Machine learning approach for automated beach waste prediction and management system: A case study of Mumbai

Abstract

Asia’s coastlines are choking in waste. The region is now home to many of the world’s most polluted beaches. The populous Indian Cities are growing economically but in an unsustainable manner. With Mumbai counted among topmost polluted beaches in the world, it is the need of the hour to take necessary steps for effective waste management by systematic data analysis for deriving useful information from waste generation patterns. The major objective of the study is pattern recognition and beach waste quantum prediction based on 5 years data, with a frequency of daily waste collection. The size of the training data set is 1,661 days and the validation data set is 335 days. The influence of population trend, waste generation during festivals, special days, weekends, and seasonal variations form the basis for the analysis. Using machine learning algorithms, the study identifies and investigates data patterns for the case study of Dadar-Mahim beach. Data frequency and weights are correlated with occurrence of events, festivals, weekends, and seasons. Exploratory Data Analysis (EDA) is employed for data preprocessing and wrangling, followed by a Random Forest algorithm-based model for the prediction of waste generated at Dadar-Mahim beach. The major challenges in data prediction are limited data availability and variation in the dates of festivals and holidays as well as lack of waste segregation information. Despite the above-mentioned challenges, the observations indicate the model’s average accuracy for making predictions of around 60%. The Graphic User Interface (GUI) developed based on the model provides a user-friendly application for predicting the total daily generation of beach waste with reasonable precision. On the basis of the model’s outcome and applicability, a schematic approach for efficient beach waste management is proposed. The recommendations would serve as guidelines for Urban Local Bodies (ULBs) to automate the collection, transport, and disposal of beach waste.

Photocatalytic degradation of drugs and dyes using a maching learning approach

“…[49][50][51] Furthermore, machine learning algorithms in waste management can also assist in decision-making processes for waste treatment and disposal. [52][53][54][55][56][57] Machine learning algorithms help in identifying the most appropriate treatment technologies and strategies for different types of waste, considering factors such as waste composition, environmental impact, and costeffectiveness. 25,[58][59][60][61][62] Because machine learning algorithms are suitable for depicting complex nonlinear processes, they are gradually being adopted to better manage waste and facilitate sustainable environmental development.…”

“…49–51 Furthermore, machine learning algorithms in waste management can also assist in decision-making processes for waste treatment and disposal. 52–57 Machine learning algorithms help in identifying the most appropriate treatment technologies and strategies for different types of waste, considering factors such as waste composition, environmental impact, and cost-effectiveness. 25,58–62…”