Rapid Detection Method of Ecological Pollution in Scenic Spot under Different Tourism Disturbance.

At present, the ecological pollution of scenic spots in China is becoming more and more serious, which has attracted wide attention of the society. The rapid detection method of ecological pollution in scenic spots under different tourism disturbance is deeply researched in this paper. On the basis of BP neural network, the ecological pollution in scenic spot can be divided into six factors of sewage, debris, garbage deposits harmful to human body and air, soil physical properties, soil nutrient content, and heavy metal content. Each type of influencing factor can be divided into four levels of very good, good, general and poor. The scenic area ecological pollution detection model is built based on three-layer feedforward of neural network. The classification results of eco-polluted garbage attribute and different tourism disturbance (severe, moderate and mild) are used as input values of the rapid detection model of eco-pollution in scenic spot, and the output values are the influencing factors, impact grades and detection values of eco-pollution in scenic spot. Signoid transform function is used to transform the output value to the interval [0, 1], which realizes the rapid detection of ecological pollution in scenic spots under different tourism disturbances. The results show that the proposed method can effectively detect soil physical properties, soil nutrient content and soil heavy metal content in experimental scenic spots under different tourism disturbance (severe area, moderate area, and mild area). The greater the intensity of tourism disturbance, the lower the soil water content and clay, the higher the soil bulk density and PH, the lower the content of soil organic matter and nutrients, and the higher the content of heavy metal. The average time obtained with the proposed method to detect ecological pollution in three scenic spots is only 1.01 s, which verifies the rapidity of the proposed method. [ABSTRACT FROM AUTHOR]