WEEE Prediction Model Based on Neural Networks

Abstract

The need to develop intelligent and innovative solutions to reduce pollution generated by Waste Electrical and Electronic Equipment (WEEE) led to the construction of a WEEE prediction model based on neural networks. The information supporting the model is derived from data obtained through a survey, as well as historical data on WEEE generation in Ecuador. The model aims to estimate waste generation within a specific month and year. Neural network algorithms were used for the model's functionality due to their adaptability to dynamic data like the ones utilized. The development of this model considered five phases: data collection, preprocessing, model generation, model application, and verification and continuous improvement. It is concluded that the proposed model provides a detailed description of the architecture, phases, and procedures required for its operation, facilitating its understanding and subsequent implementation.