A comprehensive review of the role of soft computing techniques in municipal solid waste management

Abstract

ABSTRACT In today's world, Municipal Solid Waste (MSW) management is considered the primary issue for protecting the surrounding environment, individuals, and natural resources. To effectively protect it, various strategies have been developed. Among various strategies, many attempts have been made from the perspective of soft computing, especially through Artificial Intelligence (AI) techniques aimed at achieving better results. Although much research has been conducted in this field, few efforts have focused on using AI to solve MSW problems. This article systematically reviews soft computing techniques related to waste management (for example, MSW generation, collection, treatment, and disposal). This review comprehensively analyses the contribution of different AI technologies to various evolutionary algorithms, such as Artificial Neural Networks (ANN), fuzzy-based methods, Genetic Algorithms (GA), Ant Colony Optimization (ACO), etc., and machine learning algorithms such as Support Vector Machines (SVM), Multiple Linear Regression (MLR), etc., that are used to solve MSW problems. It is observed from the literature that 53% of researchers use evolutionary algorithms and 47% of researchers use machine learning algorithms to solve MSW problems.