A Novel Feature Selection Technique to Better Predict Climate Change Stage of Change

Hamed Naseri,Ricardo A Daziano,Zachary Patterson,Bobin Wang,E Owen D Waygood

doi:10.3390/su14010040

Abstract

Indications of people’s environmental concern are linked to transport decisions and can provide great support for policymaking on climate change. This study aims to better predict individual climate change stage of change (CC-SoC) based on different features of transport-related behavior, General Ecological Behavior, New Environmental Paradigm, and socio-demographic characteristics. Together these sources result in over 100 possible features that indicate someone’s level of environmental concern. Such a large number of features may create several analytical problems, such as overfitting, accuracy reduction, and high computational costs. To this end, a new feature selection technique, named the Coyote Optimization Algorithm-Quadratic Discriminant Analysis (COA-QDA), is first proposed to find the optimal features to predict CC-SoC with the highest accuracy. Different conventional feature selection methods (Lasso, Elastic Net, Random Forest Feature Selection, Extra Trees, and Principal Component Analysis Feature Selection) are employed to compare with the COA-QDA. Afterward, eight classification techniques are applied to solve the prediction problem. Finally, a sensitivity analysis is performed to determine the most important features affecting the prediction of CC-SoC. The results indicate that COA-QDA outperforms conventional feature selection methods by increasing average testing data accuracy from 0.7% to 5.6%. Logistic Regression surpasses other classifiers with the highest prediction accuracy.

Highlights

Publisher’s Note: MDPI stays neutralGovernments around the world are trying to reduce transportation-related greenhouse gas (GHG) emissions in response to concerns about climate change
The average testing data accuracy of Coyote Optimization Algorithm-Quadratic Discriminant Analysis (COA-Quadratic Discriminant Analysis (QDA)) was 0.7%, 0.9%, 2.2%, 3.8%, 4.8%, and 5.6% higher than that of Extra Trees Feature Selection (ETFS), Elastic net (EN), Random Forest Feature Selection (RFFS), all features, Lasso, and Principal component analysis (PCA), considering all classifiers, respectively
This study proposed a new Artificial Intelligence (AI) approach that was applied to predict individual climate change stage of change (CC-SoC)

Summary

Introduction

Publisher’s Note: MDPI stays neutralGovernments around the world are trying to reduce transportation-related greenhouse gas (GHG) emissions in response to concerns about climate change. An important aspect of trying to reduce emissions is individual attitudes towards climate change [1]. Various research within the field of transport has demonstrated that environmental attitudes can help explain travel behavior (e.g., Anable [3]; Susilo et al [4]; Gaker and Walker [5]); this link is important to understand as it is a major challenge with regards to personal emissions [6]. One of the most established measures is the General Ecological Behavior (GEB) tool which includes roughly 50 questions on various behaviors, including a few on transport. Another more general “world view” measure for the environment is the New Environmental Paradigm (NEP)

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Conclusion