TY - GEN
T1 - The Design of Pipeline Leak Location Prediction System with Incremental Extreme Learning Machine (I-ELM)
AU - Suyanto,
AU - Masduqi, Ali
AU - Arifin, Syamsul
AU - Fernanda, Mohammad Alfaris
AU - Asmara, Gigih Yuli
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Water loss is a global concern faced by all nations. It stems from various factors, categorized into physical and non- physical losses. Physical loss, mainly attributed to pipe leaks, significantly contributes to total water loss, with 80% attributed to leaks. Thus, an Artificial Neural Network-based leak detection system using Incremental Extreme Learning Machine algorithm is designed. A District Metered Area (DMA) is selected for study. DMA specifications are used for modeling through EPANET software. After validating the DMA model, leak simulations employ emitter coefficients (0.5-1 LPS). Data from 17 junctions under different leak scenarios are collected. An I-ELM leak detection system is developed, using 17-feature pressure data and two target variables for training. Testing data, derived from the split training set, results in leak location predictions for pipeline sections with average deviations of 3.7577, 2.4310, and 0.5630. Leak size predictions have average deviations of 0.0807, 0.0585, and 0.0543. Overall, leak location prediction's RMSE is 0.0516, and leak size prediction's RMSE is 0.0696.
AB - Water loss is a global concern faced by all nations. It stems from various factors, categorized into physical and non- physical losses. Physical loss, mainly attributed to pipe leaks, significantly contributes to total water loss, with 80% attributed to leaks. Thus, an Artificial Neural Network-based leak detection system using Incremental Extreme Learning Machine algorithm is designed. A District Metered Area (DMA) is selected for study. DMA specifications are used for modeling through EPANET software. After validating the DMA model, leak simulations employ emitter coefficients (0.5-1 LPS). Data from 17 junctions under different leak scenarios are collected. An I-ELM leak detection system is developed, using 17-feature pressure data and two target variables for training. Testing data, derived from the split training set, results in leak location predictions for pipeline sections with average deviations of 3.7577, 2.4310, and 0.5630. Leak size predictions have average deviations of 0.0807, 0.0585, and 0.0543. Overall, leak location prediction's RMSE is 0.0516, and leak size prediction's RMSE is 0.0696.
KW - DMA
KW - I-ELM
KW - Leak Location
KW - Pressure
KW - RMSE
UR - http://www.scopus.com/inward/record.url?scp=85189755057&partnerID=8YFLogxK
U2 - 10.1109/ICMERALDA60125.2023.10458203
DO - 10.1109/ICMERALDA60125.2023.10458203
M3 - Conference contribution
AN - SCOPUS:85189755057
T3 - Proceedings: ICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications
SP - 214
EP - 219
BT - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023
Y2 - 24 November 2023 through 24 November 2023
ER -