Jiping Xu

Analysis of algae growth mechanism and water bloom prediction under the effect of multi-affecting factor

The formation process of algae is described inaccurately and water blooms are predicted with a low precision by current methods. In this paper, chemical mechanism of algae growth is analyzed, and a correlation analysis of chlorophyll-a and algal density is conducted by chemical measurement. Taking into account the influence of multi-factors on algae growth and water blooms, the comprehensive prediction method combined with multivariate time series and intelligent model is put forward in this paper. Firstly, through the process of photosynthesis, the main factors that affect the reproduction of the algae are analyzed. A compensation prediction method of multivariate time series analysis based on neural network and Support Vector Machine has been put forward which is combined with Kernel Principal Component Analysis to deal with dimension reduction of the influence factors of blooms. Then, Genetic Algorithm is applied to improve the generalization ability of the BP network and Least Squares Support Vector Machine. Experimental results show that this method could better compensate the prediction model of multivariate time series analysis which is an effective way to improve the description accuracy of algae growth and prediction precision of water blooms.

A Novel Group Decision-Making Method Based on Sensor Data and Fuzzy Information

Algal bloom is a typical phenomenon of the eutrophication of rivers and lakes and makes the water dirty and smelly. It is a serious threat to water security and public health. Most scholars studying solutions for this pollution have studied the principles of remediation approaches, but few have studied the decision-making and selection of the approaches. Existing research uses simplex decision-making information which is highly subjective and uses little of the data from water quality sensors. To utilize these data and solve the rational decision-making problem, a novel group decision-making method is proposed using the sensor data with fuzzy evaluation information. Firstly, the optimal similarity aggregation model of group opinions is built based on the modified similarity measurement of Vague values. Secondly, the approaches’ ability to improve the water quality indexes is expressed using Vague evaluation methods. Thirdly, the water quality sensor data are analyzed to match the features of the alternative approaches with grey relational degrees. This allows the best remediation approach to be selected to meet the current water status. Finally, the selection model is applied to the remediation of algal bloom in lakes. The results show this method’s rationality and feasibility when using different data from different sources.

A Fusion Water Quality Soft-Sensing Method Based on WASP Model and Its Application in Water Eutrophication Evaluation

Water environment protection is of great significance for both economic development and improvement of people’s livelihood, where modeling of water environment evolution is indispensable in water quality analysis. However, many water quality indexes related to water quality model cannot be measured online, and some model parameters always vary among different water areas. Thus, this paper proposes a water quality soft-sensing method based on the water quality mechanism model to simulate evolution of water quality indexes online, where unscented Kalman filter is utilized to estimate model parameters. Furthermore, a modified fuzzy comprehensive evaluation method is presented to evaluate the level of water eutrophication condition. Finally, the water quality data collected from Taihu Lake and Beihai Lake are used to validate the effectiveness and generality of the proposed method. The results show that the proposed soft-sensing method is able to describe the variation of related water quality indexes, with better accuracy compared to nonlinear least squares based method and traditional trial-and-error based method. On this basis, the water eutrophication condition can be also accurately evaluated.

An approach of recursive timing deep belief network for algal bloom forecasting

The forecasting methods of water bloom in existence are hard to reflect nonlinear dynamic change in algal bloom formation mechanism, leading to poor forecasting accuracy of bloom. To solve this problem, this paper deeply analyzes the generation process of algal bloom, introduces the recursive time series algorithm into the deep belief network model and improves the model structure and training algorithm, and proposes a forecasting method based on the recursive timed deep belief network model. The model introduces the current moments and historical time values of the characterization factors and influencing factors at the input layer, and increases the connection between the input layer and the hidden layer of the deep belief network. A recursive algorithm is used to establish the relationship between the current time value of the characterization factor and the historical time value of the characterization factor, and the connection between the current time value of the hidden layer and the influencing factor is increased. By re-extracting the characteristics of the hidden layer at each moment, and then fine tuning the network parameters by the BP neural network, a recursive timing deep belief network model is finally constructed. The results show that compared with the existing forecasting methods, this method can extract the characteristics of time series data more accurately and completely to deal with the dynamic nonlinear process and can further improve the forecast accuracy of algal blooms.

Study on Mechanism Experiments and Evaluation Methods for Water Eutrophication

The process of water eutrophication involves the interaction of external factors, nutrients, microorganisms, and other factors. It is complex and has not yet been effectively studied. To examine the formation process of water eutrophication, a set of orthogonal experiments with three factors and four levels is designed to analyze the key factors. At the same time, with the help of a large amount of monitoring data, the principal component analysis method is used to extract the main components of water eutrophication and determine the effective evaluation indicators of eutrophication. Finally, the Bayesian theory of uncertainty is applied to the evaluation of the eutrophication process to evaluate the sample data. The simulation results demonstrate the validity of the research method.

An approach of improved dynamic deep belief nets modeling for algae bloom prediction

Algae bloom outbreak is a dynamic nonlinear process with time-varying characteristics and it is difficult for existing algal bloom prediction method to consider the complex characteristics, which leads to low accuracy prediction. For the problem, a dynamic deep belief nets model that combines time series analysis with deep learning methods is proposed by analyzing algal bloom outbreak mechanism. The model introduces historical moment in input layer, increases connection between input layer and hidden layer, uses contrastive divergence algorithm to introduce historical moment in hidden layer and weight and bias algorithms are given timing characteristic in pre-training stage. At the same time, the model adopts dynamic learning rate to complete pre-training and the back-propagation algorithm is used to fine tune network parameters to complete the whole model training. The instance validation results show that the method can more accurately describe dynamic nonlinear process than other prediction methods and further improve prediction accuracy.

The Adaptive-Clustering and Error-Correction Method for Forecasting Cyanobacteria Blooms in Lakes and Reservoirs

Globally, cyanobacteria blooms frequently occur, and effective prediction of cyanobacteria blooms in lakes and reservoirs could constitute an essential proactive strategy for water-resource protection. However, cyanobacteria blooms are very complicated because of the internal stochastic nature of the system evolution and the external uncertainty of the observation data. In this study, an adaptive-clustering algorithm is introduced to obtain some typical operating intervals. In addition, the number of nearest neighbors used for modeling was optimized by particle swarm optimization. Finally, a fuzzy linear regression method based on error-correction was used to revise the model dynamically near the operating point. We found that the combined method can characterize the evolutionary track of cyanobacteria blooms in lakes and reservoirs. The model constructed in this paper is compared to other cyanobacteria-bloom forecasting methods (e.g., phase space reconstruction and traditional-clustering linear regression), and, then, the average relative error and average absolute error are used to compare the accuracies of these models. The results suggest that the proposed model is superior. As such, the newly developed approach achieves more precise predictions, which can be used to prevent the further deterioration of the water environment.

Research on the Design of Food Safety Supervision Information System in Beijing

Currently, there are still many problems in food safety supervision of Beijing, more than 80% of the foods are depending on the outer supply which has increased difficulty in the effective control from the source of food safety. The planning levels of food safety management are still in need, the food safety risk evaluation and warning are scarcely used. With the development of the investment to the outer supply base, the food safety supervision mechanism should be perfected while the food input risk should be controlled. This paper build the food safety supervision information system in Beijing by using the HACCP theory based on the intelligent information processing technology and the food safety data platform, in order to improve the management level and efficiency, and then to realize the overall monitoring of food safety through the all-process supervision of the outer supply and food circulation.

Research on Skin Texture Classification by Gray Level Co-occurrence Matrix and the BP Neural Network

It’s very common to use the skin texture of gray level co-occurrence matrix to calculate the four most representative eigenvalues of human facial skin image: energy, moment of inertia, correlation and entropy. To test whether the four eigenvalues can represent the skin texture information, the article designed a verification experiment: the article used comparison data included arithmetic average roughness(Ra), average roughness(Rz), and smooth depth data(Rt) measured from DERMATOP V3 of CK in Germany, and experimental data included the four eigenvalues, to do principal component analysis, respectively, for unrelated principal component as the input data of BP neural network classifier. The experimental results show that using the four eigenvalues, the classification accuracy is higher. The method using gray level co-occurrence matrix to extract facial skin texture eigenvalue can relatively reflect the degree of human facial texture state than texture information measured by DERMATOP V3, which provides a simple and effective method for the data acquisition of skin texture. Keywordsskin texture; gray level co-occurrence matrix; principal component analysis; BP neural network classifier

Research on Algae Growth Mechanism Model Based on Genetic Algorithm in Lakes and Reservoirs

According to further researches of algae blooms growth mechanism in lakes and reservoirs, an algae growth mathematical mechanism model was built. The data obtained from simulation of the algae blooms formation in sun room laboratory is treated as sample points, then using Genetic Algorithm to optimize and calibrate the parameters in the mechanism model, solved the nonlinear and random problem in the formation of algae blooms. Simulation results show that, this mechanism model can well simulate algae growth changing regularity in the formation of algae blooms, for optimization, the residual error becomes smaller, and the fitting precision improves dramatically.