Machine Learning Methodologies for Enhancing Predictive Accuracy in Environmental Impact Studies

Abstract

Introduction/Importance of the Study: Yearly, the scope and detail of EIAs continue to expand and therefore, the need for better prediction models likewise rises. The conventional techniques are inadequate in handling large data and the variation that is inherent in environmental issues. This research examines the applicability of ML techniques to reinforce predictive efficacy in EIAs to provide evidence-based recommendations for the betterment of the environmental situation.

Novelty Statement: This study systematically reviews several machine learning approaches for analyzing big environmental data with a focus on the constructed models’ performance in terms of prediction improvements over existing techniques.

Materials and Methods: The data for this study was gathered from well-established authorities with a anyway interest in the environment such as agencies that monitor the environment and governmental databases on air quality, water pollution, and land use change. The following machine learning models: support vector machines (SVM), decision trees, random forest, and deep learning models were adopted. Concerning the evaluation part, cross-validation, and other statistical tests including ANOVA, VIF analysis, and normality tests were used in testing the accuracy of the proposed model.

Results and Discussion : The study concluded that all four machine learning models resulted in a better accuracy of prediction when compared to the traditional models with Model B producing cross-validation accuracies that ranged from 78 to 84 percent. However, multicollinearity as determined from VIF was observed in some of the predictor variables which may require caution in the choice of the variables to include in the analysis. In general, the classifications achieved by machine learning models were better than the traditional methods, as reported by ANOVA and model evaluation.

Conclusion: Mobile application of machine learning methodologies in EIAs helps in improving predictive accuracy to contribute towards more accurate assessments of the environment. Work that should be done as part of future work includes; the selection of the model and controlling multicollinearity.  In this research paper, the following sections clearly state the study’s goal and scope, method, results, and future implications in the field of environmental impact studies.