Abstract
Forecasting the upcoming water level of a dam or reservoir is the goal of water level forecasting in reservoirs. In order to predict the water level of the dam or reservoir for the subsequent consecutive time interval, this paper proposes a method based on the ARIMA (Auto Regressive Integrated Moving Averages) machine learning model, which fed on historical data of water levels with respect to consecutive time intervals. Additionally, the anticipated output, whether it be in TMC or MFTC units, is depending on the data that is given. The model’s performance is further examined in the study using certain machine learning metrics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nguyen, X.H.: Combining statistical machine learning models with ARIMA for water level forecasting: the case of the Red river. Adv. Water Res. 142, 103656 (2020)
Titolo, A.: Use of time-series NDWI to monitor emerging archaeological sites: case studies from Iraqi artificial reservoirs. Remote Sens. 13(4), 786 (2021)
Wang, J., et al.: Reliable model of reservoir water quality prediction based on improved ARIMA method. Environ. Eng. Sci. 36(9), 1041–1048 (2019)
Arvor, D., et al.: Monitoring thirty years of small water reservoirs proliferation in the southern Brazilian Amazon with Landsat time series. ISPRS J. Photogram. Remote Sens. 145, 225–237 (2018)
Skariah, M., Suriyakala, C.D.: Forecasting reservoir inflow combining exponential smoothing, ARIMA, and LSTM models. Arab. J. Geosci. 15(14), 1–11 (2022)
Huang, L., et al.: Evolutionary optimization assisted delayed deep cycle reservoir modeling method with its application to ship heave motion prediction. ISA Trans. 126, 638–648 (2022)
Üneş, F., et al.: Estimating dam reservoir level fluctuations using datadriven techniques (2019)
Paparoditis, E., Politis, D.N.: The asymptotic size and power of the augmented Dickey-Fuller test for a unit root. Econ. Rev. 37(9), 955–973 (2018)
Bai, Z., Choi, K.P., Fujikoshi, Y.: Consistency of AIC and BIC in estimating the number of significant components in high-dimensional principal component analysis. Ann. Stat. 46(3), 1050–1076 (2018)
Yan, B., et al.: Flood risk analysis of reservoirs based on full-series ARIMA model under climate change. J. Hydrol., 127979 (2022)
Tegegne, G., Kim, Y.-O.: Representing inflow uncertainty for the development of monthly reservoir operations using genetic algorithms. J. Hydrol. 586, 124876 (2020)
Ali, A., Bello, A.M., Raymond, J.: Machine learning algorithms for predicting reservoir porosity using stratigraphic dependent parameters. Glob. J. Comput. Sci. Technol. (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Kovvuri, A.R., Uppalapati, P.J., Bonthu, S., Kandula, N.R. (2023). Water Level Forecasting in Reservoirs Using Time Series Analysis – Auto ARIMA Model. In: Gupta, N., Pareek, P., Reis, M. (eds) Cognitive Computing and Cyber Physical Systems. IC4S 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 472. Springer, Cham. https://doi.org/10.1007/978-3-031-28975-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-28975-0_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28974-3
Online ISBN: 978-3-031-28975-0
eBook Packages: Computer ScienceComputer Science (R0)