Scientific decision support system of marine environmental management in China’s Yellow Sea and Bohai Sea based on cloud computing mode

Abstract

Due to the large amount of pollutant discharge, the environmental pollution capacity of the coastal waters of the Yellow Sea and Bohai Sea in China is seriously overloaded, which has caused unacceptable impact on the marine ecological environment. Based on this, this paper is based on the cloud computing model of marine environmental management scientific decision-making evaluation algorithm, constructing a model of complex network dynamic correlation characteristics, and externally describing water bloom. Through the weight parameters in the cloud model, it is effectively applied to the evaluation of marine environmental management science decision support system. According to the complexity characteristics of cyanobacterial bloom management decision, the cyanobacteria bloom control decision model is inferred, and the intuitionistic fuzzy rough set algorithm is improved. The similarity is calculated, the best matching case is found, and the experts are adjusting to form a governance plan. The research results show that the scientific decision-making evaluation algorithm of marine environment management based on cloud computing model is effective for the verification of marine environmental management science decision support system evaluation system, embedding eutrophication evaluation method and cyanobacteria water bloom governance decision-making method into water quality remote monitoring and cyanobacteria water China’s governance decision-making system, to achieve the decision-making of cyanobacteria bloom control in the lake water body.

Keywords

Cloud computing ocean environmental management scientific decision

1. Introduction

China’s industrial and agricultural industries have developed rapidly since the 1980 s, but this has led to more environmental problems. The pollutants caused by a large number of industrial and agricultural production have caused the water environment to deteriorate [1]. In 2012, the United Nations Environment Programme (UNEP) updated the results of a new round of global environmental surveys. According to data from the environmental report, Global Environment Outlook 5, global river nutrient exports have increased by about 15% since 1970. The total productivity of lake algae and large plants are increased by 74% [2]. At the same time, China’s water environment problems are also not optimistic. In May 2015, the Ministry of Water Resources of the People’s Republic of China released the 2014 China Water Resources Bulletin. After investigations on 119 major lakes and 646 major reservoirs in China, there was eutrophication. The number of lakes and reservoirs was 83 and 271, respectively, and the eutrophication rate was as high as 69.8% and 41.95%. The eutrophication of water bodies is becoming more and more serious, which leads to frequent outbreaks of algal blooms, which poses a threat to people’s drinking water safety and seriously affects many human activities such as farmland irrigation, aquaculture, sightseeing and tourism [3].

In order to solve the uncertainty problem in the evaluation of eutrophication of lakes and reservoirs, the improved multi-dimensional normal cloud model is used to correct and improve the effectiveness of water eutrophication evaluation. Based on this, the case-based reasoning technique is applied to the lake. The decision-making problem of cyanobacteria blooms emergency management is researched and the scientific and effective decision-making of cyanobacteria bloom management is improved. First of all, the existing research is fully investigated, including eutrophication evaluation methods, governance decision-making techniques and cyanobacteria bloom management; then, a multi-dimensional normal cloud water eutrophication evaluation model and case-based reasoning cyanobacteria water China governance decision model are established. The analytic hierarchy process and CRITIC method are used to optimize the parameters of the cloud model, and the results of water eutrophication evaluation which effectively describe randomness and ambiguity are obtained. As one of the main indicators of cyanobacterial bloom management decision-making, the evaluation results are based on the complex network association characteristics of cyanobacterial bloom management, and the decision-making indicator group is established. By using the general ontology model of cyanobacteria bloom management decision-making, case representation is used, and rule reasoning and intuition are utilized. The fuzzy rough set method is used for retrieval, and the inferred case is applied to the cyanobacteria bloom management. Finally, the eutrophication evaluation method and the cyanobacteria bloom control decision-making method are embedded in the remote monitoring of water quality and the cyanobacteria bloom control decision-making system to realize the decision-making of cyanobacteria bloom control in the lake water body.

The research in this paper has certain innovations and is divided into three parts: The first part introduces the research on the evaluation of water eutrophication and water pollution. The second part puts forward the scientific decision-making evaluation algorithm of marine environment management based on cloud computing mode, and designs the water quality remote monitoring and water bloom governance decision-making system based on cloud computing mode. In the third part, the effectiveness of the algorithm is verified by experiments. The evaluation of eutrophication of water bodies and the decision-making of cyanobacterial blooms are complex system problems.

2. Related work

The evaluation of water eutrophication involves the relevant theoretical content of information science and environmental science, and plays an important role in the research of natural environment representation and language quantitative evaluation, and carries the task of water environment capacity calculation and water resources system planning. Many scholars have studied this aspect. Poslad S believed that the evaluation of water eutrophication should first determine the water quality indicators that affect the nutritional status of the water body, and extract key indicators that have a significant impact on water quality in this series of indicators, by analyzing the possible interactions between indicators and correlation, and the degree to which a single indicator deviates from the natural level, gives the level of nutrient status of the water body [4].

Kiessling T proposed a water assessment method for water pollution and first proposed a biological assessment classification method for water assessment [5]. According to Mcdonald G, the biological assessment classification method mainly analyzed the negative impacts on the environment after the river is polluted, using the degree of environmental damage to assess the level of eutrophication of the water. At the same time as the biological evaluation classification method, there is also an expert evaluation method. The purpose of the expert evaluation method is to solve the qualitative problem in the water environment assessment, and to transform the qualitative problem into a quantitative solution problem based on the mathematical theory basis, through the field of organizational environmental science or scholars and experts in related fields use scoring methods and data to analyze the eutrophication level of a given water body. Early research has a large advantage due to lack of sufficient water quality data [6]. The characterization method proposed by Levin N lays the foundation for the eutrophication evaluation methods widely used in countries around the world [7]. After that, Yang Y proposed the parameter method and proposed the nutritional status index method, which used the numerical value of single factor to measure the quality of water quality [8].

In recent years, many scholars of China and other countries have carried out research on marine ecological environment management. Reckhow KH et al. pointed out that uncertainty in environmental decision-making should not be considered as one of the most easily overlooked problems. For the uncertainty of the marine environment, intelligent algorithms can be used to solve the problem. Through algorithm training and learning, the changing laws of the marine environment are further mastered [9]. Ulibarri et al. believed that cloud technology could be applied to the evaluation and management of the marine environment, combining cloud computing and analytic hierarchy process to construct a marine environmental assessment model, which lays the foundation for the application of cloud computing technology in environmental assessment [10]. Grant DB et al. proposed that the multilateral nature and uncertainty of the marine environment have brought many obstacles to the assessment of the marine environment, and the search for changes in the marine environment is essential for the management of the environment, which can be used by intelligent algorithms [11]. Romero Gelvez JI et al. used a mixed multi-criteria group decision model (MCDM) combined with social network analysis (SNA), analytic hierarchy process (AHP) and similarity measures to solve the consensus and anchoring problems among environmental decision makers. Environmental decision-making problems are often divisive, and even in the technical field, decision-makers with strong personality influence the outcome [12]. Haddaway NR and others introduced cloud computing technology into the marine environment evaluation. Through research, it believes that cloud computing technology can solve the problem of variability of the marine environment to a certain extent, so as to better evaluate the marine environment, but in the current technology, it is not mature and needs further optimization [13]. Li F et al. proposed a comprehensive risk management calculation model, which proposed a graded remediation value for water and soil environment management from the aspects of health risks, local background values, land remediation cases, existing remediation technologies and financial costs. Purification values of chromium (VI), chromium, arsenic, lead and cadmium in the primary control layer should be 7.5, 1000, 30, 250 and 1.4 mg/kg [14]. Taking the Hawaiian waters as an example, Leslie M et al. used cloud computing technology to study the scientific decision-making methods of marine environmental management. According to the research results, the center can be used as a supplement to the capacity of federal agencies to undertake environmental management responsibilities, including the model of the responsibility distribution mechanism of the marine environment [15].

In short, scholars have been rich in the evaluation and management of the marine environment. In recent years, many scholars have introduced cloud computing models into the scientific decision-making of marine environmental management, and proposed management models such as hierarchical remediation mechanism and responsibility distribution mechanism [16, 17]. The use of calculations in this area is not yet mature, and the results of environmental assessment and the scientific nature of decision-making have yet to be improved. This has been further studied.

3. Scientific decision evaluation algorithm for marine environment management based on cloud computing model

3.1 Water eutrophication evaluation method based on improved cloud model

Although many scholars have made a lot of research work on the evaluation of eutrophication of water bodies, there are complex interactions between plants, animals, microorganisms and trace elements in water environment. At present, the mechanism of eutrophication of water bodies has not been determined. The mechanism model of algal blooms is still under investigation. Due to the disorderly development and construction of coastal areas, climate change, river cutoff, land-based pollutant input, underground salt-water intrusion and increasing marine disasters, the coastal habitat degradation and change in the Yellow Sea and Bohai Seas in China has become one in Bohai. Important environmental problems, such as coastal erosion and natural wetland encroachment, such as habitat degradation, shoreline retreat, seawater intrusion, coastal low-lying inundation, and secondary soil salinization are easily observed in the Bohai Sea [18, 19].

The multidimensional normal cloud model is based on probability theory and fuzzy sets, and the cloud model includes multiple forms [20]. The multi-dimensional normal cloud model represents a kind of high-dimensional and normal cloud, which is a model for describing the relationship between ambiguity and randomness, which can realize the uncertainty transformation of qualitative concepts and quantitative concepts. The problem is universally applicable.

The multi-dimensional normal cloud model first needs to define the domain U of the cloud model, and use CT to represent the mapping of U to the closed interval [0, 1], as shown in formula (1), where the fuzzy subset on the domain U is represented by T. $CT (x) : U \to [0, 1]$ (1)

Let x be a random number that follows a certain law, where x = x₀, CT (x)] are not constant. The cloud model of T (abbreviated as cloud) is expressed as the membership cloud of T, that is, the distribution of CT (x) in the domain U, as shown in Equation (2). $\forall x \in U, x \to CT (x)$ (2)

The water eutrophication evaluation language is inaccurate or even incomplete, which is defined as a fuzzy subset, using the uncertainty conversion model of linguistic values to realize the conversion between qualitative concepts and quantitative concepts. ∀x ∈ U, CT(x) is represented by a large number of cloud droplets, rather than a curve representing fuzzy membership. The normal cloud model refers to a large number of cloud droplets exhibiting a normal distribution law, and the domain U can be extended from one dimension. To multidimensional, such a cloud model is called a multidimensional normal cloud model.

The randomness in the cloud model is reflected in the random generation of probability. The degree of certainty is the concept of fuzzy membership in fuzzy theory. The result of combining randomness and fuzziness is the degree of certainty, indicating the random probability distribution in the fuzzy sense, which is also fuzzy. Intrinsic correlation with randomness, there are a large number of clouds generated by actual meaning in cloud droplets. The larger the number of cloud droplets, the more reflective the meaning of data distribution. The greater the degree of certainty of cloud droplets, the more it reflects the contribution of the cloud droplets.

3.2 General orient model design algorithm for decision-making of marine environment and water management

During the eutrophication process of water bodies and the cyanobacterial blooms, the interaction between water nutrients, water bodies and the external environment, and the external environment will be involved. All kinds of substances in nature will be organically linked through interaction. The linkages are either tight or loose, and the strength of the interaction between them is different. In view of the complex internal relationship of the water environment, the mechanism of the blooms is still unclear. Therefore, a model for constructing the dynamic correlation characteristics of complex networks is proposed to externally describe the blooms.

In the decision-making of cyanobacterial blooms, due to the different nature of the indicators, the scope of the indicators is divided into the total network and sub-networks. The total network is used to describe the comprehensive attributes of the governance cases, and the sub-network is the subdivision of the attributes in the total network. The total network diagram and sub-network diagram of water bloom management are shown in Fig. 1.

Fig. 1

Total network representation of water bloom decision making.

3.3 Water quality remote monitoring and water bloom management decision-making system design based on cloud computing model

The remote monitoring of water quality and the decision-making system of cyanobacteria bloom management are designed on the basis of detailed understanding of the requirements. The prerequisites for system development are not only to meet business needs and user needs, but also to design principles that meet functional and non-functional requirements. According to the needs of relevant departments for water environmental pollution control, the system design should follow several aspects:

Business needs: The development of water quality remote monitoring and water bloom management decision-making system is designed to assist the environmental protection department to monitor the changing trend of water quality and assist field experts to timely and effectively deal with water environmental issues. User requirements: The main purpose of water quality remote monitoring and water bloom management decision-making system is to realize real-time water quality data acquisition, statistical analysis, eutrophication evaluation, and water bloom management decision-making. Functional requirements: Realize 3G network transmission to read water quality sensor data; realize water quality data display, historical data query, water quality trend analysis, report printing and other functions. Non-functional requirements: The system should meet the requirements of stable performance, information security, economy and practicality, and adapt to users. Based on the above requirements, the module system of water quality remote monitoring and water bloom management decision system is shown in Fig. 2. The lower water quality data collection adopts YSI6600 water quality sensor, which uses the data transmission module of the lower main controller to transmit through 3G. The remote monitoring of water quality and the cyanobacteria bloom management decision system serve as the upper receiving system to assist in post-processing and decision-making.

Fig. 2

Modular structure system of the system.

Based on the design of the modular structure system, the remote monitoring of water quality and the cyanobacteria bloom control decision system encapsulate the subsystems to form the functional module diagram of Fig. 3, thereby achieving easy maintenance, improved efficiency, and convenient upgrade.

Fig. 3

Function module diagram of system.

4. Experimental design and analysis

4.1 Data processing

The data processing function module includes main functions such as “real-time data display”, “historical data query”, and “data analysis”. Real-Time Data Display displays the current site’s real-time water quality data and site status by moving the mouse cursor to the monitoring site location.

When using the “Historical Data Query” function, you should first check the query date. After confirming that the information is correct, click OK. The “Data Analysis” function can perform regional comparison and trend analysis on specific water quality indicators in the selected time period, and realize multiple functions such as “single point inquiry” and “multi-point inquiry”. Click “Print” to connect to the printer for printing.

4.2 Algorithm implementation

The improved eutrophication evaluation method proposed in this paper is embedded in the system to realize the eutrophication evaluation of the lake water body. Click the “Water Quality Evaluation” function button to adjust the water quality evaluation page. After selecting the evaluation site and reading the data according to the requirements, the water body eutrophication evaluation can be performed.

The case-based reasoning strategy proposed in this paper realizes the decision of cyanobacteria bloom management. The algorithm is embedded in the system after encapsulation. Before the decision is started, the water location that needs to be analyzed is selected first. The system performs calculations internally. Click “Query” to retrieve similar cases from the database. The governance situation, the final result of the decision gives the ordering of similar cases, and provides a reference for governance decision-making.

The water bloom management decision-making plan of the query case library shows that the West Lake water bloom treatment adopts the mechanical algae removal method in the physical method. The algae-containing water can be pumped into the filter through the electromechanical equipment, and then the separation equipment is used to separate the algae water and complete the collection of algae plants. The method can greatly improve the purification ability of the water body in a short period of time, effectively reduce the algae content in the water body, thereby achieving the purpose of controlling the water bloom, and the method can be used as a reference case for the treatment of Kunming Lake in this paper. Finally, if the governance of Kunming Lake adopts the reasoning results and is strictly implemented, the governance process and effects of Kunming Lake will be evaluated and recorded, and saved in the case library, thus supplementing the integrity and coverage of the case library and improving the case base, which save the richness of the case.

4.3 Analysis of experimental results

According to Table 1, the evaluation degree and the evaluation level are obtained. For the clear and clear relationship between the evaluation results, the bubble chart of the water sample evaluation result is drawn, as shown in Fig. 4. The black dot indicates the name of the water body, the position of the black dot indicates the eutrophication level of the water quality and the degree of eutrophication, the water quality within the dotted line indicates the acceptable water quality of the natural environment, and the samples other than the dotted line are treated according to the direction of the arrow. The water quality in the dotted line is reached.

Fig. 4

Bubble Diagram of water quality evaluation results.

Table 1

Determination and evaluation results of typical lakes in China

Evaluation grade	I	II	III	IV	V	VI	Evaluation grade
Qionghai Lake	0.8698	0.8744	0.8943	0.9275	0.8941	0.5604	IV
Erhai	0.9955	0.9968	0.9995	0.9971	0.8832	0.4244	III
Bosten Lake	0.9786	0.9812	0.9894	0.9983	0.8733	0.4746	IV
Yuqiao Reservoir	0.4741	0.4776	0.4842	0.4917	0.3873	0.2925	IV
Magnetic Lake	0.9223	0.9279	0.9448	0.9737	0.9492	0.5958	IV
Chaohu Lake	0.8361	0.8429	0.8668	0.9109	0.956	0.6674	V
Gan Tang Lake	0.5164	0.5261	0.5493	0.6017	0.8671	0.8936	VI
Lake Agassiz	0.2242	0.2305	0.2502	0.2962	0.5844	0.9393	VI
Hangzhou West Lake	0.3917	0.4003	0.4202	0.4665	0.7417	0.8635	VI
Nanjing Xuanwu Lake	0.0005	0.0005	0.0006	0.0009	0.0055	0.042	VI
Ink Lake Wuhan	0.0003	0.0004	0.0004	0.0006	0.0039	0.042	VI
Dongshan Lake, Guangzhou	0.0013	0.0013	0.0016	0.0023	0.0125	0.0787	VI

The evaluation results of the improved multi-dimensional normal cloud model are compared with the existing methods. The comparison is shown in Table 2.

Table 2

Comparison of the results of eutrophication evaluation methods

Lake storehouse	Single factor	Comprehensive evaluation	Multidimensional normal cloud	AHP-CRITIC
Evaluation method	evaluation method	method	model evaluation method
Qionghai Lake	V	IV	IV	IV
Erhai	IV	III	III	III
Bosten Lake	IV	IV	IV	IV
Yuqiao Reservoir	V	IV	IV	IV
Magnetic Lake	V	V	V	IV
Chaohu Lake	VI	V	V	V
Gan Tang Lake	VI	V	VI	VI
Lake Agassiz	VI	VI	VI	VI
Hangzhou West Lake	VI	VI	VI	VI
Nanjing Xuanwu Lake	VI	VI	VI	VI
Ink Lake Wuhan	VI	VI	VI	VI
Dongshan Lake, Guangzhou	VI	VI	VI	VI

By comparing the above methods, it can be seen that the AHP-CRITIC multi-dimensional normal cloud model method is roughly the same as the other methods, indicating its applicability in the evaluation of water eutrophication. The AHP-CRITIC multi-dimensional normal cloud model method can weigh the influence of all water quality indicators on the final water state, effectively avoiding the evaluation results in the single factor evaluation method only depends on the bias of the eutrophication level of the worst factor, so that the final result is more Comprehensive and complete.

The case of Kunming Lake water bloom in the case analysis is a real case in 2005, and the data source is also extracted from the case database. The results recorded in the library indicate that the Kunhua Lake water purification treatment actually uses mechanical algae removal in physical methods. According to the analysis of the blooms and treatment requirements issued by the environmental protection department, the water blooms in Kunming Lake are most similar to the water blooms in the West Lake. Using the reasoning method of this paper, the case of Xihu Waterhua can be accurately retrieved, in line with the pre-set results.

The matching results of Kunming Lake in the simulation of the example are shown in Fig. 5. According to the analysis, in the matching case, the treatment methods of lakes similar to those in Kunming Lake are mostly mechanical algae removal, indicating the mechanical algae removal method. It is the first choice, and other methods that can be used are artificial aeration, source nutrient salt biological control technology, and so on. Referring to the existing method of water bloom management, the rankings of water bloom governance decisions obtained by case-based reasoning, vague set multi-objective decision making, fuzzy-Bayesian and multi-attribute decision making are shown in the figure, and several methods can be seen. The optimal choices are mechanical algae removal methods. In addition, the ranking results of other water bloom management methods are basically consistent, indicating the practical effectiveness of the modeling method.

Fig. 5

Kunming Lake case matching results.

Fig. 6

Comparison of sorting results.

The results obtained by the method of this paper are consulted by experts to further verify the accuracy of the method. Compared with the existing research methods, the water bloom governance case-based reasoning method based on the dynamic association characteristics of complex networks has the following advantages: the reasoning process is based on the basic experience of real governance cases, and the governance effect is also recorded in the library. After the implementation of the governance measures, The emergent situation or the consequences of governance may have certain predictability; the governance method is operability, in addition to the reference governance method, it can further refer to the matching case in terms of the management method operation flow, reagents and material dosage; In the case of outbreaks, some data is difficult to obtain. The concept of contribution in this paper can effectively solve the problem of attribute loss.

In summary, based on the cloud computing model to analyze the scientific decision support system for marine environmental management in the Yellow Sea and Bohai Sea, and to draw on the theoretical framework of the scientific decision support system for marine environmental management in marine developed countries, a adaptation must be established to the Yellow Sea under the guiding ideology and principles of science. Bohai’s own set of scientific decision support system is for marine environmental management. On the basis of making full use of the ecological services of the Yellow Sea and Bohai Sea, the overall capacity of the Yellow Sea and Bohai Sea pollution and ecological environment monitoring and surveillance, marine disaster warning and emergency response, information systems and technical support will be built to effectively serve offshore resources and the environment. Land maintenance and management, comprehensive development and utilization of marine resources, unified management and protection of the Yellow Sea and Bohai Sea, and sustainable development of the marine economy.

5. Conclusion

The research status of water eutrophication evaluation and water bloom governance decision-making are first summarized. Secondly, the multi-dimensional normal cloud water eutrophication evaluation model is established. Then, the application of case-based reasoning strategy in cyanobacteria bloom management decision-making is studied. Finally, the design and implementation of water quality remote monitoring and water bloom governance decision system. The application of multi-dimensional normal cloud model in the evaluation of water eutrophication is studied. According to the fuzziness and uncertainty of evaluation language, the concept of digital feature expectation, entropy and super-entropy of cloud model is used to describe the weight of index. The subjective weighting method AHP and the objective weighting method CRITIC are used for comprehensive measurement, and the cloud model is optimized to achieve the eutrophication evaluation. Exploring the application of case-based reasoning strategy in the decision-making of cyanobacteria bloom management, constructing a general ontology model of cyanobacteria bloom management decision-making, and on the basis of this, collecting and sorting the case base and defining the concept of contribution degree to realize the case retrieval process, the case-based reasoning model optimizes the cyanobacteria bloom management method and solves the bottleneck problem in the actual operation process of the cyanobacteria bloom management decision. The assessment of eutrophication of water bodies and the decision-making of cyanobacterial blooms are complex system problems. Due to limited time and energy, the research content and methods need to be improved.

Footnotes

Acknowledgements

The present study was supported by phased project achievements of the Key Research Center for Philosophy and Social Sciences in Zhejiang Province (Research Center for Ocean Culture and Economics in Zhejiang Province): “Research on the Regional Marine Environment Governance of the Yangtze River Delta under the Integration Strategy - Initiated from Ningbo” (Grant No.16JDGH043).

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