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 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 Improvement of SVR-Based Drought Forecasting Models using Wavelet Pre-Processing Technique

2018 ◽  
Vol 65 ◽  
pp. 07007 ◽  
Author(s):  
Kit Fai Fung ◽  
Yuk Feng Huang ◽  
Chai Hoon Koo
Keyword(s):  
Support Vector Regression ◽  
River Basin ◽  
Absolute Error ◽  
Processing Technique ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Drought Risk ◽  
Drought Forecasting ◽  
Drought Prediction ◽  
Langat River

Drought is a damaging natural hazard due to the lack of precipitation from the expected amount for a period of time. Mitigations are required to reduced its impact. Due to the difficulty in determining the onset and offset of droughts, accurate drought forecasting approaches are required for drought risk management. Given the growing use of machine learning in the field, Wavelet-Boosting Support Vector Regression (W-BS-SVR) was proposed for drought forecasting at Langat River Basin, Malaysia. Monthly rainfall, mean temperature and evapotranspiration for years 1976 - 2015 were used to compute Standardized Precipitation Evapotranspiration Index (SPEI) in this study, producing SPEI-1, SPEI-3 and SPEI-6. The 1-month lead time SPEIs forecasting capability of W-BS-SVR model was compared with the Support Vector Regression (SVR) and Boosting-Support Vector Regression (BS-SVR) models using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2) and Adjusted R2. The results demonstrated that W-BS-SVR provides higher accuracy for drought prediction in Langat River Basin.

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 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.

Visualization of High Speed Flow in a Pipe with Cavity

1997 ◽  
Vol 17 (Supplement2) ◽  
pp. 113-116
Author(s):  
Kenji HOSOI ◽  
Masaaki KAWAHASHI ◽  
Hiroyuki HIRAHARA ◽  
Kouju SHIOZAKI ◽  
Kenichirou SATOH
Keyword(s):  
High Speed ◽  
High Speed Flow ◽  
Speed Flow

scholarly journals An interpretable prediction method for university student academic crisis warning

2021 ◽  
Author(s):  
Zhai Mingyu ◽  
Wang Sutong ◽  
Wang Yanzhang ◽  
Wang Dujuan
Keyword(s):  
Academic Achievement ◽  
Visual Analysis ◽  
Prediction Method ◽  
Absolute Error ◽  
University Student ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Achievement Prediction ◽  
Academic Achievement Prediction ◽  
Education Administrators

AbstractData-driven techniques improve the quality of talent training comprehensively for university by discovering potential academic problems and proposing solutions. We propose an interpretable prediction method for university student academic crisis warning, which consists of K-prototype-based student portrait construction and Catboost–SHAP-based academic achievement prediction. The academic crisis warning experiment is carried out on desensitization multi-source student data of a university. The experimental results show that the proposed method has significant advantages over common machine learning algorithms. In terms of achievement prediction, mean square error (MSE) reaches 24.976, mean absolute error (MAE) reaches 3.551, coefficient of determination ($$R^{2}$$ R 2 ) reaches 80.3%. The student portrait and Catboost–SHAP method are used for visual analysis of the academic achievement factors, which provide intuitive decision support and guidance assistance for education administrators.

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