Development of a Long Term Forecasting Model for Dam Inflow in South Korea Using the LTSF Algorithm

For long-term time series forecasting of hydro-meteorological variables, physical models and artificial intelligence (AI)-based models have been used. Long-term forecast using physical models may have a limited predictive performance due to assumptions and conditions used in the physical model. Although AI-based models for long-term forecast of hydro-meteorological variables have a restricted capacity to explain phenomena, they have practical strengths such as high precision and short computation time. Zeng et al. (2022) proposed Long-Term Time Series Forecasting (LTSF) models and showed that they outperformed transformer-based AI models for long-term forecast In this study, a long-term forecasting model was developed using the LTSF model applied to dam inflow data and assess the feasibility of LTSF in the long-term forecast of inflow data. For comparison, The Long Short Term Memory (LSTM) algorithm was employed. The results show that the LTSF showed a comparable performance of long-term forecast to the LSTM although the structures of LTSF models are much simpler than LSTM. The LSTF models can be considered as a good alternative of LSTM when the forecast task need prompt computation.

Zeng, A., Chen, M., Zhang, L., & Xu, Q. (2022). Are Transformers Effective for Time Series Forecasting? The Chinese University of Hong Kong and International Digital Economy Academy (IDEA).