A Data-Driven Framework for Operational Management of Pumping Stations Using Statistical methods in Water Transmission Infrastructure
Subject Areas : Journal of Building Information ModelingFarhad Pazhuheian 1 , Alireza Farahbakhsh 2 , Ali Liaghat 3
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Keywords: pumping station, failure count, electricity consumption, spatial regression, generalized additive model,
Abstract :
This research presents a data-driven framework that integrates spatial modeling techniques and nonlinear methods to analyze the performance of pumping stations in water transmission infrastructure. The study highlights the importance of considering spatial and temporal factors to enhance operational reliability and optimize resource allocation.Modeling the number of failures against electrical energy consumption in pumping stations enables better maintenance planning. By analyzing the relationship between energy use and failures, patterns can be identified to predict potential breakdowns and schedule preventive maintenance more effectively. This approach helps reduce unexpected downtime, lower costs, and improve system efficiency and equipment lifespan.By combining advanced statistical methods, such as spatial regression and generalized additive model, the study develops a comprehensive tool for predicting pumping station performance. A case study at the Pumping Station in Iran demonstrates how these techniques can help analyze the of pumping stations in water transmission.
Abadia, R., Rocamora, C., Ruiz, A., & Puerto, H. (2008). Energy efficiency in irrigation distribution networks I: theory. Biosystems engineering, 101(1), 21-27.
Aliyu, R., Mokhtar, A. A., & Hussin, H. (2023). A Study on Comparison of Classification Algorithms for Pump Failure Prediction. Journal of Advanced Industrial Technology and Application, 4(2), 48-65.
Anselin, L. (1988). Spatial econometrics: methods and models (Vol. 4). Springer Science & Business Media.
Bao, C., & Chen, X. (2017). Spatial econometric analysis on influencing factors of water consumption efficiency in urbanizing China. Journal of Geographical Sciences, 27, 1450-1462.
Blokus-Dziula, A., Dziula, P., Kamedulski, B., & Michalak, P. (2023). Operation and maintenance cost of water management systems: Analysis and optimization. Water, 15(17), 3053.
Barton, N. A., Farewell, T. S., & Hallett, S. H. (2020). Using generalized additive models to investigate the environmental effects on pipe failure in clean water networks. Npj Clean Water, 3(1), 31.
Chatfield, C., & Xing, H. (2019). The analysis of time series: an introduction with R. Chapman and hall/CRC.
Christodoulou, S., Gagatsis, A., Agathokleous, A., Xanthos, S., & Kranioti, S. (2012). Urban water distribution network asset management using spatio-temporal analysis of pipe-failure data. Proc. ICCCBE, 27-29.
Cliff, A. D., & Ord, J. K. (1981). Spatial processes: models & applications. (No Title).
Cressie, N. (2015). Statistics for spatial data. John Wiley & Sons.
Dadar, S., Đurin, B., Alamatian, E., & Plantak, L. (2021). Impact of the pumping regime on electricity cost savings in urban water supply system. Water, 13(9), 1141.
Du, B., Ye, J., Zhu, H., Sun, L., & Du, Y. (2023). Intelligent monitoring system based on spatio–temporal data for underground space infrastructure. Engineering, 25, 194-203.
Elhorst, J. P. (2014). Spatial econometrics: from cross-sectional data to spatial panels (Vol. 479, p. 480). Heidelberg: Springer.
Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical analysis, 24(3), 189-206.
Hou, C. C., Wang, H. F., Li, C., & Xu, Q. (2020). From metal–organic frameworks to single/dual-atom and cluster metal catalysts for energy applications. Energy & Environmental Science, 13(6), 1658-1693.
Ikramov, N., Majidov, T., Kan, E., & Mukhammadjonov, A. (2020, July). Monitoring system for electricity consumption at pumping stations. In IOP Conference Series: Materials Science and Engineering (Vol. 883, No. 1, p. 012101). IOP Publishing.
Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern recognition letters, 31(8), 651-666.
Kofinas, D., Ulanczyk, R., & Laspidou, C. S. (2020). Simulation of a water distribution network with key performance indicators for spatio-temporal analysis and operation of highly stressed water infrastructure. Water, 12(4), 1149.
Liu, X., Liu, H., Wan, Z., Chen, T., & Tian, K. (2015). Application and study of internet of things used in rural water conservancy project. Journal of Computational Methods in Science and Engineering, 15(3), 477-488.
Luna, T., Ribau, J., Figueiredo, D., & Alves, R. (2019). Improving energy efficiency in water supply systems with pump scheduling optimization. Journal of cleaner production, 213, 342-356.
MacQueen, J. (1967, January). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics (Vol. 5, pp. 281-298). University of California press.
Moran, P. A. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17-23.
Mutambik, I. (2024). Assessing Urban Vulnerability to Emergencies: A Spatiotemporal Approach Using K-Means Clustering. Land, 13(11), 1744.
Qiu, P., Yan, J., Xu, H., & Yu, Y. (2024). Health status assessment of pump station units based on spatio-temporal fusion and uncertainty information. Scientific Reports, 14(1), 24096.
Shi, F. (2018). Data-driven predictive analytics for water infrastructure condition assessment and management (Doctoral dissertation, University of British Columbia).
Tiony, C. K. (2013). An energy assessment of the water pumping Systems at the Gigiri pumping station (Doctoral dissertation, University of Nairobi).
Wood, S. N. (2017). Generalized additive models: an introduction with R. chapman and hall/CRC.
Yates, M. A., & Weybourne, I. (2001). Improving the energy efficiency of pumping systems. Journal of Water Supply: Research and Technology—AQUA, 50(2), 101-111.
