Baseline Estimation and System Performance Assessment of Municipal Solid Waste in Tourism Areas: A Case Study of Phuket, Thailand

Information from the abstract

The management of municipal solid waste (MSW) in a region driven by tourism is a challenge. The dynamics of the waste produced can fluctuate significantly. Islands are particularly vulnerable due to geographic constraints and limited infrastructure. Using data to forecast and evaluate MSW management strategies would help make policy decisions. This research introduces a data-driven framework that integrates tourism dynamics with a predictive baseline for MSW generation and facilitates ongoing evaluation of system performance. The framework comprises four operational zones, namely data acquisition, baseline modelling, deviation analysis, and dashboard-based communication. It also follows the waste stream from upstream waste generation, through midstream collection and transfer, to downstream treatment and disposal. Phuket Island, Thailand, was chosen as a case study. The island has strong tourism-driven waste variability, limited waste management infrastructure, and a clear upstream–midstream–downstream waste-management chain. Tourism was explicitly integrated as an exogenous factor to capture transient demand effects. The amount of MSW was modelled using monthly tourism data. Support Vector Regression was selected to establish the expected waste baseline owing to its high predictive accuracy (MAE = 1143 t, RMSE = 1352 t and MAPE = 3.3%) outperforming other tested models (i.e., SARIMAX, Mean Baseline, Seasonal Naive, Random Forest and Histogram-Based Gradient Boost). Then, it was used to establish the baseline under normal conditions. The residual was then calculated as the difference between the observed values, and the performance of the maintenance system can be further assessed using the deviation analysis. A normal range defined within 3σ of the residual percentages from a baseline; values outside this range are flagged as anomalies. The results were integrated into a prototype dashboard to support operational monitoring, policy evaluation, and stakeholder-orientated interpretation. The proposed framework provides a practical basis for an evidence-based adaptive policy in tourism-dependent regions. Future work should incorporate waste composition and spatially explicit data to further improve the MSW management protocol. Considering that tourism demand exhibits seasonal patterns influenced by external factors, the results of this research offer an invaluable tool for waste management.

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Related topics: Municipal Solid Waste Management · Food Waste Reduction and Sustainability · Healthcare and Environmental Waste Management