Water Quality Ecological Risk Assessment with Sedimentological Approach

The potential ecological risk index (ERI) is a useful diagnostic tool for water system assessment. It’s based on sedimentology and combined with environmental chemistry and ecotoxicology. This chapter introduces the approach, including basic theory, calculation formula, evaluation criteria, and its parameters. Using a case study, the modification of the classification of the potential ecological risk is discussed. The water quality of the Liaohe River is assessed by the potential ecological risk index with the sedimentological approach. The sediments samples were collected from 19 sites and were analyzed for seven substances (Cd, As, Cu, Ni, Pb, Cr, and Zn) to assess the potential ecological risk. According to the results, Cd was found to be the main pollutant in the Liaohe River. The consequence of the monomial potential ecological risk factor E r i (mean) of each element is ranked as: Cd (93.39%) > As (3.13%) > Cu (1.26%) > Ni (0.97%) > Pb (0.70%) > Cr (0.34%) > Zn (0.22%). The ERI results (358.35) indicate the Liaohe River poses a very high potential ecological risk

Adaptive semi-supervised affinity propagation clustering algorithm based on structural similarity

Uzimajući u obzir nezadovoljavajuće djelovanje grupiranja srodnog širenja algoritma grupiranja, kada se radi o nizovima podataka složenih struktura, u ovom se radu predlaže prilagodljivi nadzirani algoritam grupiranja srodnog širenja utemeljen na strukturnoj sličnosti (SAAP-SS). Najprije se predlaže nova strukturna sličnost rješavanjem nelinearnog problema zastupljenosti niskoga ranga. Zatim slijedi srodno širenje na temelju podešavanja matrice sličnosti primjenom poznatih udvojenih ograničenja. Na kraju se u postupak algoritma uvodi ideja eksplozija kod vatrometa. Prilagodljivo pretražujući preferencijalni prostor u dva smjera, uravnotežuju se globalne i lokalne pretraživačke sposobnosti algoritma u cilju pronalaženja optimalne strukture grupiranja. Rezultati eksperimenata i sa sintetičkim i s realnim nizovima podataka pokazuju poboljšanja u radu predloženog algoritma u usporedbi s AP, FEO-SAP i K-means metodama.In view of the unsatisfying clustering effect of affinity propagation (AP) clustering algorithm when dealing with data sets of complex structures, an adaptive semi-supervised affinity propagation clustering algorithm based on structural similarity (SAAP-SS) is proposed in this paper. First, a novel structural similarity is proposed by solving a non-linear, low-rank representation problem. Then we perform affinity propagation on the basis of adjusting the similarity matrix by utilizing the known pairwise constraints. Finally, the idea of fireworks explosion is introduced into the process of the algorithm. By adaptively searching the preference space bi-directionally, the algorithm’s global and local searching abilities are balanced in order to find the optimal clustering structure. The results of the experiments with both synthetic and real data sets show performance improvements of the proposed algorithm compared with AP, FEO-SAP and K-means methods

An Improved K-means Algorithm and Its Application for Assessment of Culture Industry Listed Companies

Owing to K-means algorithm has the shortcoming that it always neglects the influence of cluster size when the Euclidean distances between samples and cluster center is calculated. In order to overcome the lack, the influence of cluster size is introduced into K-means algorithm in this paper. Therefore an improved K-means algorithm based on gravity is proposed, namely GK-means algorithm. The experimental simulation results show that GK-means algorithm has better performance compared with K-means algorithm. So the GK-means algorithm is adopted for assessing the performance of culture industry listed companies in this paper. Furthermore some satisfactory results are also obtained

On the Accuracy of Judgments in the AHP

Errors or inaccuracy always occurs when we use the Analytic Hierarchy Process to aid decision making. This paper shows the errors can be divided into two parts. One is called System Error and another is called Judgment Error. System Error is caused by the judgment ratio of pairwise compare matrix which must be taken from set of {1/9, 1/8, ..., 1,2,..., 9}. The Judgment Error is caused by human wrong judgment. The computational results in this paper demonstrate that the System Error may cause the confusable priority of the alternatives and a proposed method which increase the ratio accuracy can clear the priority of the alternatives

Swarm Intelligence Optimization Algorithms and Their Application

Swarm intelligence optimization algorithm is an emerging technology tosimulate the evolution of the law of nature and acts of biological communities, it has simple and robust characteristics. The algorithm has been successfully applied in many fields. This paper summarizes the research status of swarm intelligence optimization algorithm and application progress. Elaborate the basic principle of ant colony algorithm and particle swarm algorithm. Carry out a detailed analysis of drosophila algorithm and firefly algorithm developed in recent years, and put forward deficiencies of each algorithm and direction for improvement

A Wolf Pack Optimization Theory Based Improved Density Peaks Clustering Approach

In view of the problem that the Density Peaks Clustering (DPC) algorithm needs to manually set the parameter cut-off distance (dc) we propose a Wolf Pack optimization theory based Density Peaks Clustering approach (WPA-DPC). Firstly, we introduce dc parameter into the Wolf Pack Algorithm (WPA) to speed up the search. Secondly, we introduce the WPA into the DPC algorithm; the cut-off distance is used as the location of the wolf group. Finally, we make silhouette index in the search process as the fitness value, and the optimal location of the wolf group is the parameter value at the end. The simulation results show that compared with the traditional Density Peaks Clustering algorithm, the proposed algorithm is closer to the true clustering number. According to the evaluation results of silhouette and f-measure, the quality of clustering and the accuracy are greatly improved

An Efficient Universal Bee Colony Optimization Algorithm

The artificial bee colony algorithm is a global optimization algorithm. The artificial bee colony optimization algorithm is easy to fall into local optimal. We proposed an efficient universal bee colony optimization algorithm (EUBCOA). The algorithm adds the search factor u and the selection strategy of the onlooker bees based on local optimal solution. In order to realize the controllability of algorithm search ability, the search factor u is introduced to improve the global search range and local search range. In the early stage of the iteration, the search scope is expanded and the convergence rate is increased. In the latter part of the iteration, the algorithm uses the selection strategy to improve the algorithm accuracy and convergence rate. We select ten benchmark functions to testify the performance of the algorithm. Experimental results show that the EUBCOA algorithm effectively improves the convergence speed and convergence accuracy of the ABC algorithm