Integrated Streamflow Forecasting System: A Step Towards Smart Flood Management

Irfan Ardiansah, Alfonsus Mario Agung, Chay Asdak, Nurpilihan Bafdal, Roni Kastaman, Selly Harnesa Putri, Desy Nurliasari Suparno

Abstract


This study aims to create an online monthly streamflow forecast system that will assist users in managing water resources and avoiding floods. We integrated a regression model into the system, utilizing historical rainfall and streamflow selective information from multiple monitoring stations in the Upper Cimanuk sub-basin. Users can access the online system to input and view rainfall and streamflow data and enumerate monthly streamflow rate projections. To verify the system's forecast accuracy, we compared it with manual calculations employing the velocity-area method and field observations. The system provides reasonably accurate forecasts, as indicated by the system's high coefficient of determination (R2) value of 0.91. However, discrepancies between forecasted and measured values suggest that we still can improve the accuracy of the system by incorporating additional variables and more comprehensive data. Future enhancements may include the incorporation of precipitation intensity, duration, basin shape, and basin size, as well as additional validation using a broader array of field data. The developed monthly streamflow forecasting system is a valuable tool for analyzing and forecasting streamflow rates, providing a basis for making informed decisions in water resource management and flood disaster mitigation.


Full Text:

PDF

References


M. Tanoue, R. Taguchi, S. Nakata, S. Watanabe, S. Fujimori, and Y. Hirabayashi, “Estimation of Direct and Indirect Economic Losses Caused by a Flood With Long‐Lasting Inundation: Application to the 2011 Thailand Flood,” Water Resour. Res., vol. 56, no. 5, May 2020, doi: 10.1029/2019WR026092.

Z. Qin, M. Storozum, H. Liu, X. Zhang, and T. R. Kidder, “Investigating environmental changes as the driving force of agricultural intensification in the lower reaches of the Yellow River: A case study at the Sanyangzhuang site,” Quat. Int., vol. 521, pp. 25–34, Jun. 2019, doi: 10.1016/j.quaint.2019.06.033.

Z. Vojinovic, Flood Risk: The Holistic Perspective. in Urban Hydroinformatics Series. IWA Publishing, 2015. [Online]. Available: https://books.google.co.id/books?id=GmIbCgAAQBAJ

E. Savitri and I. Pramono, “ANALISIS BANJIR CIMANUK HULU 2016,” J. Penelit. pengelolaan Drh. Aliran Sungai, vol. 1, no. 2, pp. 97–110, Oct. 2017, doi: 10.20886/jppdas.2017.1.2.97-110.

J. G. Putri, S. Suharyanto, and P. S. Atmojo, “Analisis banjir Subdas Cimanuk untuk menentukan status peringatan dini banjir Kota Garut,” Rang Tek. J., vol. 4, no. 2, pp. 229–239, 2021.

F. A. Hirpa et al., “Calibration of the Global Flood Awareness System (GloFAS) using daily streamflow data,” J. Hydrol., vol. 566, pp. 595–606, Nov. 2018, doi: 10.1016/j.jhydrol.2018.09.052.

Y. Gao, C. Merz, G. Lischeid, and M. Schneider, “A review on missing hydrological data processing,” Environ. Earth Sci., vol. 77, no. 2, p. 47, Jan. 2018, doi: 10.1007/s12665-018-7228-6.

P. C. Austin and E. W. Steyerberg, “The number of subjects per variable required in linear regression analyses,” J. Clin. Epidemiol., vol. 68, no. 6, pp. 627–636, Jun. 2015, doi: 10.1016/j.jclinepi.2014.12.014.

Reza Pahlevi, “APJII: Penetrasi Internet Indonesia Capai 77,02% pada 2022,” katadata, 2022. https://databoks.katadata.co.id/datapublish/2022/06/10/apjii-penetrasi-internet-indonesia-capai-7702-pada-2022

S. Otuagoma, E. Ogujor, and P. Kuale, “Comparative Measurement of Stream Flow in the Ethiope River for Small Hydropower Development,” Niger. J. Technol., vol. 34, no. 1, p. 184, Dec. 2014, doi: 10.4314/njt.v34i1.23.

A. Marulitua Sinaga, “Case Study on Testing of Web-Based Application: Del’s Students Information System,” J. Telecommun. Electron. Comput. Eng., vol. 9, no. 2-4 SE-Articles, pp. 1–5, Jul. 2017, [Online]. Available: https://jtec.utem.edu.my/jtec/article/view/2349

D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to Linear Regression Analysis. in Wiley Series in Probability and Statistics. Wiley, 2021. [Online]. Available: https://books.google.co.id/books?id=tCIgEAAAQBAJ

I. Ardiansah, N. Bafdal, E. Suryadi, and A. Bono, “Design of micro-climate data monitoring system for tropical greenhouse based on arduino UNO and raspberry pi,” IOP Conf. Ser. Earth Environ. Sci., vol. 757, no. 1, 2021, doi: 10.1088/1755-1315/757/1/012017.

A.-A. Hussein, V. Govindu, and A. G. M. Nigusse, “Evaluation of groundwater potential using geospatial techniques,” Appl. Water Sci., vol. 7, no. 5, pp. 2447–2461, Sep. 2017, doi: 10.1007/s13201-016-0433-0.

G. B. Adane, B. A. Hirpa, C.-H. Lim, and W.-K. Lee, “Evaluation and Comparison of Satellite-Derived Estimates of Rainfall in the Diverse Climate and Terrain of Central and Northeastern Ethiopia,” Remote Sens., vol. 13, no. 7, p. 1275, Mar. 2021, doi: 10.3390/rs13071275.

Y. Wu and P. Xie, “Exploration of Enterprise Audit Information Management System Model Based on Data Flow Diagram,” in 2021 International Wireless Communications and Mobile Computing (IWCMC), IEEE, Jun. 2021, pp. 1997–2001. doi: 10.1109/IWCMC51323.2021.9498870.

A. Y. Aleryani, “Comparative study between data flow diagram and use case diagram,” Int. J. Sci. Res. Publ., vol. 6, no. 3, pp. 124–126, 2016.

R. Rashkovits and I. Lavy, “Mapping Common Errors in Entity Relationship Diagram Design of Novice Designers,” Int. J. Database Manag. Syst., vol. 13, no. 1, pp. 1–19, 2021.




DOI: https://doi.org/10.31449/inf.v47i9.4890

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.