PREDICTION OF RESERVOIR CONTENTS TO IDENTIFY DISCHARGE NEEDS AT THE INTAKE DOOR

Nazilatus Sa'idah; Esti Wulandari; Djoko Laksono

Authors

Nazilatus Sa'idah Civil Engineering Department, 17 Agustus 1945 University, Surabaya, Indonesia
Esti Wulandari Civil Engineering Department, 17 Agustus 1945 University, Surabaya, Indonesia
Djoko Laksono Civil Engineering Department, 17 Agustus 1945 University, Surabaya, Indonesia

Keywords:

Prediction, Reservoir Contents, Intake Door

Abstract

The rapid development of technology has given rise to new methods in science. Among these sciences, data prediction is a science that is often used to find out data on events that will appear in the future, one of which is forecasting discharge data. Water data that fills a reservoir can also be predicted by knowing past rainfall data that occurred in that area. This study aims to examine the water content of the Lowayu Reservoir by predicting past rainfall data to find out the rainfall that occurred in 2026. The results of the rainfall data prediction are then transformed into water discharge that will fill the reservoir and then be used to meet water needs for agriculture. The prediction method used is the Holt-Winters exponential smoothing model multiplicative method. The results of the study showed that the highest rainfall was obtained in December - January 2024 - 2026 at 220 mm - 224 mm with a MAPE error value of 15.8% and produced a maximum water volume in the reservoir of 213,400,000 liters -217,280,000 liters/month during 2024-2026. Based on the reservoir water content data, intake gate 1 has an opening height of 0.37 m for 115 days for 1 harvest season with an agricultural land area of 231.5 ha. While intake gate 2 has an opening height of 1.2 m for 1 harvest season with an area of 1137.5 ha

References

Agricultural Research and Development Agency. 2020. Seeds are the Key. Jakarta. http://www.litbang.pertanian.go.id/info-aktual/3962/. [December 20, 2020]. (2021) Reservoir Sedimentation.

Aini, AN, Intan, PK, & Ulinnuha, N. (2022). “Prediction of Average Monthly Rainfall in Pasuruan Using the Holt-Winters Exponential Smoothing Method”. JRST (Journal of Science and Technology Research), 5(2), 117.

Angel Novita Tri Lara Atica, Gusfan Halik, Saifurridzal.(2022). Rainfall Prediction Using CHIRPS and PERSIANN-CDR Satellite Rainfall Data in Bedadung Watershed, Jember Regency. Journal of Water Resources Engineering. Volume 2. No. 2 pp. 69-80. DOI 10.56860/jtsda.v2i2.36

D. Desmonda, T. Tursina, dan M. A. Irwansyah, “Prediksi Besaran Curah Hujan Menggunakan Metode Fuzzy Time Series,” J. Sist. dan Teknol. Inf., vol. 6, no. 4, hal. 141, 2018, doi: 10.26418/justin.v6i4.27036.

Desvina, A. P., & Ratnawati. (2014). Penerapan Model Vector Autoregressive (VAR) Untuk Peramalan Curah Hujan Kota Pekanbaru. Jurnal Sains, Teknologi Dan Industri, 11(2), 151–159.

E. M. Manihuruk, H. Harianto, and N. Kusnadi, “Analisis Faktor Yang Memengaruhi Petani Memilih Pola Tanam Ubi Kayu Serta Efisiensi Teknis Di Kabupaten Lampung Tengah,” J. AGRISEP Kaji. Masal. Sos. Ekon. Pertan. dan Agribisnis, vol. 17, no. 2, pp. 139–150, 2018, doi: 10.31186/jagrisep.17.2.139-150.

E. Pujiati, D. Yuniarti, and R. Goejantoro, “Forecasting Using Brown’s Double Exponential Smoothing Method (Case Study: Consumer Price Index (CPI) of Samarinda City),” J. Exponential, vol. 7, no. 1, pp. 33–40, 2016.

F. Prawaka, A. Zakaria, and S. Tugiono, “Analysis of Missing Rainfall Data Using Normal Ratio Method, Inversed Square Distance, and Algebraic Average Method (Case Study of Rainfall of Several Rainfall Stations in Bandar Lampung Area),” J. Civil Engineering and Design, vol. 4, no. 3, pp. 397– 406, 2016.

HDES Sinaga and N. Irawati, “Comparison of Double Moving Average with Double Exponential Smoothing in Forecasting Medical Consumables,” Jurteksi (Journal of Technology and Information Systems), vol. IV, no. 2, pp. 197–204, 2018.

IGW Sena, JW Dillak, P. Leunupun, and AJ Santoso, (2020). Predicting rainfall intensity using Naïve Bayes and Information Gain methods (case study: Sleman Regency). J Phys. Conf. Ser. 1577(1), doi: 10.1088/1742-6596/1577/1/012011.

Kementerian Pekerjaan Umum, Direktorat Jenderal Sumber Daya Air, Direktorat Irigasi dan Rawa, Standar Perencanaan Irigasi, Kriteria Perencanaan Bagian Saluran, KP – 03 ; 2013. Direktur Jenderal Sumber Daya Air; Jakarta.

L. J. Sinay and S. N. Aulele. 2015. Rainfall and Number of Rainy Days Prediction in Ambon Island using Vector Autoregression Model. International Seminar Basic Science. Ambon

Luthfiarta, A., Febriyanto, A., Lestiawan, H., & Wicaksono, W. (2020). Analisa Prakiraan Cuaca dengan Parameter Suhu, Kelembaban, Tekanan Udara, dan Kecepatan Angin Menggunakan Regresi Linear Berganda. JOINS (Journal of Information System), 5(1), 10–17. https://doi.org/10.33633/joins.v5i1.2760

Musfiroh et al. (2023). Application of Principal Component Analysis (PCA) and Long Short-Term Memory (LSTM) Methods in Predicting Daily Rainfall Prediction. Building of Informatics, Technology and Science (BITS) Volume 5, No 1, pp. 1−11 ISSN 2684-8910 (print media) ISSN 2685-3310 (online media) DOI 10.47065/bits.v5i1.3114

Perdana, DA, Zakaria, A., and Sumiharn, 2015. Synthetic Modeling Study of Daily Rainfall at Several Rainfall Stations in Pringsewu Regency. Jrsdd, 3 (1), 45–56

Prawaka, F., Zakaria, A., and Tugiono, S., 2016. Analysis of Missing Rainfall Data Using the Normal Ratio, Inversed Square Distance, and Algebraic Average Methods (Case Study of Rainfall at Several Rainfall Stations in the Bandar Lampung Area)

R. Utami and S. Atmojo, “Perbandingan Metode Holt Exponential Smoothing dan Winter Exponential Smoothing Untuk Peramalan Penjualan Souvenir,” J. Ilm. Teknol. Inf. Asia, vol. 11, no. 2, pp. 123–130, 2017.

S. Putramulyo and S. Alaa, “Monthly Rainfall Prediction in Samarinda City Using Regression Equation with Temperature and Humidity Data Predictors,” Eig. Math. J., vol. 2, no. 2, pp. 13–16, 2018.

Safitri, T., Dwidayati, N., & Sugiman. (2017). Perbandingan Peramalan Menggunakan Metode Exponential Smoothing Holt-Winters dan Arima. UNNES Journal Of Mathematics, 6(1), 48–58.

SF Hilmi and E. Nurjani, “The Relationship between Rainfall Variability and Flood Occurrence in Bandung Area,” J. Bumi Indones., vol. 8, no. 4, pp. 1–11, 2019.

Sisinggih, D., Wahyuni, S., Nugroho, R., Hidayat, F., & Rahman, K.I. (2020). Sediment transport functions in HEC-RAS 4.0 and their evaluation using data from sediment flushing of Wlingi reservoir – Indonesia. Prosiding IOP Conference Series: Earth and Environmental Science, Volume 437, The 3rd International Conference of Water. Resources Development and Environmental Protection 12–13 October 2019, Malang. doi:10.1088/1755-1315/437/1/012014.

SNI, 19-6728.1-2002 (2002) Penyusunan Neraca Sumber Daya. Indonesia: Standar. Nasional Indonesia.

Sri Dewi SS (2019) Rainfall Calculation Information System. Final Project, Diploma III Telecommunication Technology, Telkom University, Bandung

Sun, H., Wang, S., & Hao, X. (2017). An improved analytic hierarchy process method for the evaluation of agricultural water management in irrigation districts of northern China. Agricultural Water Management, 179, 324-337.

Tiurma ES, Eben OZ, Estetika Z. (2023). Reliable Discharge Analysis (Case Study on Parmongan II PLTM). CONSTRUCT: Civil Engineering Journal Vol. 2, No. 2, pp. 13-24.

Triatmojo, B., 1998. Applied Hydrology. Yogyakarta: Beta Offset.

Tukidi. 2007. Meteorology and Climatology. Semarang: Department of Geography FIS UNNES

Wei, W.S. 1994. Time Series Analysis: Univariate and Multivariate. Method. New York: Addison Wesley Publishing Company

Yasar, M., Siwar, C., & Firdaus, RR (2015). Assessing paddy farming sustainability in the Northern Terengganu integrated agricultural development area (IADA KETARA): A structural equation modeling approach. Pacific Science Review B: Humanities and Social Sciences, 1(2), 71-75.

Yonida, Arinda Dwi (2018), Kondisi Lahan Pertanian Indonesia, Agribisnis. Universitas Gajah Mada, Yogyakarta, https://farming.id/.