FORECASTING UPWELLING IN LAKE MANINJAU USING VECTOR AUTOREGRESSIVE, SUPPORT VECTOR MACHINE AND DASHBOARD VISUALIZATION

Lake Maninjau experiences periodic upwelling events that disrupt water quality, harm fish stocks, and pose socioeconomic challenges to surrounding communities. This study aimed to enhance upwelling prediction accuracy by integrating Vector Autoregressive (VAR) time series modelling with Support Vector Machine (SVM) classification. A five-year dataset (2020–2024) of daily climate variables surface temperature, precipitation, and wind speed was collected from NASA. Data stationarity was confirmed using Box-Cox transformations and Augmented Dickey-Fuller tests, while Granger Causality analysis revealed bidirectional relationships among the variables. The optimal forecasting model, VAR(17), was selected based on the Akaike Information Criterion (AIC), ensuring residuals met white-noise criteria. K-means clustering then labelled potential upwelling days, and these labels were employed to train SVM classifiers. An interactive dashboard was developed using Python and Streamlit to facilitate real-time forecasts and classification outputs. The VAR(17) model produced highly accurate forecasts, reflected by minimal error metrics (e.g., RMSE < 0.60). SVM classification of potential upwelling events achieved strong performance, consistently attaining F1-scores above 0.95. By merging time series forecasts with event classification, the hybrid VAR–SVM framework outperformed single-method approaches in identifying and predicting upwelling episodes. This integrated modelling strategy effectively addresses the complexity of upwelling in Lake Maninjau, enabling timely decision-making for fisheries management and local tourism stakeholders. Future work may incorporate additional environmental indicators (e.g., dissolved oxygen, pH) and extend dashboard functionalities to bolster sustainable resource management and community resilience