Dam safety emergency response decision-making method based on a graph database and language model

As the country with the highest number of dams in the world, China’s dam safety management and risk control are crucial to public safety and economic development. This paper systematically analyzes the current status of dam safety in China, explores the causes of accidents such as design and construction defects, poor operation management, the impact of natural disasters, and proposes comprehensive dam safety management measures based on these analyses.

Comparing graph databases with traditional, e.g., relational databases, some important database features are often missing there. Particularly, a graph database schema including integrity constraints is not explicitly defined, also a conceptual modelling is not used at all. It is hard to check a consistency of the graph database, because almost no integrity constraints are defined. In the paper, we discuss these issues and present current possibilities and challenges in graph database modelling. Also a conceptual level of a graph database design is considered. We propose a sufficient conceptual model and show its relationship to a graph database model. We focus also on integrity constraints modelling functional dependencies between entity types, which reminds modelling functional dependencies known from relational databases and extend them to conditional functional dependencies.

Certain frontline dam safety management personnel lack the ability to diagnose dam hazard issues and often find it difficult to identify potential risks in practice. Existing causal analysis methods for dam safety diagnosis struggle to provide specific reasoning paths and quantify risk probabilities. To address this gap, this study proposes a dam safety diagnostic method based on Bayesian networks (BNs). First, historical cases of dam safety hazards were collected and classified to extract various types of hazard issues, abnormal manifestations, and underlying causes, which were used as nodes within the BN. Correlation analysis was then performed to identify relationships among the nodes, enabling the construction of directed edges that form the BN structure. The degree centrality algorithm was employed to analyze the prior probabilities of parent nodes, while Bayes’ theorem was applied to calculate the conditional probabilities of the child nodes, generating conditional probability tables for all nodes within the network. Using the BN’s posterior probability inference method, the probabilities of hidden hazards in a target dam were calculated, facilitating accurate diagnosis and root cause tracing of potential risks. Finally, a case study involving a hidden hazard in a domestic earth-rock dam was used to validate the proposed method. The results demonstrate that the method efficiently utilizes a large number of scattered dam hazard cases, is less affected by subjective factors, provides clear reasoning links and risk probabilities, and can accurately identify dam hazard issues and trace their root causes, offering technical support for dam operation and safety management personnel.

The Department of Irrigation and Drainage Malaysia (DID) has established a national guidelines on dam safety management for a safe governance of dams and resilient community surrounding the dams in Malaysia, titled Malaysia Dam Safety Management Guidelines (MyDAMS) in September 2017. This is the first national guidelines ever established. While the guidelines outline the best management practices on dam safety management, there is still a major gap towards its implementation into the dam industry. MyDAMS emphasized on all aspects of dam safety comprising legal requirements, potential hazards, safety principles, safety management system, investigation, design, construction, commissioning, operation and maintenance, surveillance and safety review, rehabilitation, emergency preparedness, changes and decommissioning of dams. There is a need to establish the framework of the government agencies towards its dam safety management as per MyDAMS. Meanwhile, the stakeholders have to gear up immediately towards capacity building to achieve the overarching objective of dam safety management as stated in MyDAMS. This paper discussed some required framework and way forward to be carried out by stakeholders on what entail next after the recent establishment of MyDAMS.

Comparing graph databases with traditional,e.g., relational databases, some important database features are often missing there. Particularly, a graph database schema including integrity constraints is mostly not explicitly defined, also a conceptual modelling is not used. It is hard to check a consistency of the graph database, because almost no integrity constraints are defined or only their very simple representatives can be specified. In the paper, we discuss these issues and present current possibilities and challenges in graph database modelling. We focus also on integrity constraints modelling and propose functional dependencies between entity types, which reminds modelling functional dependencies known from relational databases. We show a number of examples of often cited GDBMSs and their approach to database schemas and ICs specification. Also a conceptual level of a graph database design is considered. We propose a sufficient conceptual model based on a binary variant of the ER model and show its relationship to a graph database model, i.e. a mapping conceptual schemas to database schemas. An alternative based on the conceptual functions called attributes is presented. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Efficiently conducting seismic hazard assessment and retrospective testing of seismic prediction strategies relies on the integration of proprietary seismic data into the forecasting analysis and decision process, however, the large volume and structural diversity of proprietary seismic data, and the fact that related knowledge is stored in multiple databases, throw a stumbling block to data integration. Hence, this paper introduce the Seismic Knowlee Graph (SKG), a flexible and powerful platform currently containing nearly 34,043 nodes and 32,248 relationships, representing the relevant observed data, public databases. The platform contains a variety of proprietary seismic data in different formats and from different data sources, providing data support to realize the data modeling and business process processing of strong and impending earthquake prediction model, so as to build a new model of regional earthquake forecasting study. In this work, we perform data organization of relational databases, and after graph database modeling and data import, we build knowledge graph using HugeGraph, a kind of NoSQL graph database, which organized easily extensible architecture that can be easily scale to new nodes and relationships when generates new data . We use WebGIS technology to build intuitive interfaces to visual graphical databases for user interaction, querying, and roaming the SKG. We illustrate the possibility of using a graph database to build a knowledge graph for seismic forecasting, which establishes a seismic forecast base database, a distributed database system for proprietary earthquake data. This graph database of relational queries has the possibility to reveal new relationships between heterogeneous seismic data and metadata, and it can be demonstrated that the graph structure can provide an efficient and reliable data support service for testing the validity of technical solutions for determining the urgency of strong earthquake generation.

Abstract. Dams as well as protective dikes and levees are critical infrastructures whose associated risk must be properly managed in a continuous and updated process. Usually, dam safety management has been carried out assuming stationary climatic and non-climatic conditions. However, the projected alterations due to climate change are likely to affect different factors driving dam risk. Although some reference institutions develop guidance for including climate change in their decision support strategies, related information is still vast and scattered and its application to specific analyses such as dam safety assessments remains a challenge. This article presents a comprehensive and multidisciplinary review of the impacts of climate change that could affect dam safety. The global effect can be assessed through the integration of the various projected effects acting on each aspect of the risk, from the input hydrology to the calculation of the consequences of the flood wave on population and assets at risk. This will provide useful information for dam owners and dam safety practitioners in their decision-making process.

The use of risk assessment in dam safety management as advocated by the International Commission On Large Dams (ICOLD) will be greatly enhanced by Risk and Uncertainty in Dam Safety, an authoritative, comprehensive and valuable contribution to dam safety practices. Through the presentation of a systematic and integrated process, Risk and Uncertainty in Dam Safety assists the dam owner in evaluating the needs for dam safety improvement, selecting and prioritizing remedial and corrective actions, and improving the operation, maintenance and surveillance procedures. As a result of the unique cooperation among experienced and knowledgeable dam owners, dam safety mangers and engineers, and experts in the theoretical basis for risk assessment, Risk and Uncertainty in dam safety contains a thorough review of how state-of-the-art 'the industry' has become, provides lessons from first hand practical experience, and gives significant new contributions that will enhance understandingof the risk assessment and management process and how to apply it effectively, increasing awareness and reduce complacency regarding dam safety issues. Risk and Uncertainty in Dam Safety will appeal not only to industry specialists but also to readers outside the dam engineering community due to its general and excellent treatment of the various topics in the integrated process of risk assessment

PurposeFarm dam safety in Australia is being flouted and sustainability of catchments compromised because of the potential and severe consequences of dam failure. Hence, the purpose of this paper is to explore policy issues associated with safety of farm dam water storage through a comparison of developments in two Australian states against an analysis of international benchmarks and to provide an exemplar of best practice.Design/methodology/approachA strategic review and content analysis is firstly undertaken to establish international dam safety policy benchmarks ranging from minimum to best practice as well as selection guidelines for varying circumstances, and to identify an exemplar best practice model. Longitudinal study over a 12‐year period then provides the basis for case analysis in order to reinforce the established minimum level benchmark and to demonstrate the application of the benchmarked model policy selection guidelines.FindingsResearch results show that in Australia, South Australia is lagging international benchmarks for on‐farm dam safety management in a number of ways whilst a second state, Tasmania, provides leadership in this respect. The paper adds to the existing international benchmarking literature by identifying updated international best practice in private/farm dam safety assurance policy whilst establishing and providing longitudinal case study reinforcement for an acceptable minimum level benchmark in this area. The updated policy guidelines presented can be used to determine appropriate dam safety policy for any jurisdiction.Originality/valueThe paper provides an original contribution of analysis, establishment and case study validation of international benchmarks and guidelines on developing appropriate dam safety management and assurance policy for varying jurisdictional circumstances. In addition, it provides an updated exemplar of how policy benchmarks can go towards addressing cumulative threats of smaller dams in catchments not previously addressed.

Due to an increasing amount of dam damage or break in resent years, great importance is attached to dam safety management in many countries. This paper analyzes the status of Chinese dam safety management according to the integral safety concept for dam including structural safety, dam safety monitoring, operational safety and emergency planning. Comparing with the safety management of large and medium-sized dams, the level of small-sized dam's safety management is relatively low. It also studies application of risk management technologies in China and presents a quantitative method to calculate the vulnerability to dam-break flood at downstream area.

At present, there are few research methods that can convert any relational database into a graph database, and most of them are based on a specific field data set to build a relational database, and then perform simple conversion through the characteristics of the data set.Aiming at this problem, a universal conversion method is proposed. Firstly, converted the most basic component tables name, records, and fields in the relational database into labels, nodes, and corresponding attributes of the nodes under the graph database; secondly, used the intermediate connection table method to convert the foreign keys in the relational database into the relationship of a graph database between the nodes; then some constraint issues in relational databases, such as multiple primary key issues, indexes, and no default values, were optimized to form a final graph database model that met expectations; finally, Realized the effective migration of large quantities of data in the relational database to the constructed graph database model. In the experiment, the above method was used to successfully convert a relational database to a graph database, and the database construction, data import, SQL query and Cypher language query were performed for the database before and after the conversion, and through the analysis and comparison of data integrity, time cost, result validity,which shows that the integrity and operability of the database before and after conversion are consistent, and the data processing efficiency of the database is much higher than that of the relational database, which verifies that the method in this paper is feasible.

The advantages of Relational Database Management System (RDBMS) model and design methodology are being utilized by industry/institutions for any software design and implementation. The future of RDBMS certainly will be the Graph Databases with NoSql methodologies, which is emerging as beyond of relational model. In this paper, there is a need to highlight all the databases evolved after RDBMS. They couldn't stay in market for so long period and survey has been made to highlight those databases after RDBMS. Relational Database Management System has certain advantages like (i) Storing in Tables, Column and Rows (ii) Data Storing in Normal Form (iii) Easy to use via SQL to retrieve information via complex join operators (iv) Maintainability via Reverse Engineering (v) Indexing and quick search. Due to these inherent features of RDBMS and SQL, it is necessary to explore and compare RDBMS with NoSQL methods to avoid complex join operation. Recently, numerous software industries and research institutions are trying their old RDBMS system to be re-engineered into some other architecture via nodes, edges and relationships where different type of information can be stored easily. So, it is a big challenges for any industry and institutions how quickly they can re-engineer their old RDBMS into Graph Databases which is also called now-a-days the future of databases. In this project, it is highlighted that the importance of the re-engineering work lies in three different directions such as (i) Comparison of RDBMS with GDBMS (Graph Database Management System) where face book, twitter, Amazon, Google are adopting (ii) Survey work of Graph Databases and (iii) Graph Database Models have increasingly become a topic of interest. The representation of data in the form of a graph lends itself well to structure a data with a schema. No standard system of query languages yet had been found to have been unique and stable for graph databases. Research and industry adoptions will determine the future direction of graph databases.(iv) Beyond RDBMS artifacts were established by industry and academics. It feeds a series of recycling collectives trying to eke out an existence of positive incentives and principles.

Graph databases have been widely employed for representing connected pieces of information for different kind of domains. The data model embraces relationships as a core aspect to connect objects, and organizes everything into a network for efficient query processing of versatile applications, e.g., on-line social networking, metropolitan traffic modeling, marketing channels simulations or even counterterrorism analysis. As an emerging technology for encoding network structures, graph databases are also widely used as an infrastructure for social network analytics, which help us understand some phenomenon or hidden knowledge in buzz marketing, technology trends or public issues regarding social behaviors. Although many graph database management systems have been developed, there are still no formal definitions for theoretical graph database modeling. In this paper, we will present a formal definition for graph database model, extend the concept of data warehouse into graph warehouse, and define the basic elements of a graph warehouse for the development and derivation of graph-based multi-dimensional business intelligence through on-line analytical processing (OLAP) on graph databases.

This paper studies the RDF model from a database perspective. From this point of view it is compared with other database models, particularly with graph database models, which are very close in motivations and use cases to RDF. We concentrate on query languages, analyze current RDF trends, and propose the incorporation to RDF query languages of primitives which are not present today, based on the experience and techniques of graph database research.

Dam failures pose catastrophic risks to human life and property, necessitating robust safety monitoring systems for risk mitigation. However, the specific contributions of distinct monitoring modalities to dam safety remain inadequately characterized, particularly regarding their differential impacts on structural integrity assessment. This study investigates the correlation between diverse monitoring modalities and dam structural safety through a comprehensive analysis of the Silin Hydropower Station dam. We analyzed 324 datasets collected from nine types of monitoring sensors installed across 36 dam cross-sections. Statistical analyses including one-way ANOVA, cluster analysis, and principal component analysis (PCA) were employed to quantify the influence patterns of monitoring parameters. The safety impact levels of all 36 cross-sections were systematically ranked, establishing a prioritized reference framework to inform decision-making in dam safety management. Unlike conventional dam safety assessments that predominantly rely on subjective empirical judgments, this study introduces an objective methodology integrating principal component analysis (PCA) of heterogeneous monitoring data across multiple dam cross-sections. The analytical outcomes were systematically quantified, hierarchically ranked, and visualized through multidimensional mapping techniques. The results demonstrated that variations in fissure (X2), horizontal displacement (X3), tilt (X4), stress (X6), soil-displacement (X8), and denotes water-level (X9) exerted highly significant effects on dam safety (p < 0.001). The first two principal components cumulatively accounted for 74.1876% of the total variance, with eigenvalues reaching 6.6769. In the comprehensive evaluation, cross-section T4 (T4) obtained the maximum score (0.8500), while cross-section T35 (T35) showed the minimum score (0.0175). In conclusion, the analysis revealed that X9, X8, X2, X3, and X4 exerted significant impacts on dam safety, while cross-section T4 achieved the highest comprehensive evaluation score. This approach employs Principal Component Analysis (PCA) with integrated scoring to reduce multivariate dimensionality, enabling rapid identification of key monitoring sections critical to dam safety, and demonstrates broad applicability for dam safety monitoring.