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 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 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 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.

Multicriteria analysis and genetic algorithms for mass appraisals in the Italian property market

2018 ◽  
Vol 11 (2) ◽  
pp. 229-262 ◽  
Author(s):  
Pierluigi Morano ◽  
Francesco Tajani ◽  
Marco Locurcio
Keyword(s):  
Genetic Algorithms ◽  
Real Estate ◽  
Polynomial Regression ◽  
Real Estate Market ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
Multi Objective ◽  
Evolutionary Polynomial Regression ◽  
Residential Properties

Purpose This paper aims to test and compare two innovative methodologies (utility additive and evolutionary polynomial regression) for mass appraisal of residential properties. The aim is to deepen their characteristics, by exploring the potentialities and the operating limits. Design/methodology/approach With reference to the same case studies, concerning samples of residential properties recently sold in three Italian cities, the two procedures are tested and the results are compared. The first method is the utility additive, which interprets the process of the property price formation as a multi-criteria selection of multi-objective typology, where the selection criteria are the property characteristics that are decisive in the real estate market; the second method is a hybrid data-driven technique, called evolutionary polynomial regression, that uses multi-objective genetic algorithms to search those models expressions that simultaneously maximize accuracy of data and parsimony of mathematical functions. Findings The outputs obtained from the experimentation highlight the potentialities and the limits of the two methodologies, as well as the possibility of jointly applying them to interpret and predict the real estate phenomena in a more realistic representation. Originality value In all countries, mass appraisal techniques have become strategic for the definition of management and enhancement policies of public and private property assets, in the case of investments of technical and economic refunctionalization (energy, environment, etc.), and for the alienation of buildings no longer suitable for public needs (military barracks, hospitals, areas in disuse, etc.). In this context, the use of mass appraisal techniques for residential properties assumes a leading role for sector operators (buyers, sellers, institutions, insurance companies, banks, real estate funds, etc.). Therefore, the results of the applications outline the potentialities of the two methodologies implemented and the opportunity of further insights of the topics that have been dealt with in this research.

scholarly journals Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

2017 ◽  
Vol 2017 ◽  
pp. 1-23 ◽  
Author(s):  
Ali Ghorbani ◽  
Mostafa Firouzi Niavol
Keyword(s):  
Neural Networks ◽  
Finite Difference ◽  
Polynomial Regression ◽  
Three Dimensional ◽  
Dynamic Loads ◽  
Prototype Model ◽  
Evolutionary Polynomial Regression ◽  
Dimensional Finite Element ◽  
Heavy Loads ◽  
Three Dimensional Finite Element

Coupled Piled Raft Foundations (CPRFs) are broadly applied to share heavy loads of superstructures between piles and rafts and reduce total and differential settlements. Settlements induced by static/coupled static-dynamic loads are one of the main concerns of engineers in designing CPRFs. Evaluation of induced settlements of CPRFs has been commonly carried out using three-dimensional finite element/finite difference modeling or through expensive real-scale/prototype model tests. Since the analyses, especially in the case of coupled static-dynamic loads, are not simply conducted, this paper presents two practical methods to gain the values of settlement. First, different nonlinear finite difference models under different static and coupled static-dynamic loads are developed to calculate exerted settlements. Analyses are performed with respect to different axial loads and pile’s configurations, numbers, lengths, diameters, and spacing for both loading cases. Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. Also, using a sensitivity analysis based on Cosine Amplitude Method, axial load is introduced as the most influential parameter, while the ratio l/d is reported as the least effective parameter on the settlements of CPRFs.

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