# Time-Series Prediction of Long-Term Sustainability of Grounds Improved by Chemical Grouting
## 4 Conclusions and Future Studies  
It can be concluded that the MLP model is the most accurate prediction model compared to the ARIMA model and the SSR model. The accuracy of the output results of the MLP model further increased when the highly fluctuating data were omitted from the input data. For all three methods, their accuracy increased as the input data volume increased. Since the ARIMA model generated very large amount of overall errors when making long-term predictions from a minimum amount of input data, the results cannot be used for future reference. The followings are the major conclusions drawn from this study.  
1. The output results of the SSR model were moderate at their best. The accuracy increased with the increase in the amount of input data amount, although the output results were still generated in a range instead of a single value. However, the 10-year period prediction from 28 days of data was found to be more accurate than that from the 180 days and 1-year input data.
2. Although the MLP model generated the smallest errors among the three models, the margin of error remained and was only omitted when the highly fluctuating data were omitted from the input data. This might bring some sort of conflict in the scientific community regarding its reliability.
3. Since the MLP model can generate reliable results only for equivalent input data, the SSR model might be a better choice for long-term forecasting with a small amount of input data. In addition, since the chances of availability of long-term data, such as 5-year period data, are very low in all cases, the MLP method might not be favored in all situations.
4. The ARIMA model produced the most accurate predictions for short periods when the amount of input data was large, such as the case of 5-year input data, where the results for 730 days were more accurate than those using the MLP model.  
All three models have shown some shortcomings in terms of achieving the most reliable outcome, and, therefore, it is difficult to claim any one of these models is absolutely the best model for all situations. If the amount of available input data is equivalent to the future prediction period, then the MLP model is the best choice. If the amount of input data is small, but the prediction period is long-term, the SSR model is the best option. However, if the amount of available input data is large and the prediction period is short, then the ARIMA model is the best option. Thus, it is suggested that future research can be conducted with more time-series prediction models and comparisons should be made among these models to yield better results.  
Conceptualization, S.I. and S.N.; methodology, S.I.; software, S.S.; validation, S.S. and C.C.; formal analysis, S.S. and C.C.; investigation, H.K.; resources, H.K.; data curation, S.S. and C.C.; writing--original draft preparation, S.S.; writing--review and editing, S.I. and S.N.; visualization, S.I.; supervision, S.I.; project administration, S.I.; funding acquisition, S.I. and H.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.