Authors: Zachariah Munyoro, Alice Nambiro
Abstract: Different researchers have carried out Beach-litter prediction, with basis of their studies being detection and counting of existing litter. Forecasting requires integration of temporal litter patterns, with contextual conditions that influence accumulation. Continuous monitoring of beach litter is necessary within this area of study. Conventional process of monitoring provides observation to a given extent, which limits rapid or automated assessment. AI-based detection allows automatic identification and quantification, allowing monitoring to continue from detection towards prediction. Existing methods address detection, forecasting or contextual integration separately. Various predictive studies use historical litter records, contextual variables, camera observations and Machine Learning. Limited architectural integration connects AI-derived litter measurements directly with temporal and contextual information for multi-predictor variables within multi-duration dimensions. This study developed an AI-based predictive model architecture. The model connected automated litter measurement temporal construction, contextual integration, predictive modelling and forecasting generation. Images were captured through IoT-enabled cameras, subjected under RT-DETRv2, which detected, classified and counted the objects. The output of this process is category-specific quantitative litter observations. The image-level counts are aggregated into beach-week observations, forming temporal litter representation. The temporal information provides the predictive baseline. The predictive model generated category-specific forecasts at multiple future durations. These were weekly, bi-weekly and monthly forecasts. The architecture linked AI-based measurement (RT-DETRv2), data integration, prediction and monitoring output in one end-to-end flow. The model provides a framework for anticipatory beach-litter monitoring instead of detection alone. The output of the predictive model architecture provide diverse options necessary for specific management decision making as well as a feedback loop to the predictive process.
