Assessment of Soil Environment Pollution Based on Fuzzy Pattern Recognition Model

As one of the basic elements of the ecosystem and natural environment, the soil is closely related to human life. However, the problem of soil environment pollution is becoming more and more serious, which needs to be solved urgently. It will provide a reference for solving the problem of soil environment pollution if a suitable method can be found to evaluate the degree of soil environment pollution. The degree of soil environment pollution belongs to the fuzzy concept. It is a fuzzy pattern recognition problem to evaluate the degree of soil environment pollution according to the soil environment level standard value. The fuzzy pattern recognition method of soil environment pollution assessment makes full use of the fuzzy characteristics existing in soil environment assessment, considers the common influence of the weight of evaluation factors and the index value, and also considers the correlation between indexes and the similarity of index characteristics of different sampling points, therefore, the interference of some man-made certain factors is concealed. The model was applied to the soil sampling point in Wudi County of Shandong Province in the Yellow River basin of China. The results showed that the evaluation of soil environment pollution by this method accorded with the actual situation and can provide a basis for ensuring the stability of soil ecological environment, improving the quality of cultivated land, and improving regional ecological conditions. At the same time, in view of the current problem of soil environment pollution, the paper calls on human beings to pay attention to environmental protection and be responsible for their own safety and also proposes four aspects of treatment schemes and measures.

Development of Fuzzy Pattern Recognition Model for Underground Metal Mining Method Selection

Selection of underground metal mining method is a crucial task for the mining industry to excavate the ore deposit with proper safety and economy. The objective of the proposed study is to demonstrate the application of a fuzzy pattern recognition model for the decision-making of the most favourable underground metal mining method for a typical ore deposit. The model considers eight factors (shape, depth, dip, rock mass rating [RMR] of ore zone, RMR of footwall, RMR of hanging wall, thickness of the ore body, grade distribution), which influence the mining method, as input variables. The weights of these factors were determined using the analytic hierarchy process (AHP). The study used the pair-wise comparison method to determine the relative membership degrees of qualitative and quantitative criteria as well as weights of the criteria set. The model validation was done with the deposit characteristics of Uranium Corporation of India Limited (UCIL), Tummalapalle mine selected. The weighted distances for easiest to adopt are found to be 0.1436, 0.0230, 0.0497, 0.2085, 0.0952, 0.1228, and 0.1274, respectively, for block caving, sublevel stoping, sublevel caving, room and pillar, shrinkage stoping, cut and fill stoping, and squares set stoping. The results indicate that the room and pillar mining method is having the maximum weighted distance value for the given ore deposit characteristics and thus assigned the first rank. It was observed that the mining method selected using fuzzy pattern recognition model and the actual mining method adopted to extract the ore deposit are the same.

Combining Heuristics with Simulation and Fuzzy Logic to Solve a Flexible-Size Location Routing Problem under Uncertainty

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm.