From: Asset management analytics for urban water mains: a literature review
Cluster of research | Techniques | Applications | Input parameters |
---|---|---|---|
Deterministic model | – Multiple Regression models – Linear Regression – Poisson Regression – Multivariate adaptive regression splines | – Water mains annual break rates prediction – Finding correlation between water main failure rates and input parameters | Physical Factors: DIA, LEN, AGE, MAT |
Probabilistic model | – Cox proportional hazard model – Weibull proportional hazard survival analysis | – Water main failures prediction – Time to next break prediction | Physical Factors: DIA, LEN, AGE, MAT, THK |
Operational Factors: WP, VEL, TRF, RT, WPH | |||
Environmental Factors: SR, SPH, MC, FI, SZN | |||
Artificial intelligence model | – ANN – Random Forest – Xgboost – LR – SVC – Evolutionary Polynomial Regression – Boosted regression tree | – Predict the failure rate of pipeline networks – Binary classification which shows whether or not the pipe break | Physical Factors: DIA, LEN, AGE, MAT, YEAR, NC, NT |
Operational Factors: WP, TRF, NB, BD, BY | |||
Environmental Factors: MC, ST, PP, LU, LO |