound research on the WC engineering of DMT, so there is a theoretical basis for selecting it for the research in this article.

The research purpose of the construction and management of WC projects is mainly to provide a management system with clear objectives and simple operations for the development of the construction and management of WC projects at this stage (Huang et al. 2018). In order to provide reference for the development and research of the subsequent WC project construction and management, this paper will use DMT and IIT to study the construction and management of WC projects, and the mining of WC project construction data through DMT, in Area A as an example. Using DMT, we can gain valuable insights into water conservancy data, specifically by conducting a comprehensive data mining analysis of reservoir runoff for Area A throughout the entire year. This analysis aims to uncover the underlying relationships within the data and enable the collection of a larger volume of data. By conducting extensive data collection and investigation, we strive to address the issue of runoff prediction errors that commonly arise in other construction and management practices of WC projects. This study will contribute to improving the accuracy of runoff prediction for Area A, thus supporting more precise forecasting in subsequent projects. Furthermore, the completion of data mining activities will uncover the intrinsic value within WC project data. Through the data, we can reduce management costs and maximize the effectiveness of WC projects, enabling them to fulfill their intended purpose more effectively.

A construction project generally refers to a project that is constructed in accordance with an overall design, and that would organize in an orderly way a large number of human, material, and financial resources within a certain range of time, space, quality, and cost to form a whole complete system with a usable value (Owusu et al. 2019; Rashid 2020). According to the concept of the whole-process management of WC engineering construction projects, the life cycle of WC engineering construction projects includes several stages. The first stage is the preliminary preparation stage that is mainly used for the corresponding design and preparation of WC engineering construction projects. The second stage is the formal implementation stage of the project and is mainly based on the plan of the preparation stage, and the designed plan is systematically put into the project to achieve specific project objectives. The third stage is the project completion stage, which mainly includes summarizing and concluding the entire WC project construction work.

IIT mainly uses data as information, information as knowledge, and knowledge as intelligence through its integrated functions (Zhang et al. 2020). Today, information technology is accelerating its development toward a new direction of intelligence and informatization, and informatization is entering a stage of all-round and high-speed development. The application of information technology has been continuously deepened vertically, from international to national, to all walks of life in all fields, such as in the fields of education, medical care, enterprise, scientific research, railway, e-government, and other fields (Park et al. 2020). With the rapid development of big data technology, the current level of information construction is gradually maturing. The goal of information construction is to use information technology to promote the development and structural upgrading of various industries in the development of applied information technology, and to use information technology to change the decision-making methods and management methods of issues, forming a more significant impact. The scope of informatization is very broad, including national defense informatization, weapons, equipment informatization, military informatization, etc. Economic informatization includes multiple fields such as informatization zones in various industries such as agriculture, manufacturing, industry, and service industries.

where t_ij represents engineering information, represents river channel management and represents quality management.

Data mining is also known as knowledge discovery in the academic field, which means the process of extracting potentially valuable information and knowledge hidden in incomplete and random large amounts of actual data through an algorithmic search (Shakiba Zahed et al. 2020; Moosavi et al. 2022). DMT is an interdisciplinary subject that involves multiple theoretical methods, including theoretical methods such as statistics, pattern recognition, machine learning, artificial intelligence, database technology, and visualization technology. Data mining is a process of finding knowledge for decision-making purposes, which includes approximately five parts, namely data preparation, data preprocessing, feature extraction, modeling, and analyzing data (Yee et al. 2018). Data preparation involves accessing a database to collect and organize questionnaires and capture network data to obtain the required data. Data preprocessing can ensure the accuracy of model establishment, and the accuracy, integrity, and uniformity of sample data can be ensured through data cleaning and data conversion. Feature extraction selects some features that have a strong correlation with the target and extracts feature values from each of the data. Modeling refers to using different algorithms to build models for different problems. Analyzing data refers to analyzing mining results and evaluating their effectiveness and accuracy.

Only with specific DMTs can potentially relevant data information be extracted from a vast volume of data during the application process. The following are some of the most popular DMTs: the silt data categorization and integration approaches. When dealing with large volumes of data, accurate categorization and integration based on data qualities are crucial. This involves grouping data with similar conditions or patterns together, ensuring that all data points have appropriate classifications. By avoiding blind analysis of irregular data and simultaneously increasing data mining speed, more efficient and reliable results can be obtained. In the face of extensive data, examining each data point individually becomes essential. This approach focuses on finding relationships between data points to facilitate effective evaluation and processing.

Therefore, the problem of mining correlation rules can also be attributed to mining frequent item sets. Specifically, X in is the predicted value of runoff in hydrology, is the true value, and is the confidence between them.

The main focus of Global Positioning System (GPS) technology in WC project management is the collection of WC project information, which is mainly based on the measurement of WC-project-related parameters (Dibs et al. 2023; Koch et al. 2019). In the past, the collection of information on WC project management was mainly done manually, which would lead to low work efficiency. It is prone to errors, which is not conducive to improving the management level of WC projects. The research on WC project management information based on GPS technology can further ensure the accuracy of information data and avoid excessive errors. To use GPS technology for measurement, the following aspects must be emphasized: the first is to pay attention to different measurement methods. The measurement methods using GPS measurement technology include static, dynamic, and fast static measurements. The fast static method mainly focuses on the setting of reference stations. The dynamic measurement method must not only meet the standards for the distance between the reference station and the mobile station but also prevent the occurrence of closed images. For static measurement, a scientific and reasonable control network should be established based on the measurement objectives, so that the measurement points can communicate with each other. The second aspect is to determine the appropriate measuring equipment.

Satellite positioning technology is also one of the common IITs in the construction and management of WC projects, and it has been developed for decades in China and has been widely used in many fields. Due to the advantages of simple operation, precise data, and speed, this technology has many advantages in the construction and management of WC projects. If relatively accurate three-dimensional coordinates can be obtained in a short time, the impact of external interference factors is not significant, and the coverage is wide. Within a certain range, information required for WC construction is continuously provided. At present, compared with wireless positioning technology, satellite positioning technology can be immune to external interference factors, and can still achieve timely and accurate positioning in the event of disasters, playing a role in earthquake prevention and disaster reduction.

With the continuous development of IIT, WC engineering construction is gradually developing in this modern direction. The management of WC project construction includes many aspects, including hydrological forecasting, river management, quality management, safety management, cost management, document management, and other aspects. However, traditional management methods have significant shortcomings and cannot effectively collect and process relevant WC project information. It is relatively easy to make mistakes, thereby affecting the level of construction and management of WC projects. The application of IIT provides many conveniences for the related work of construction and management of WC projects.

It can fully establish the connection with Geographic Information System (GIS). The scale of WC project construction is generally relatively large, so it may be affected by terrain and climate during the construction process. Therefore, the reasonable construction of WC projects must have strong data analysis capabilities. The process of building and integrating data from all problems encountered in engineering construction is very complex.

The data of water pumps in the construction of WC projects in Area A are collected to lay the foundation for the hydrological runoff prediction of Reservoir A. The specific data are shown in Table 1.

The application of DMT in WC project construction is conducive to predicting the runoff of the constructed reservoir through data models, providing convenience for WC project construction and management. As an important link in the management process of WC projects, hydrological forecasting utilizes data mining application technology and introduces its specific operation process into hydrological forecasting. This paper uses DMT to predict the runoff of Reservoir A for each month from January to December 2020. In order to further verify the accuracy of DMT in predicting WC project construction runoff, this paper compares it with machine-learning-based technology. The runoff values predicted by the two technologies are compared with the actual values, and the specific data are displayed in Table 2.

As displayed in Figure 3, using DMT to predict runoff in hydrology, the error rate between the predicted value and the actual value is smaller than the error rate using machine learning technology for prediction. As displayed in Figure 3(a), there is a 3.44% difference between the maximum and minimum error rates for runoff prediction using DMT. Among them, the maximum error rate for runoff prediction using DMT was 6.75% in October, 2.37% lower than using machine learning technology. In this paper, using DMT, the error rate of runoff prediction in April is the smallest, only 3.31%, which is 7.5% lower than using machine learning technology. As displayed in Figure 3(b), the difference between the maximum and minimum error rates of runoff prediction using machine learning technology is 6.2%. Among them, using machine learning technology has the largest error rate in runoff prediction in November, with 12.41%, which is 6.94% higher than using DMT. Using machine learning technology to predict runoff in January has the smallest error rate, only 6.21%, but it is still 2.19% higher than using DMT. From the above data it can be found that the use of DMT for data prediction accuracy is better, and DMT can improve the prediction accuracy of runoff. This is achieved through data collection and analysis, taking into account various factors such as weather conditions, rainy or sunny days, and their impact on reservoir runoff. At the same time, the influence of irrelevant factors can also be excluded, to make better predictions of the reservoir runoff, get a better prediction accuracy, and improve the operation efficiency of the project.

As displayed in Figure 4, the efficiency of information management methods based on IIT and DMT for various aspects of WC projects is much higher than that of traditional management methods. As displayed in Figure 4(a), the efficiency of information management methods for WC project information processing is over 93%; as displayed in Figure 4(b), the processing efficiency of traditional management methods is below 87%. Among them, information management methods have the highest processing efficiency in WC project quality management with 96.71%, which is 11.59% higher than traditional management methods. The information management method has the lowest processing efficiency in the safety management of WC projects, only 93.84%, which is 9.24% higher than traditional management methods. Traditional management methods have the highest processing efficiency in WC project document management, with 86.38%, which is 8.93% lower than information management methods. However, the traditional management methods have the lowest processing efficiency in the cost management of WC projects, only 83.47%, which is 12.33% lower than the information management methods. The two management methods have the smallest difference in processing efficiency in river management of WC projects, and the informatization management method is 8.56% higher than the traditional management method. It can be supported by IIT and DMT to conduct WC project construction management, which not only improves the efficiency of management, but also provides assurance for environmental and ecological construction and provides technical support for analysis and monitoring after the completion of WC projects.

As displayed in Figure 5, this paper applies two technologies to the construction management of WC projects, allowing experts to rate the various changes they bring. It is very intuitive to see that experts in DMT score far higher than machine learning technology in all aspects. As displayed in Figure 5(a), experts in DMT score above 7.7 points, while as displayed in Figure 5(b), experts in machine learning technology score below 7.4 points. Among them, DMT has the highest expert score on the utilization rate of water resources in No. 3, with a score of 8.55, 1.55 points higher than machine learning technology. While DMT has the lowest expert score of 7.75 in terms of No. 4 and information resource sharing, it is still 0.9 points higher than machine learning technology. Machine learning technology has the highest expert score of 7.35 on the improvement of management ability of managers in No. 5, but it is still 0.65 points lower than DMT. Machine learning technology has the lowest expert score for improving the management efficiency of No. 1 WC projects, with only 6.5 points, 1.95 points lower than DMT. This article applies DMT to the construction and management of WC projects, which is conducive to reducing management costs, improving management efficiency, better playing the value of WC projects, and improving the utilization rate of water resources. Because DMT, compared with machine learning technology, for all kinds of information mining speed is faster and more accurate, it will help better use the data information to allocate water resources, improve the utilization of water resources, and at the same time extract the data of the WC project construction, to quantify the characteristics of each project index and improve the cost of project management.

It is urgent and important to improve the rational allocation of regional water resources to serve as a guide for water resource management and security of the water supply in Area A. Research on the rational allocation of water resources and the construction of ecological water conservation projects is a very complicated and difficult task. To address this, the utilization of DMT and other advanced techniques can significantly contribute to the management of water conservation project construction. By applying DMT, a comprehensive analysis of various types of project management data can be conducted. This allows for the extraction of valuable insights and the identification of correlations and patterns within different water conservation project data. Currently, the degree of correlation and pattern mining in water conservation project data is relatively low, resulting in historical regulatory data being insufficient in providing effective guidance for subsequent supervision work. By using DMT, the relationship between data can be mined, and the existing regulatory data of water conservancy projects can be analyzed and mined faster and can be accurately analyzed, providing an efficient supervision, development of intelligent tools, and innovative management approaches.

With the continuous development of WC projects, an engineering system with functions such as flood control, water supply, and tourism has been formed, playing an extremely important role in the development of the national economy. In long-term development, WC projects have begun to take shape and have made some achievements, but in the management of WC projects, management methods are relatively backward. Some managers only attach importance to construction and neglect management, and the resource utilization rate of many projects is gradually reduced, which may even lead to a significant shortening of the service life of some WC projects, making it difficult to demonstrate the actual value of WC projects. This paper examines the construction management of water conservation projects, advances the engineering system for allocating water resources, maintains the water resources protection project, raises the bar for regional water function, ensures good regional ecology, and ensures regional flood control safety. It also significantly raises the bar for water conservation project management and gives modern water management a powerful impetus. Additionally, data processing and analysis of pertinent water conservation data can be done using DMT, which makes it easier for staff to access data, increases the precision of runoff prediction, standardizes project construction management, and significantly raises the level and efficiency of work. However, due to time constraints, there are still some shortcomings in this paper. This paper has not conducted research on the WC project construction management indicator system, and it is recommended to conduct research on the WC project management indicator system in subsequent work.

The authors received no financial support for the research, authorship, and/or publication of this article.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.