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.

scholarly journals Advances in data-driven analyses and modelling using EPR-MOGA

2009 ◽  
Vol 11 (3-4) ◽  
pp. 225-236 ◽  
Author(s):  
O. Giustolisi ◽  
D. A. Savic
Keyword(s):  
Genetic Algorithm ◽  
Polynomial Regression ◽  
Variable Number ◽  
Sequential Search ◽  
Multi Objective ◽  
Polynomial Coefficients ◽  
Evolutionary Polynomial Regression ◽  
Multi Objective Genetic Algorithm ◽  
Regression Techniques

Evolutionary Polynomial Regression (EPR) is a recently developed hybrid regression method that combines the best features of conventional numerical regression techniques with the genetic programming/symbolic regression technique. The original version of EPR works with formulae based on true or pseudo-polynomial expressions using a single-objective genetic algorithm. Therefore, to obtain a set of formulae with a variable number of pseudo-polynomial coefficients, the sequential search is performed in the formulae space. This article presents an improved EPR strategy that uses a multi-objective genetic algorithm instead. We demonstrate that multi-objective approach is a more feasible instrument for data analysis and model selection. Moreover, we show that EPR can also allow for simple uncertainty analysis (since it returns polynomial structures that are linear with respect to the estimated coefficients). The methodology is tested and the results are reported in a case study relating groundwater level predictions to total monthly rainfall.

scholarly journals Evaluating performances of simplified physically based models for landslide susceptibility

2015 ◽  
Vol 12 (12) ◽  
pp. 13217-13256 ◽  
Author(s):  
G. Formetta ◽  
G. Capparelli ◽  
P. Versace
Keyword(s):  
Landslide Susceptibility ◽  
Goodness Of Fit ◽  
Shallow Landslides ◽  
Model Parameters ◽  
Test Case ◽  
Susceptibility Analysis ◽  
Physically Based ◽  
Private And Public ◽  
Physically Based Models

Abstract. Rainfall induced shallow landslides cause loss of life and significant damages involving private and public properties, transportation system, etc. Prediction of shallow landslides susceptible locations is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, and statistics. Usually to accomplish this task two main approaches are used: statistical or physically based model. Reliable models' applications involve: automatic parameters calibration, objective quantification of the quality of susceptibility maps, model sensitivity analysis. This paper presents a methodology to systemically and objectively calibrate, verify and compare different models and different models performances indicators in order to individuate and eventually select the models whose behaviors are more reliable for a certain case study. The procedure was implemented in package of models for landslide susceptibility analysis and integrated in the NewAge-JGrass hydrological model. The package includes three simplified physically based models for landslides susceptibility analysis (M1, M2, and M3) and a component for models verifications. It computes eight goodness of fit indices by comparing pixel-by-pixel model results and measurements data. Moreover, the package integration in NewAge-JGrass allows the use of other components such as geographic information system tools to manage inputs-output processes, and automatic calibration algorithms to estimate model parameters. The system was applied for a case study in Calabria (Italy) along the Salerno-Reggio Calabria highway, between Cosenza and Altilia municipality. The analysis provided that among all the optimized indices and all the three models, the optimization of the index distance to perfect classification in the receiver operating characteristic plane (D2PC) coupled with model M3 is the best modeling solution for our test case.

Predicting Landslide Displacements by Multi-objective Evolutionary Polynomial Regression

2014 ◽  
pp. 651-654 ◽  
Author(s):  
Angelo Doglioni ◽  
Giovanni B. Crosta ◽  
Paolo Frattini ◽  
Nicola L. Melidoro ◽  
Vincenzo Simeone
Keyword(s):  
Polynomial Regression ◽  
Multi Objective ◽  
Evolutionary Polynomial Regression

scholarly journals Prediction of side thermal buoyant discharge in the cross flow using multi-objective evolutionary polynomial regression (EPR-MOGA)

2019 ◽  
Vol 21 (6) ◽  
pp. 980-998
Author(s):  
Milad Khosravi ◽  
Mitra Javan
Keyword(s):  
Polynomial Regression ◽  
Velocity Ratio ◽  
Three Dimensional ◽  
Cross Flow ◽  
Temperature Profiles ◽  
Cfd Model ◽  
Multi Objective ◽  
Evolutionary Polynomial Regression ◽  
The Cross ◽  
Thermal Cross Section

Abstract The capability to predict the distribution of pollutants in water bodies is one of the most important issues in the design of jet outfalls. Three-dimensional computational fluid dynamics (CFD) model and multi-objective evolutionary polynomial regression (EPR-MOGA) are used and compared in modeling the temperature field in the side thermal buoyant discharge in the cross flow. The input variables used for training the EPR-MOGA models are spatial coordinates (x, y, z), jet to cross flow velocity ratio (R), depth of the channel (d), and the temperature excess (T0). A previous experimental study is used to verify and compare the performance of the EPR-MOGA and CFD models. The results show that the EPR-MOGA model predicts the thermal cross section of the flow and the spread of pollutants at the surface with a better accuracy than the CFD model. However, the CFD method performs significantly better than EPR-MOGA in predicting temperature profiles. The uncertainty analysis indicated that the EPR-MOGA model had lower mean prediction error and smaller uncertainty band than the CFD model. The relationships achieved by the EPR-MOGA model are very useful to predict temperature profiles, temperature half-thickness, and temperature spread on surface in practice.

scholarly journals Robustness Analysis of Model Parameters for Sediment Transport Equation Development

2019 ◽  
Vol 12 ◽  
pp. 1-17
Author(s):  
Nadiatul Adilah Ahmad Abdul Ghani ◽  
Junaidah Ariffin ◽  
Duratul Ain Tholibon
Keyword(s):  
Sediment Transport ◽  
Transport Equation ◽  
Polynomial Regression ◽  
Robustness Analysis ◽  
Shear Velocity ◽  
Model Parameters ◽  
Evolutionary Polynomial Regression ◽  
Sediment Transport Equation ◽  
Sediment Data ◽  
Influential Parameters

Robustness analysis of model parameters for sediment transport equation development is carried out using 256 hydraulics and sediment data from twelve Malaysian rivers. The model parameters used in the analyses include parameters in equations by Ackers-White, Brownlie, Engelund-Hansen, Graf, Molinas-Wu, Karim-Kennedy, Yang, Ariffin and Sinnakaudan. Seven parameters in five parameter classes were initially tested. Robustness of the model parameters was measured on the statistical relations through Evolutionary Polynomial Regression (EPR) technique and further examined using the discrepancy ratio of the predicted versus the measured values. Results from analyses suggest  (ratio of shear velocity to flow velocity) and  (ratio of hydraulic radius to mean sediment diameter) to be the most significant and influential parameters for the development of sediment transport equation.

Improved air valve design using evolutionary polynomial regression

2019 ◽  
Vol 19 (7) ◽  
pp. 2036-2043
Author(s):  
G. Balacco ◽  
D. Laucelli
Keyword(s):  
Polynomial Regression ◽  
Pipeline System ◽  
Heuristic Methods ◽  
Data Modelling ◽  
Valve Design ◽  
Technical Literature ◽  
Evolutionary Polynomial Regression ◽  
Physically Based ◽  
Air Valve ◽  
Modelling Approach

Abstract Air valves are usually sized by heuristic methods or, sometimes, even oversized. Although the technical literature has long focused on the correct sizing of air valves to reduce the overpressure generated by the filling of a pipe, the phenomenon is complex and does not seem to be representable by physically based equations in an easy way, to be of practical use for technicians and designers. In this paper, air valve design is approached through an alternative data-modelling approach, based on evolutionary polynomial regression, with the aim to provide symbolic formulas of variable complexity and accuracy, suitable for physical interpretation, and at the same time easy to be used and applied for design purposes. The present investigation suggests a design formula that, given the geometric parameters of the pipeline system where the air valve is installed, provides the maximum tolerable overpressure, thus allowing the optimal air valve orifice size to be identified.

scholarly journals Multi-objective evolutionary polynomial regression-based prediction of energy consumption probing

2017 ◽  
Vol 75 (12) ◽  
pp. 2791-2799 ◽  
Author(s):  
Hossein Bonakdari ◽  
Isa Ebtehaj ◽  
Azam Akhbari
Keyword(s):  
Energy Consumption ◽  
Polynomial Regression ◽  
Treatment Time ◽  
Synthetic Wastewater ◽  
Process Conditions ◽  
Multi Objective ◽  
Evolutionary Polynomial Regression ◽  
Sensitivity Analysis Method ◽  
Initial Ph ◽  
Input Variables

Electrocoagulation (EC) is employed to investigate the energy consumption (EnC) of synthetic wastewater. In order to find the best process conditions, the influence of various parameters including initial pH, initial dye concentration, applied voltage, initial electrolyte concentration, and treatment time are investigated in this study. EnC is considered the main criterion of process evaluation in investigating the effect of the independent variables on the EC process and determining the optimum condition. Evolutionary polynomial regression is combined with a multi-objective genetic algorithm (EPR-MOGA) to present a new, simple and accurate equation for estimating EnC to overcome existing method weaknesses. To survey the influence of the effective variables, six different input combinations are considered. According to the results, EPR-MOGA Model 1 is the most accurate compared to other models, as it has the lowest error indices in predicting EnC (MARE = 0.35, RMSE = 2.33, SI = 0.23 and R2 = 0.98). A comparison of EPR-MOGA with reduced quadratic multiple regression methods in terms of feasibility confirms that EPR-MOGA is an effective alternative method. Moreover, the partial derivative sensitivity analysis method is employed to analyze the EnC variation trend according to input variables.

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.

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