Predictive model using neural networks and multitechnique validation in digital environments with scarce data for sustainable WEEE management

The sustainable management of Waste Electrical and Electronic Equipment (WEEE) is a critical global challenge, particularly in contexts with limited data. This study proposes a predictive model based on artificial neural networks, developed from surveys and historical records in the city of Guayaquil, with the aim of estimating WEEE generation on annual and monthly scales. The model was structured in phases of data collection, preprocessing, training, and validation, integrating sociodemographic variables and categories of discarded devices. To ensure reliability, a multi-technique validation protocol was applied, including Hold-Out, Stratified K-Fold, and Bootstrap Sampling methods. Results showed strong performance, with a coefficient of determination (R²) of 0.9125 in initial tests, an average of 0.9097 in cross-validation, and up to 0.9789 with bootstrap, significantly outperforming traditional linear regression methods. These findings confirm the model’s ability to capture non-linear relationships and produce accurate forecasts in data-scarce environments. It is concluded that neural networks represent an effective tool to support strategic planning and decision-making in sustainable WEEE management, providing a replicable framework for other regions facing similar challenges.