scholarly journals Prediction of the Void Ratio Parameter in Mineral Tailings Using Gene Expression Programming

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ali Akbar Heshmati R. ◽  
Hossein Salehzadeh ◽  
Mehdi Shahidi
Keyword(s):  
Gene Expression ◽  
Void Ratio ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Physical Parameters ◽  
Experimental Database ◽  
Initial Void Ratio ◽  
Different Sources ◽  
Initial Prediction

Mineral tailing deposits are one of the most important issues in the field of geotechnical engineering. The void ratio of mineral tailings is an essential parameter for investigating the geotechnical behavior of tailings. However, there has not yet been a comprehensive empirical formulation for initial prediction of the void ratio of mineral tailings. In this study, the void ratio of various types of mineral waste is estimated by using gene expression programming (GEP). Therefore, taking into consideration the effective physical parameters that affect the estimation of this parameter, eight different models are presented. A reliable experimental database collected from different sources in the literature was applied to develop the GEP models. The performance of the developed GEP models was measured based on coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). According to the results, the model with effective stress σ ′ , initial void ratio (e0), and parameters of R2 = 0.92, MAE = 0.109, and RMSE = 0.180 performed the best. Finally, a new empirical formulation for the initial prediction of the void ratio parameter is proposed based on the aforementioned analyses.

scholarly journals Estimation of aerator air demand by an embedded multi-gene genetic programming

2021 ◽  
Author(s):  
Shicheng Li ◽  
James Yang ◽  
Wei Liu
Keyword(s):  
Genetic Programming ◽  
High Speed ◽  
Pareto Front ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Processing Technique ◽  
Coefficient Of Determination ◽  
Speed Flow ◽  
Processing Measure ◽  
Programming Solution

Abstract A spillway discharging a high-speed flow is susceptible to cavitation damages. As a countermeasure, an aerator is often used to artificially entrain air into the flow. Its air demand is of relevance to cavitation reduction and requires accurate estimations. The main contribution of this study is to establish an embedded multi-gene genetic programming (EMGGP) model for improved prediction of air demand. It is an MGGP-based framework coupled with the gene expression programming acting as a pre-processing technique for input determination and the Pareto front serving as a post-processing measure for solution optimization. Experimental data from a spillway aerator are used to develop and validate the proposed technique. Its performance is statistically evaluated by the coefficient of determination (CD), Nash–Sutcliffe coefficient (NSC), root-mean-square error (RMSE) and mean absolute error (MAE). Satisfactory predictions are yielded with CD = 0.95, NSC = 0.94, RMSE = 0.17 m3/s and MAE = 0.12 m3/s. Compared with the best empirical formula, the EMGGP approach enhances the fitness (CD and NSC) by 23% and reduces the errors (RMSE and MAE) by 48%. It also exhibits higher prediction accuracy and a simpler expressional form than the genetic programming solution. This study provides a procedure for the establishment of parameter relationships for similar hydraulic issues.

scholarly journals Prediction of the depth of local scouring at a bridge pier using a gene expression programming method

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Waqed H. Hassan ◽  
Halah K. Jalal
Keyword(s):  
Gene Expression ◽  
Mean Squared Error ◽  
Human Life ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Width Ratio ◽  
Statistical Parameters ◽  
Data Set ◽  
Total Data ◽  
Local Depth

AbstractLocal scouring around the piers of a bridge is the one of the major reasons for bridge failure, potentially resulting in heavy losses in terms of both the economy and human life. Prediction of accurate depth of local scouring is a difficult task due to the many factors that contribute to this process, however. The main aim of this study is thus to offer a new formula for the prediction the local depth of scouring around the pier of a bridge using a modern fine computing modelling technique known as gene expression programming (GEP), with data obtained from numerical simulations used to compare GEP performance with that of a standard non-linear regression (NLR) model. The best technique for prediction of the local scouring depth is then determined based on three statistical parameters: the determination coefficient (R2), mean absolute error (MAE), and root mean squared error (RMSE). A total data set of 243 measurements, obtained by numerical simulation in Flow-3D, for intensity of flow, ratio of pier width, ratio of flow depth, pier Froude number, and pier shape factor is divided into training and validation (testing) datasets to achieve this. The results suggest that the formula from the GEP model provides better performance for predicting the local depth of scouring as compared with conventional regression with the NLR model, with R2 = 0.901, MAE = 0.111, and RMSE = 0.142. The sensitivity analysis results further suggest that the ratio of the depth of flow has the greatest impact on the prediction of local scour depth as compared to the other input parameters. The formula obtained from the GEP model gives the best predictor of depth of scouring, and, in addition, GEP offers the special feature of providing both explicit and compressed arithmetical terms to allow calculation of such depth of scouring.

scholarly journals Wet-Bulb Temperature from Relative Humidity and Air Temperature

2011 ◽  
Vol 50 (11) ◽  
pp. 2267-2269 ◽  
Author(s):  
Roland Stull
Keyword(s):  
Gene Expression ◽  
Relative Humidity ◽  
Sea Level ◽  
Air Temperature ◽  
Mean Absolute Error ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Sea Level Pressure ◽  
Air Temperatures ◽  
Low Humidity

AbstractAn equation is presented for wet-bulb temperature as a function of air temperature and relative humidity at standard sea level pressure. It was found as an empirical fit using gene-expression programming. This equation is valid for relative humidities between 5% and 99% and for air temperatures between −20° and 50°C, except for situations having both low humidity and cold temperature. Over the valid range, errors in wet-bulb temperature range from −1° to +0.65°C, with mean absolute error of less than 0.3°C.

scholarly journals Rainfall Runoff Modeling using Gene Expression Programming and Artificial Neural Network

2020 ◽  
Vol 9 (3) ◽  
pp. 978-983
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Water Resource Management ◽  
Meteorological Data ◽  
Gene Expression Programming ◽  
Coefficient Of Determination ◽  
Rainfall Runoff ◽  
Statistical Parameters ◽  
Artificial Neural

In water resource management and planning the Rainfall-Runoff models play a crucial role and depends mainly on the data available for planning activities. The rainfall-runoff relationship comes under the nonlinear and complex hydrological Event. In the present study two data driven modeling approaches, Artificial Neural Network (ANN) and Gene Expression Programming (GEP) has been used for modeling of rainfall-runoff process as these methods does not consider the physical nature of the process, which is complex to understand. GEP and ANN are used to model rainfall-runoff relationship for Dindori catchment in upper Narmada River Basin. Daily hydro-meteorological data of Dindori gauging station and precipitation of the catchment for a period of eighteen years were used as input in the model design. Various combinations of input variables for training and testing of models were selected based on statistical parameters. The performance of model was evaluated in term of the root mean square error (RMSE), coefficient of determination, RMSE to standard deviation ratio (RSR) and Nash Sutcliffe Efficiency. The results obtained after applying the two techniques were compared. Which indicates that GEP performed better in all performance evaluation parameters (R2 is 0.92) then ANN (R2 0.90) and is able to give mathematical relationship for rainfallrunoff modeling.

scholarly journals Comparison of neural networks and neuro-fuzzy computing techniques for prediction of peak breach outflow

2016 ◽  
Vol 18 (4) ◽  
pp. 724-740 ◽  
Author(s):  
Hasan G. Elmazoghi ◽  
Vail Karakale (Waiel Mowrtage) ◽  
Lubna S. Bentaher
Keyword(s):  
Fuzzy Inference ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Data Set ◽  
Inference System ◽  
Embankment Dams ◽  
Small Dams ◽  
Neuro Fuzzy

Accurate prediction of peak outflows from breached embankment dams is a key parameter in dam risk assessment. In this study, efficient models were developed to predict peak breach outflows utilizing artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Historical data from 93 embankment dam failures were used to train and evaluate the applicability of these models. Two scenarios were applied with each model by either considering the whole data set without classification or classifying the set into small dams (48 dams) and large dams (45 dams). In this way, nine models were developed and their results were compared to each other and to the results of the best available regression equations and recent gene expression programming. Among the different models, the ANFIS model of the first scenario exhibited better performance based on its higher efficiency (E = 0.98), higher coefficient of determination (R2 = 0.98) and lower mean absolute error (MAE = 840.9). Moreover, models based on classified data enhanced the prediction of peak outflows particularly for small dams. Finally, this study indicated the potential of the developed ANFIS and ANN models to be used as predictive tools of peak outflow rates of embankment dams.

scholarly journals Prediction of the Compressive Strength of Concrete Admixed with Metakaolin Using Gene Expression Programming

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Oluwatobi O. Akin ◽  
Amana Ocholi ◽  
Olugbenga S. Abejide ◽  
Johnson A. Obari
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Stepwise Regression ◽  
Gene Expression Programming ◽  
Good Alternative ◽  
Coefficient Of Determination ◽  
Concrete Compressive Strength ◽  
Compressive Strength Of Concrete ◽  
Input Variables ◽  
The Relationship

One of the problems of optimization of concrete is to formulate a mathematical equation that shows the relationship between the various constituents of concrete and its properties. In this work, modelling of the compressive strength of concrete admixed with metakaolin was carried out using the Gene Expression Programming (GEP) algorithm. The dataset from laboratory experimentation was used for the analysis. The mixture proportions were made of three different water/binder ratios (0.4, 0.5, and 0.6), and the grades of concrete produced were grade M15 and M20. The compressive strength of the concrete was determined after 28 days of curing. The parameters used in the GEP algorithm are the input variables which include cement content, water, metakaolin content, and fine and coarse aggregate, while the response was designated as the compressive strength. The model was trained and tested using the parameters. The R-square value from the GEP algorithm was compared with the use of conventional stepwise regression analysis. With a coefficient of determination (R-square value) of 0.95, the GEP algorithm has shown to be a good alternative for modelling concrete compressive strength.

scholarly journals Improving the performance of rainfall-runoff models using the gene expression programming approach

2021 ◽  
Author(s):  
Hassan Esmaeili-Gisavandani ◽  
Morteza Lotfirad ◽  
Masoud Soori Damirchi Sofla ◽  
Afshin Ashrafzadeh
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Swat Model ◽  
Combination Method ◽  
Balance Model ◽  
Coefficient Of Determination ◽  
Programming Approach ◽  
Model Parameters ◽  
Efficiency Coefficient ◽  
Australian Water

Abstract In this study, five hydrological models, including the soil and water assessment tool (SWAT), identification of unit hydrograph and component flows from rainfall, evapotranspiration, and streamflow (IHACRES), Hydrologiska Byråns Vattenbalansavdelning (HBV), Australian water balance model (AWBM), and Soil Moisture Accounting (SMA), were used to simulate the flow of the Hablehroud River, north-central Iran. All the models were validated based on the root mean square error (RMSE), coefficient of determination (R2), Nash-Sutcliffe model efficiency coefficient (NS), and Kling-Gupta efficiency (KGE). It was found that SWAT, IHACRES, and HBV had satisfactory results in the calibration phase. However, only the SWAT model had good performance in the validation phase and outperformed the other models. It was also observed that peak flows were generally underestimated by the models. The sensitivity analysis results of the model parameters were also evaluated. A hybrid model was developed using gene expression programming (GEP). According to the error measures, the ensemble model had the best performance in both calibration (NS = 0.79) and validation (NS = 0.56). The GEP combination method can combine model outputs from less accurate individual models and produce a superior river flow estimate.

scholarly journals Modelling Reservoir Turbidity Using Landsat 8 Satellite Imagery by Gene Expression Programming

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1479 ◽  
Author(s):  
Liu ◽  
Wang
Keyword(s):  
Gene Expression ◽  
Performance Evaluation ◽  
Satellite Imagery ◽  
Mean Squared Error ◽  
Accuracy Assessment ◽  
Gene Expression Programming ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Model Performance Evaluation

This study aimed to develop a reliable turbidity model to assess reservoir turbidity based on Landsat-8 satellite imagery. Models were established by multiple linear regression (MLR) and gene-expression programming (GEP) algorithms. Totally 55 and 18 measured turbidity data from Tseng-Wen and Nan-Hwa reservoir paired and screened with satellite imagery. Finally, MLR and GEP were applied to simulated 13 turbid water data for critical turbidity assessment. The coefficient of determination (R2), root mean squared error (RMSE), and relative RMSE (R-RMSE) calculated for model performance evaluation. The result show that, in model development, MLR and GEP shows a similar consequent. However, in model testing, the R2, RMSE, and R-RMSE of MLR and GEP are 0.7277 and 0.8278, 0.7248 NTU and 0.5815 NTU, 22.26% and 17.86%, respectively. Accuracy assessment result shows that GEP is more reasonable than MLR, even in critical turbidity situation, GEP is more convincible. In the model performance evaluation, MLR and GEP are normal and good level, in critical turbidity condition, GEP even belongs to outstanding level. These results exhibit GEP denotes rationality and with relatively good applicability for turbidity simulation. From this study, one can conclude that GEP is suitable for turbidity modeling and is accurate enough for reservoir turbidity estimation.

scholarly journals Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model

2019 ◽  
Vol 4 (2) ◽  
pp. 26 ◽  
Author(s):  
Danial Mohammadzadeh S. ◽  
Seyed-Farzan Kazemi ◽  
Amir Mosavi ◽  
Ehsan Nasseralshariati ◽  
Joseph H. M. Tah
Keyword(s):  
Gene Expression ◽  
Mean Squared Error ◽  
Programming Model ◽  
Gene Expression Programming ◽  
Liquid Limit ◽  
Coefficient Of Determination ◽  
Average Error ◽  
Soil Parameters ◽  
Compression Index ◽  
Fine Grained

In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R2), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R2, RMSE, and MAE compared to the other models.

scholarly journals Comparison of SVM, ANFIS and GEP in modeling monthly potential evapotranspiration in an arid region (Case study: Sistan and Baluchestan Province, Iran)

2018 ◽  
Vol 19 (2) ◽  
pp. 392-403 ◽  
Author(s):  
Omolbani Mohammadrezapour ◽  
Jamshid Piri ◽  
Ozgur Kisi
Keyword(s):  
Fuzzy Inference ◽  
Climatic Factors ◽  
Potential Evapotranspiration ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Heuristic Methods ◽  
Inference System

Abstract Evapotranspiration is an important component in planning and management of water resources. It depends on climatic factors and the influence of these factors on each other makes evapotranspiration estimation difficult. This study attempts to explore the possibility of predicting this important component using three different heuristic methods: support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP). In this regard, according to the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith equation, the monthly potential evapotranspiration in four synoptic stations (Zahedan, Zabol, Iranshahr, and Chabahar) was calculated using monthly weather data. The weather data were then used as inputs to the SVM, ANFIS and GEP models to estimate potential evapotranspiration. Five different input combinations were tried in the applications. The results of SVM, ANFIS and GEP models were compared based on the coefficient of determination (R2), mean absolute error and root mean square error. Findings showed that the SVM model, whose inputs are average air temperature, relative humidity, wind speed, and sunny hours of the current and one previous month, performed better than the other models for the Zahedan, Zabol, Iranshahr, and Chabahar stations. Comparison of the three heuristic methods indicated that in all stations, the SVM, GEP and ANFIS models took first, second, and third place in estimation of the monthly potential evapotranspiration, respectively.

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