Comparison of Decision Tree Algorithms for Predicting Potential Air Pollutant Emissions with Data Mining Models

Abstract

Predicting air pollutant emissions from potential industrial installations is important for controlling air pollution and future planning of air quality management. This paper proposes the classification and prediction of the emission levels of industrial air pollutant sources using decision tree technique. It presents the comparison results of many decision tree algorithms (C4.5, CART, NBTree, BFTree, LADTree, REPTree, Random Tree, Random Forest, LMT, FT and Decision Stump) in terms of running time, classification accuracy and applicability. In comparison, six performance metrics were used: classification accuracy, precision, recall, f-measure, mean absolute error and mean squared error. The aim of the study is to determine the best classifier as a data mining model for the prediction of emission levels of the industrial plants as dependent variable from known values of independent variables: the physical region of the plant, the height of the plant, working hours, the height of the stack, the diameter of the stack, the velocity of the waste in the stack, the temperature of the waste in the stack, plume rise, source classification code, control equipment type and emissions method code. In the experimental studies, all these algorithms are applied on the dataset that consists of sulphur oxide emission levels of industrial pollutants in Izmir. According to the results, while C4.5 algorithm has the highest accuracy value, Decision Stump algorithm is the fastest one. The average classification accuracy found as 82.4% empirically shows the benefits of using decision tree technique in the classification and the prediction of emission levels.

Şirket İflaslarının Tahminlenmesinde Karar Ağacı Algoritmalarının Karşılaştırmalı Başarım Analizi

“…Sınıflandırma ve tahmin etmede karar ağaçlarının kullanılması, eğitim verisinden karar ağacı modelinin oluşturulması, bu modelin, test verisi kullanılarak uygun sınama ölçütleri aracılığıyla değerlendirilmesi ve ilgili modelin gelecekteki değerleri tahmin edilmesinde kullanılması şeklinde işlemektedir [23][24].…”

Fuzzy interval programming for energy and environmental systems management under constraint-violation and energy-substitution effects: A case study for the City of Beijing

“…Meanwhile, many challenges (e.g., energy technologies and environmental regulations are changing rapidly) remain in the planning of energy systems over a long-time horizon. These lead to multiple uncertainties in relevant processes of energy decision-making (Diwekar et al, 1997;Gritsevskyi and Nakićenovi, 2000), which are hardly tackled by most of the previous studies, (b) multiple risks are existing in energy management systems such as financial and operational ones, potentially resulting in a series of economic and environmental consequences, which have been generally ignored or simplified by previous studies, (c) most of the previous inexact optimization methods (such as interval and fuzzy linear programming) were effective in dealing with uncertainties in energy systems planning (Birant, 2011;Cai et al, 2008Cai et al, , 2009cDong et al, 2011Dong et al, , 2012Huang et al, 2010;Li et al, 2011;Liu et al, 2000;Yang et al, 2011), however, they could not help conduct violation analysis of flexible constraints to identify and address the inherent risks of energy systems, and (d) there were no reports on incorporation of substitution effects among different energy resources within the management of energy systems under different violation risks and multiple uncertainties.…”

A Fuzzy Evacuation Management Model Oriented Toward the Mitigation of Emissions

“…The transportation sector accounts for about 25% of total commercial energy consumed and 50% of total oil produced worldwide, representing a major contributor to urban air pollution and global climate change (Gorham, 2002;Yoshida and Matsuhashi, 2009;Birant, 2011). There is thus a growing awareness of its role in the efforts for mitigating emissions of air pollutants and greenhouse gases.…”