Abstract
With the rapid development of the economy, and fossil fuel consumption lacking systematic emission controls, China has experienced substantially elevated concentrations of air pollutants, which not only degrades regional air quality but also poses significant impacts on public health. However, faced with the demand for a large number of experts in air pollution protection, people with real expertise for air pollutant management are difficult to find. Therefore, individualized recommendation is an effective and sustainable method for enhancing the professional level of managers and is good for improving the quality of air pollutant management. Thus, this paper initially proposes a novel framework to recommend strengths in air pollutant management. This framework comprises four stages: data preprocessing is the first stage; then, after constructing ability classifications and ability assessment strategies, activity experiences are transformed into corresponding ability values; next, a multilayer perceptron deep neural network (MLP-DNN) is used to predict potential types according to their ability values; finally, a hybrid system is constructed to recommend suitable and sustainable potential managers for air pollutant management. The experiments indicate that the proposed method can assess the full picture of people’s strengths, which can recommend suggestions for building a scientific and rational specialties recommendation system for governments and schools. This method can have significant effects on pollutant emission reduction by enhancing the professional level of managers with regard to air pollutant management.
Highlights
Air pollution in China has attracted considerable attention from the public, scientists, and policymakers [1,2], as the hazards of ultrafine particles affect human health
This paper proposes a novel individualized recommendation framework for air pollutant management level improvement
Where Bi+ is the number of correctly classified students of the i-th cultivating type, Ai is the number of students that were classified to the i-th cultivating type by multilayer perceptron deep neural network (MLP-DNN), and Bi is the number of students that were the real i-th cultivating type referring to the real dataset
Summary
Air pollution in China has attracted considerable attention from the public, scientists, and policymakers [1,2], as the hazards of ultrafine particles affect human health. Air pollution management in China is badly in need of a large number of professionals every year, and talent in this area needs a long time of professional training to suit future jobs. It is very difficult to master professional knowledge in a short time This requires that students in this major have certain talent in air pollution management. By analyzing students’ first-view data, we can more objectively identify their talents and reduce the influence of subjective factors, which would provide considerable help for air pollutant management level improvement. (2) Faced with the chaotic and irregular mass of survey data, a multidimensional ability evaluation model is proposed to acquire the abilities and talents of different people in different aspects It formulates targeted countermeasures for improving the level of managers by finding people with talent in the field of air pollutant management.
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