TIME SERIES FORECASTING AND CLASSIFICATION OF POTENTIAL SAFETY RISKS OF STORING RADIOACTIVE WASTE NEAR SURFACE DISPOSAL
DOI:
https://doi.org/10.34288/jri.v8i3.440Keywords:
Near Surface Disposal, Machine Learning, Time Series Forecasting, ClassificationAbstract
Long-term radioactive waste management, especially at Near Surface Disposal (NSD) facilities, requires a predictive approach and adaptive monitoring system to anticipate risks to groundwater quality. This research aims to develop a time series model to predict groundwater level parameters including depth, pH, and tds and integrate it with a rule-based ESG risk classification system and machine learning. The method used includes the Prophet time series model for predicting groundwater parameters in the next 50 years. The prediction results are classified using rule-based classification which is then evaluated using the Random Forest algorithm. The final application was developed web-based using Streamlit. The Prophet model provided the best prediction performance for depth MAE: 0.71; MAPE: 7.41% and pH MAE: 0.21; MAPE: 4.89%, but less accurate for TDS MAE: 12.16; MAPE: 31,62%. The Random Forest model produced classification accuracy of up to 98% and was able to replicate the rule-based classification system well. The integration of these models can produce a predictive system that supports decision making in sustainable radioactive waste management.
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