**4. Conclusions**

Accurate time series forecasting for upcoming flood events is important, but it is a challenging task such as in Pakistan. The flood forecasting at the Swat River flow gauging station, particularly for downstream stations that lack discharge information, such as Saidu Sharif in this study, plays an important role in early flood warning systems. In this research, we examined the classical statistical models used such as the deep learning model, LSTM, and the regression model, SARIMA. In our comparison of the models for Kalam Station, the LSTM model achieved better results and more accurate forecasting performance than the SARIMA model. Linear data modeling of water flows for SARIMA yield better result as compared to other statistical deep learning models which are good for nonlinear datasets. The RMSE values of the LSTM model fit to the series found for training and testing; for oneyear-ahead forecasting, the values are 22.79 and 35.05, respectively. These results indicates that the deep learning algorithm is a dependable ideal solution for flood prediction due to its high precision.

**Author Contributions:** Conceptualization, M.I., M.Z. and M.D.M.; Models selection for water modeling, M.Z., M.A.S. and S.M.Z.; Data Collection, M.I., M.S. and Z.M. Model Training, Testing, and Forecast, M.I., M.D.M. and R.M.S.; Future Directions & Conclusions, M.Z. and D.Z.; writing—original draft preparation, M.I., M.D.M. and R.M.S.; writing a review and editing, M.I. and M.D.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Available on request after due procedure.

**Conflicts of Interest:** The authors declare no conflict of interest.
