Multi-layer perceptron artificial neural network for environmental risks prediction of SW-induced pollution in Dar es Salaam, Tanzania

<p>Many metropolitan areas face significant environmental challenges posed by improper disposal and management of solid waste. As a result, environmental risks have emerged as a pressing concern, prompting dedicated research efforts. This study on environmental risk prediction of Dar es Salaam SW coincides with a mounting governmental effort over rising pollution levels from inadequate SW management. Using the multi-layer perceptron artificial neural network (MLP-ANN) model, it effectively examines the prevailing conditions and forecasts waste generation rates (WGRs) and environmental risk index (ERI) associated with SW pollution. As confirmed with 94.5% prediction accuracy and 86.5% success rate of the MLP-ANN model, WGRs in Dar es Salaam have doubled in less than two decades. Besides, over 40% of the overall generated SW is left unattended. Consequently, the ERI exhibits a consistent upward trajectory throughout the assessment period, with intermittent fluctuations between Level II and III but a persistent overall increase. Projections indicate an escalation of ERI to Level IV by 2025/26 and to a critical threshold (Level V) by 2038. The key indices such as pressure, state, and impact are anticipated to reach critical thresholds ahead of the comprehensive ERI. This underscores the imperative for timely interventions and the urgency of addressing SW management issues to curb the escalating environmental risks in Dar es Salaam and other metropolises with similar challenges.<strong></strong></p>