Water Quality Evaluation Model Research Articles

Construction and application of a composite model for acid mine drainage quality evaluation based on analytic hierarchy process, factor analysis and fuzzy comprehensive evaluation: Guizhou Province, China, as a case.

The process of mining activities often causes the formation of acid mine drainage (AMD). Through rock fractures and underground rivers, AMD can easily enter the groundwater environment near mines and cause serious pollution to water quality. In order to effectively evaluate the quality of polluted mine water and to understand its threat to the ecosystem around the mine. In this study, four AMD pollution distribution areas, Guiyang City, Bijie City, Qianxinan Prefecture, and Qiandongnan Prefecture in Guizhou Province, were used as the study area. A composite model for mine water quality evaluation was constructed using factor analysis (FA), analytic hierarchy process (AHP), and fuzzy comprehensive evaluation (FCE). Furthermore, by introducing the weighted average method and the level characteristic value (J), the water quality type and the water body environmental quality were evaluated comprehensively, respectively. Compared with the traditional evaluation model, the AHP-FA-FCE model has obvious advantages in the selection of evaluation indicators, the determination of indicator weights, and the comprehensive evaluation of water quality types, and the evaluation results obtained are more reasonable and accurate. Three common factors mainly controlled by mineral oxidation factor, human activity factor, and mineral dissolution factor were extracted by dimension reduction of the original hydrochemical data by FA. The water quality of the mine water samples was evaluated using SO4 2- , Fe, Al, Mn, Na, and F- as evaluation indicators, and the results showed that the mine water samples in the study area as a whole were dominated by class V water, which accounted for 77.78% of the total. Based on the statistical analysis of the original data, it was found that influenced by the water-rock interactions in the study area and the AMD pollution components, the hydrochemical type of the mine water is mainly SO4 2- -Ca-Mg type. The water body environmental quality of mine water in four areas, Guiyang City, Qianxinan Prefecture, Bijie City, and Qiandongnan Prefecture, is from excellent to poor. The average level characteristic value of all the areas is more than 3, and the overall environmental quality of the water body is poor. The strong water-rock interaction and mining activities in the study area may be the main cause of AMD pollution. The results of this study may provide some theoretical reference for the water quality evaluation of AMD-polluted areas. PRACTITIONER POINTS: A composite model for mine water quality evaluation was constructed. A factor analysis-based evaluation indicator selection method is proposed. This study improved the weighting process of the traditional fuzzy comprehensive evaluation. A water quality discriminant based on the weighted average method is proposed. The water environmental quality of various types of mine water was evaluated.

Evaluation of mine water quality based on the PCA–PSO–BP model

Abstract To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry.

Study on Parameter Inversion Model Construction and Evaluation Method of UAV Hyperspectral Urban Inland Water Pollution Dynamic Monitoring

The problem of environmental water pollution is becoming increasingly important. Inland rivers and lakes form interconnected water networks with fragile water ecosystems, and urban water pollution problems occur frequently. Chemical oxygen demand (COD), dissolved oxygen (DO), total phosphorus (TP), total nitrogen (TN), and ammonia nitrogen (NH3-N) in inland rivers are important indicators to evaluate water health quality. Timely and accurate reflection of dynamic changes to the key indices of urban river health status are of vital practical significance to adjust water treatment policy and ensure the stability of the aquatic environment and people’s health. This study used COD, DO, TP, TN and NH3-N as typical water quality parameters for a reservoir in Guangxi Province, China and established a set of standardized processes covering UAV hyperspectral sampling and ground spectral correction, spectral data preprocessing, and modeling. In combination with machine learning and statistical analysis, an inversion method for measuring urban inland water pollution from UAV hyperspectral imaging with different dynamic monitoring parameters was proposed. And we compared the different combinations of preprocessing algorithm-regression algorithm and dimensionality reduction algorithm to get a unified model for quantitative estimation of water quality parameter concentration. We evaluated the performance of the proposed model according to root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The experimental results showed that our model was superior to other algorithms in RMSE, MAE, MAPE, and R2. The MAPE of this model ranged from 0.01 to 0.12 and R2 ranged from 0.84 to 0.98 in all water quality parameters. In general, this study provides an effective tool for decision-makers to investigate the source and physical mechanism of water pollution and establish a graded water quality evaluation model.

基于 IoT 的便携式养殖水质监测系统的开发与评估

In this paper, the development of a portable, multifunctional water quality monitoring system for aquaculture that is based on IoT technology is presented. The system integrates a main control module, sensor module, Human Machine Interface (HMI) module, Wi-Fi module and power module, and is equipped with system software based on Real Time Operating System (RTOS) for scheduling tasks. The main control module collects crucial water quality information, including water temperature (WT), dissolved oxygen (DO), ammonia nitrogen (NH3-N), and pH, through the sensor module and facilitates data interaction with the HMI module. The proposed aquaculture water quality evaluation model utilizes water quality parameters as input to assign a grade based on the evaluation result. These parameters are transmitted wirelessly to the OneNet cloud platform using Wi-Fi modules, enabling users to remotely monitor the water quality through a visual interface. The system structure has been meticulously designed to accommodate both portable and fixed-point remote monitoring applications. The experimental results demonstrate that the system is accurate, stable, and cost-effective, providing a reliable and efficient solution for intelligent aquaculture in small and medium-sized enterprises.

Application of Improved Particle Swarm Optimization SVM in Water Quality Evaluation of Ming Cui Lake

Water quality directly determines our living environment. In order to establish a more scientific and reasonable water quality evaluation model, it needs a lot of data support, but it will lead to a large increase in the calculation time of the evaluation model. This paper proposes an improved particle swarm optimization SVM model (CPOS-SVM) to solve this problem. In this paper, the Pareto optimal solution concept is used to sparsely process the training set, which can ensure that the number of training sets is reduced without loss of data characteristics, thus reducing the training time. In order to solve the problem of the kernel parameter g and penalty factor c on the SVM algorithm, which affects the accuracy of the SVM model but it is difficult to select why, a particle swarm optimization algorithm is used in this paper to optimize the kernel parameter and penalty factor and improve the accuracy of the model. In this paper, 480 sets of data from Ming Cui Lake from 2014 to 2022 are taken as the research object, and examples are analyzed in MATLAB 2020a. The results show that the training time of the CPOS-SVM model can be completed within 2 s and does not increase with the increase of data volume. Meanwhile, by comparing the SVM model, POS-SVM model, and POS-BP model, training time increases dramatically with the amount of data. The accuracy of the POS-SVM model is the highest, and the accuracy of the CPOS-SVM model is basically consistent with that of the POS-SVM, reaching 94%, while the accuracy of the SVM model and the POS-BP model are slightly worse. This indicates that the CPOS-SVM model has good application value in water quality evaluation.

Water quality pollution assessment and source apportionment of lake wetlands: A case study of Xianghai Lake in the Northeast China Plain

Surface water pollution has always posed a serious challenge to water quality management. Improving water quality management requires figuring out how to comprehend water quality conditions scientifically and effectively as well as quantitatively identify regional pollution sources. In this study, Xianghai Lake, a typical lake-type wetland on the Northeast China Plain, was taken as the research area. Based on a geographic information system (GIS) method and 11 water quality parameters, the single-factor evaluation and comprehensive water quality index (WQI) methods were used to comprehensively evaluate the water quality of the lake-type wetland in the level period. Four key water quality parameters were determined by the principal component analysis (PCA) method, and more convenient comprehensive water quality evaluation models, the minimum WQI considering weights (WQImin-w) and the minimum WQI without considering weights (WQImin-nw) were established. The multiple statistical method and the absolute principal component score-multiple liner regression (APCS-MLR) model were combined to analyse the lake pollution sources based on the spatial changes in pollutants. The findings demonstrated that the WQImin-nw model’s water quality evaluation outcome was more accurate when weights were not taken into account. The WQImin-nw model can be used as a simple and convenient way to comprehend the variations in water quality in wetlands of lakes and reservoirs. It was concluded that the comprehensive water quality in the study area was at a “medium” level, and CODMn was the main limiting factor. Nonpoint source pollution (such as agricultural planting and livestock breeding) was the most important factor affecting the water quality of Xianghai Lake (with a comprehensive contribution rate of 31.65%). The comprehensive contribution rates of sediment endogenous and geological sources, phytoplankton and other plants, and water diversion and other hydrodynamic impacts accounted for 25.12%, 19.65%, and 23.58% of the total impact, respectively. This study can provide a scientific method for water quality assessment and management of lake wetlands, and an effective support for migration of migratory birds, habitat protection and grain production security.

Spatial-temporal Variation in Groundwater Quality and the Contamination Source Analysis in Shihezi-Changji Area

To analyze spatial-temporal variation in groundwater quality and contamination sources in the Shihezi-Changji area in Xinjiang, a Bayesian water quality evaluation model based on Shannon entropy, Spearman rank correlation coefficient, principal component analysis, and an absolute principal component scores-multiple linear regression receptor model (APCS-MLR) were comprehensively used in this study. Groundwater samples in 23 in-situ wells were collected from 2016 to 2021 for quality analysis. The results showed that ① groundwater quality was generally good, with most samples having a phreatic water quality of Class Ⅰ and Class Ⅱ and most confined groundwater quality being of Class Ⅰ. ② Temporally, 2016 and 2017 were the key time nodes of water quality variation in phreatic water and confined groundwater, respectively. Class Ⅳ and Class Ⅴ groundwater was observed only before the key time nodes, whereas after those time nodes the groundwater quality fluctuated within Class Ⅰ to Class Ⅲ. ③ Spatially, the order of phreatic water quality in descending order was Shihezi City, Hutubi county, Manas county, and Changji City, whereas that of confined groundwater quality was:Shihezi City and Changji City, Hutubi county, and Manas county. ④ The spatial-temporal variations in groundwater quality and that in major related indices were basically similar and highly heterogeneous. ⑤ Phreatic water quality was mainly affected by leaching (67.30%), leaching-migration (10.89%), and agricultural-domestic pollution (9.44%); by contrast, unconfined groundwater quality was mainly affected by leaching-enrichment (52.08%), agricultural-domestic pollution (16.06%), and ion exchange under an alkaline environment (12.64%). Although groundwater quality was improved over the years, more attention should be paid to groundwater salinization in the 149 Regiment in northern Manas County.

Water Quality Evaluation and Prediction Based on a Combined Model

Along with increasingly serious water pollution, water environmental problems have become major factors that hinder the sustainable development of our economy and society. Reliable evaluation of water quality and accurate prediction of water pollution indicators are the key links in water resource management and water pollution control. In this paper, the water quality data of Lanzhou Xincheng Bridge section in the Yellow River Basin and Sichuan Panzhihua Longdong section in the Yangtze River Basin were used to establish a water quality evaluation model and a prediction model. For the water quality evaluation model, we constructed the research samples by means of equal intervals and uniform distribution of interpolated water quality index data according to Environmental Quality Standards for Surface Water. The training samples were determined by a stratified sampling method, and the water quality evaluation model was established using a T-S fuzzy neural network. The experimental results show that the highest accuracy achieved by the evaluation model in water quality classification was 94.12%. With respect to the water quality prediction model, we propose ARIMA-WNN, which combines the autoregressive integrated moving average model (ARIMA) and a wavelet neural network (WNN) with the bat algorithm (BA) to determine the optimal weight of each individual model. The experimental results show that the highest prediction accuracy of ARIMA-WNN is 68.06% higher than that of the original model.

Wetland Ecotourism Development Using Deep Learning and Grey Clustering Algorithm from the Perspective of Sustainable Development.

The purpose is to promote the sustainable development of wetland ecotourism in China and plan the passenger flow in different tourism periods. This work selects Zhangye Heihe wetland ecotourism spot as the research object. Firstly, the two single wetland ecotourism Demand Prediction Models (DPMs) are proposed based on the time series of the optimized Fuzzy Clustering Algorithm (FCA), grey theory, and the Markov Chain Method. The proposed wetland ecotourism DPM simulates and predicts the ecotourism passenger flow of wetland-scenic spots and verifies the maximum passenger flow. Then, a hybrid model combining the above two single models is proposed, namely, the wetland ecotourism DPM based on an optimized fuzzy grey clustering algorithm. Further, the proposed three models predict the passenger flow in wetland ecotourism spots from 2015 to 2019. A wetland Water Quality Evaluation (WQE) model based on Deep Learning Backpropagation Neural Network (Deep Learning (DL) BPNN) is proposed to evaluate the water quality in different water periods. The results show that the hybrid model's Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are 1.25% and 0.2532. By comparison, for two single models, the MAPE is 11.67% and 1.45%, respectively, and the RMSE is 0.2526 and 0.1652, respectively. Therefore, the mixed hybrid has the highest accuracy and stability. The water quality of the scenic spot in the wet season is obviously better than that in the dry season and flat season. It is suggested that the natural environmental factors, such as water quality and passenger flow in different periods, should be considered when formulating ecotourism development strategies.

Water quality assessment and the influence of landscape metrics at multiple scales in Poyang Lake basin

Stream water quality has been increasingly deteriorated in recent decades because of anthropogenic activities. Assessing water quality via key environmental parameters and quantifying the respective influence of landscape metrics like landscape configuration, land use composition and topography, which can overall represent anthropogenic activities, can facilitate the development of improved water quality management strategies. However, few studies concentrate on the quantitative effect of these landscape metrics groups on the change of key water quality parameters. In this study, with Poyang Lake basin, we selected key factors of the 14 environmental variables for the cost-effective water quality evaluation model of the minimum water quality index (WQImin) by the random forest method and quantified the contribution of different groups of landscape metrics on key water quality parameters by redundancy analysis and variance partitioning analysis. The results indicated that six water quality parameters of ammonium (NH4+-N), total nitrogen (TN), permanganate index (CODMn), dissolved oxygen (DO), fecal coliforms (F.coli) and total phosphorus (TP) can significantly represent the overall water quality of the Poyang Lake basin. Based on WQImin, the water quality of Fuhe River and Xiushui River was significantly better than that of Ganjiang River, Raohe River, and Xinjiang River in Poyang Lake basin. The contribution of landscape metrics to water quality variation followed the order of landscape configuration (19.0–22.5%) > land use composition (5.2–20.2%) > topography (5.7–8.0%). Here, Build-up land was the environmental factor contributing most significantly to the variation in water quality at both spatial and seasonal scales (5.7–21%). Build-up land, Agricultural land, Contagion index and Cohesion index were positively correlated with TN, TP, CODMn, and NH4+-N and negatively with DO at different scales; Grassland, Forest land, Hypsometric Integral index, Slope, and Mean Shape index showed the opposite relationship. The influence of these landscape types changed with spatial and seasonal scales, more significantly explaining variations in water quality during the dry season and at buffer scale. Changes in water quality at the spatial scale were most sensitive to land use composition. Our results represent a basis for the improvement of water quality management.

Application of PCA-RSR Model in Reservoir Water Quality Evaluation

Water quality evaluation is a critical component of water environmental quality management, and conducting water quality assessments for reservoirs is quite practical. The inaccuracy induced by information overlap of several water quality measures is rarely taken into account in current water quality assessment systems. To solve this problem, the Principal component analysis-Rank sum ratio (PCA-RSR) water quality evaluation model was used to quantitatively evaluate the water quality of the Daheiting reservoir based on the monitoring data of different water layers in 2019. The results show that the water quality of Daheiting reservoir in 2019 is slightly better than that of the end of the reservoir, due to the influence of human factors downstream of the Upper Panjiakou Dam and the topography of the Luan River System, and the water quality from the dam head to the reservoir tail shows a decreasing trend. The PCA-RSR model has a good correlation with the traditional water quality indexes (WQI) system, which can avoid errors caused by overlapping information among the indexes while also taking into account the weight of the environmental factors of the study area. It is feasible and has some practical value in reservoir water quality evaluation..

Groundwater Quality Evaluation Based on PCA-PSO-SVM Machine Learning in Xinzhou City, China

The scientific evaluation of water quality change trends and pollution characteristics is of great significance to improving the current situation of water resources. The particle swarm optimization support vector machine based on principal component analysis (PCA-PSO-SVM) was used to conduct a comprehensive evaluation of groundwater quality in Xinzhou city, and the results were compared with those of a variety of traditional water quality evaluation methods. The evaluation results show that the water quality evaluation model based on PCA-PSO-SVM is more comprehensive and objective. The overall groundwater quality situation in Xinzhou city is generally good. The upper pore water quality is mainly class II, and the lower karst water is mainly class III. The water quality shows significant spatial differences. Compared with the traditional water quality evaluation method, the improved SVM algorithm compensates for the defects of the traditional method. The model structure is stable, and the accuracy and calculation efficiency are high, which makes the method worthy of promotion and application. The research results can provide a scientific basis and reference value for the water quality evaluation of groundwater-related projects.

Bayesian Water Quality Evaluation Model Based on Generalized Triangular Fuzzy Number and its Application

Bayesian Water Quality Evaluation Model Based on Generalized Triangular Fuzzy Number and its Application

Research on application of ReliefF and improved RVM in water quality grade evaluation.

ReliefF algorithm was used to analyze the weight of each water quality evaluation factor, and then based on the Relevance Vector Machine (RVM), Particle Swarm Optimization (PSO) was used to optimize the kernel width factor and hyperparameters of RVM to build a water quality evaluation model, and the experimental results of RVM, PSO-RVM, ReliefF-RVM and PSO-ReliefF-RVM were compared. The results show that ReliefF algorithm, combined with threshold value, selects 5 evaluation factors with significant weight from eight evaluation factors, which reduces the amount of data used in the model, CSI index is used to calculate the separability of each evaluation factor combination. The results show that the overall separability of the combination is best when the evaluation factor with significant weight is reserved. When different water quality evaluation factors were included, the evaluation accuracy of PSO-ReliefF-RVM model reached 95.74%, 14.23% higher than that of RVM model, which verified the effectiveness of PSO algorithm and ReliefF algorithm, and had a higher guiding significance for the study of water quality grade evaluation. It has good practical application value.

广东省水质现状及驱动因素

PDF HTML阅读 XML下载 导出引用 引用提醒 广东省水质现状及驱动因素 DOI: 10.5846/stxb202101120115 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 广东省科技计划项目（2018B030320002，2019A050506001）；广东省自然科学基金项目（2018B030311059，2021A1515012579） Analysis of water quality status and driving factors in Guangdong Province Author: Affiliation: Fund Project: Science & Technology Plan Project of Guangdong Province of China (2018B030320002, 2019A050506001);Natural Science Fund grant (2018B030311059, 2021A1515012579) of Guangdong Province, China 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:水质现状评估及其驱动因素分析是实现水生态保护、水资源利用和水污染治理的关键，对于水生态系统的可持续发展具有重要意义。以广东省七大流域为研究区，基于2019-2020年间的溶解氧（DO）、透明度（SDD）、悬浮物（SPM）、叶绿素a （Chla）、氨氮（NH3N）、总氮（TN）、总磷（TP）7个指标的水质监测数据，综合运用单因子指数法（SI）和综合水质指数（WQI）评价方法，分丰水期（N=66）和枯水期（N=54）评估研究区的水质现状，并探讨水质参数与地形、气象、社会经济和土地覆被类型等驱动因素之间的相关关系。SI评估结果显示广东七大流域主要以工业污水、农业面源等造成的Chla和TN浓度超标、部分水体富营养化严重为主，同时伴有溶解氧浓度偏低的问题；WQI评估结果显示研究区有57%以上的采样点属于中等以下水质。Chla、SPM、NH3N和TP浓度具有显著的季节和驱动因素差异：丰水期的Chla和TP浓度低于枯水期，但SPM和NH3N浓度高于枯水期。枯水期DO、TN和WQI的显著性影响因子为丰水期的1/3左右；这种季节差异可能是流域内降雨、营养盐负荷和土地覆被类型导致的复杂地表径流及面源污染所致。珠江三角洲河网区、粤西诸河、韩江下游以及粤东诸河练江流域的水质问题突出。未来水生态系统的可持续发展研究可以借助长时间序列、多频次、高分辨率的遥感监测手段和多种数值模拟方法以及常规水质评估模型，探讨气候变化、河岸带产业结构和流域土地利用方式对面源污染的影响，以进一步厘清降雨强度、三产结构和土地利用方式转变对区域水质变化的影响。 Abstract:Water quality evaluation and driving forces analysis, two key factors to achieve ecological protection, resource utilization, and pollution control of water, are pivotal to the sustainable development of aquatic ecosystem. We utilized the in-situ experiment data of seven water quality parameters-dissolved oxygen (DO), transparency (Secchi disk depth, SDD), suspended particulate matters (SPM), chlorophyll a (Chla), ammonia nitrogen (NH3N), total nitrogen (TN), and total phosphorus (TP)-which were collected from seven major basins in Guangdong Province during 2019-2020. This paper evaluates the water quality status of Guangdong Province in the high-flow season (HFS, N=66) and low-flow season (LFS, N=54) using the single factor index method (SI) and the comprehensive water quality index (WQI). The correlation between water quality parameters and driving factors, such as topography, meteorology, socio-economic and land cover types, were analyzed using Pearson correlation coefficient. The SI-based evaluation results show that the seven basins in Guangdong province are mainly faced with problems such as exceeding standard of Chla and TN concentration, and low DO concentration, which were caused by industrial sewage and agricultural non-point sources. The WQI evaluation results show that 57% of the sampling points in the study area are below the moderate level. The concentrations of Chla, SPM, NH3N and TP have significant differences in water period and driving factors:the concentrations of Chla and TP in HFS are lower than that in LFS, but the concentrations of SPM and NH3N are opposite in the two periods. The number of significant factors affecting the evaluation results of DO, TN, and WQI in LFS is about 1/3 of those in HFS. The seasonal difference is caused by the changes of complicated surface runoff and non-point source pollution, such as precipitation, nutrient load and land cover types in the basins. Water quality problems are prominent in the river network area of the Pearl River Delta, the lower reaches of the Hanjiang River, the rivers in western Guangdong, and the Lianjiang Basin of the eastern Guangdong. The water quality problems in the Pearl River Delta and the West River are mainly exceeded concentrations of TN and SPM. The main water quality problem of North River and East River is high TN concentration. The rivers in western Guangdong have insufficient DO, low SDD, and high concentrations of Chla and NH3N. The conditions of TN and DO for some sampling points in the rivers of eastern Guangdong and the Hanjiang River are not optimistic. In the future, the integrated methods combining remote sensing monitoring, numerical simulation, and regular water quality evaluation models should be enhanced to explore the effects of non-point source pollution from climate change, riparian industrial structure, and adjustment of watershed land cover patterns. These integrated methods can help to further clarify the water quality impact of changes in rainfall intensity, production structure, and land cover patterns, which greatly promote the sustainable development of aquatic ecosystems. 参考文献 相似文献 引证文献

Water Quality Evaluation Model Based on Principal Component and Particle Swarm Optimization Support Vector Machine

Water Quality Evaluation Model Based on Principal Component and Particle Swarm Optimization Support Vector Machine

Water Quality Evaluation of Wenyu River in Beijing by Matter Element Model

River water quality is an important indicator for identifying river changes and analyzing river health, and has an important impact on the ecological environment of the river basin. In this paper, the matter-element analysis method based on the coupling weight method is used to evaluate the water environment of the water quality measured data of Wenyu River in 2019, which provides a reference for water quality management and protection. Through the establishment of the object element to be evaluated, the classical domain, the section domain, the normalization of the evaluation standard, and the measured data, three representative indicators such as DO, NH3-N, and CODcr are selected as the object element to be evaluated. The standard value corresponding to the water quality standards of Grade I to V is the classic domain. The weight of river indicators is determined by the coupling of the ordinary objective weighting method and the multiple super-scale weighting method. After the weight is determined, the correlation degree is calculated and the matter-element analysis model for water quality evaluation is established. The results showed that the water quality of the Wenyu River in May 2019 was still mainly Grade V water, which was in line with the actual water quality situation. It shows that the method meets the feasibility and practicability in water quality evaluation and is relatively reliable.

High-Performance Computational Early Warning Analysis of Agricultural Economy Relying on Binary Fuzzy Cluster Analysis Algorithm

In this paper, a binary fuzzy cluster analysis algorithm is used for an in-depth study and analysis of high-performance computational early warning in the agricultural economy. The definition of interval type-two fuzzy set and its operation are summarized. Considering the uncertain information that will be encountered in the evaluation process, this paper constructs the evaluation model of rural informatization construction performance based on interval type-two fuzzy numbers, which improves the accuracy of the evaluation results. The fuzzy clustering centres are modified according to the nature of the fuzzy clustering centre matrix, and the optimal fuzzy clustering centres and optimal fuzzy clustering division matrix with consistent order are solved. Using the level eigenvalues to find out in the abundant water period, there are 4 monitoring sections of water quality evaluation results for still clean and 5 monitoring sections of water quality evaluation results for slight pollution. In the flat-water period, there are 2 monitoring sections of the water quality evaluation results that are still clean and 7 monitoring sections of the water quality evaluation results are slightly polluted. In the dry water period, the water quality evaluation results of the 9 monitoring sections are slightly polluted. The results and the use of integrated pollutant index evaluation method results are consistent, indicating that the use of fuzzy clustering model for water quality evaluation is practical and effective. Its functional structure includes management business, innovation business, service business, production business, and operation business functions, in addition to the design of the collection and storage scheme based on Hadoop data warehouse, to achieve accurate matching of information of science and technology agricultural services in the whole agricultural industry chain. Finally, the implementation and maintenance of the cloud-based science and technology agricultural service information system are deployed, and the operational effects of its information system-related functions are demonstrated using prototype design. This paper constructs a comprehensive information system for science and technology agricultural services based on cloud technology that integrates management, innovation, service, production, and operation, which meets not only the needs of traditional science and technology agricultural service information systems, such as expert consultation and training demonstration, but also other needs spawned by the development of agricultural modernization.

Application of Artificial Intelligence in the Process of Ecological Water Environment Governance and Its Impact on Economic Growth

With the increasing pollution of the ecological water environment, the treatment of the ecological water environment has become the focus of everyone’s attention. At present, there are many research results on water environment governance, but the effect is not ideal. In order to effectively control the ecological water environment and promote sustainable economic growth, this research combines artificial intelligence algorithms and applies them to the governance process to explore its application effects and its impact on economic growth. First, the environmental sensor of the corresponding module is designed according to the water environment factor, and the data of dissolved oxygen content, water temperature, turbidity, temperature and humidity, and smoke concentration in the water environment are collected. Then the dynamic time-varying exponential smoothing prediction method is used to predict water quality, and a water quality prediction model is established. Then use support vector machine (SVM) to train the collected data samples, use the decision tree-based SVM classification method to classify the data samples, establish a water quality evaluation model, and use particle swarm optimization algorithm to optimize the evaluation model. Put the sensors and predictive evaluation models established in this research design into the governance of a certain river reach, and collect relevant data from 7 : 00 to 18 : 00 on October 11, 2019. And predict and evaluate its water quality. The experimental results show that the average absolute error of predicting dissolved oxygen content is 0.97%, and the average absolute error of predicting phosphorus content is 2.27%. This shows that the application of artificial intelligence algorithms in the process of ecological water environmental governance can effectively help collect effective information and make more accurate predictions and evaluations of water quality, thereby improving governance efficiency and promoting sustainable economic growth.

Research on Marine Water Quality Evaluation Model Based on Improved Harmony Search Algorithm by Gaussian Disturbance to Optimize Takagi-Sugeno Fuzzy Neural Network

Shen, G., 2020. Research on marine water quality evaluation model based on improved harmony search algorithm by Gaussian disturbance to optimize Takagi-Sugeno fuzzy neural network. In: Liu, X. and Zhao, L. (eds.), Today's Modern Coastal Society: Technical and Sociological Aspects of Coastal Research. Journal of Coastal Research, Special Issue No. 111, pp. 283–287. Coconut Creek (Florida), ISSN 0749-0208.Marine water quality is affected by many factors and is difficult to evaluate accurately. An improved harmony search algorithm based on Gaussian disturbance (GDHS) is proposed to optimize the Takagi-Sugeno (T-S) fuzzy neural network (TSFNN) model of marine water quality evaluation. First, the basic harmony search algorithm is improved by the Gaussian disturbance global optimal harmony strategy and dynamic adjustment parameters. Second, the parameters of the T-S fuzzy neural network are optimized by GDHS to improve the stability and global convergence speed of the TSFNN. Finally, China's marine water quality standards and the actual conditions of the marine area to which the experimental data belong are considered, five indicators are selected to construct a neural network, and the water quality evaluation results of the data of 10 monitoring stations in the eastern waters of Qingdao show that the smaller the evaluation error of the GDHS-TSFNN model, the higher the accuracy.