scholarly journals Data-mining approach to investigate sedimentation features in combined sewer overflows

2011 ◽  
Vol 14 (3) ◽  
pp. 613-627 ◽  
Author(s):  
M. Carbone ◽  
L. Berardi ◽  
D. Laucelli ◽  
P. Piro
Keyword(s):  
Data Mining ◽  
Suspended Solids ◽  
Polynomial Regression ◽  
Experimental Studies ◽  
Combined Sewer Overflows ◽  
Detention Time ◽  
Evolutionary Polynomial Regression ◽  
Data Mining Approach ◽  
Aggregate Information ◽  
Two Parameters

Sedimentation is the most common and effectively practiced method of urban drainage control in terms of operating installations and duration of service. Assessing the percentage of suspended solids removed after a given detention time is essential for both design and management purposes. In previous experimental studies by some of the authors, the expression of iso-removal curves (i.e. representing the water depth where a given percentage of suspended solids is removed after a given detention time in a sedimentation column) has been demonstrated to depend on two parameters which describe particle settling velocity and flocculation factor. This study proposes an investigation of the influence of some hydrological and pollutant aggregate information of the sampled events on both parameters. The Multi-Objective (EPR-MOGA) and Multi-Case Strategy (MCS-EPR) variants of the Evolutionary Polynomial Regression (EPR) are originally used as data-mining strategies. Results are proved to be consistent with previous findings in the field and some indications are drawn for relevant practical applicability and future studies.

scholarly journals Asset deterioration analysis using multi-utility data and multi-objective data mining

2009 ◽  
Vol 11 (3-4) ◽  
pp. 211-224 ◽  
Author(s):  
D. A. Savic ◽  
O. Giustolisi ◽  
D. Laucelli
Keyword(s):  
Data Mining ◽  
Polynomial Regression ◽  
Single Case ◽  
Model Parameters ◽  
Physical Systems ◽  
Multi Objective ◽  
Evolutionary Polynomial Regression ◽  
Physically Based ◽  
Physical Laws

Physically-based models derive from first principles (e.g. physical laws) and rely on known variables and parameters. Because these have physical meaning, they also explain the underlying relationships of the system and are usually transportable from one system to another as a structural entity. They only require model parameters to be updated. Data-driven or regressive techniques involve data mining for modelling and one of the major drawbacks of this is that the functional form describing relationships between variables and the numerical parameters is not transportable to other physical systems as is the case with their classical physically-based counterparts. Aimed at striking a balance, Evolutionary Polynomial Regression (EPR) offers a way to model multi-utility data of asset deterioration in order to render model structures transportable across physical systems. EPR is a recently developed hybrid regression method providing symbolic expressions for models and works with formulae based on pseudo-polynomial expressions, usually in a multi-objective scenario where the best Pareto optimal models (parsimony versus accuracy) are selected from data in a single case study. This article discusses the improvement of EPR in dealing with multi-utility data (multi-case study) where it has been tried to achieve a general model structure for asset deterioration prediction across different water systems.

Sign in / Sign up

Export Citation Format

Share Document