scholarly journals Developing A Mathematical Model for Planning Repetitive Construction Projects By Using Support Vector Machine Technique

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abbas M. Burhan ◽  
Kadhim Raheim Erzaij ◽  
Wadhah Amer Hatem
Keyword(s):  
Mathematical Model ◽  
Support Vector Machine ◽  
Construction Projects ◽  
Absolute Error ◽  
Support Vector ◽  
Planning And Scheduling ◽  
Factors Affecting ◽  
Project Management System ◽  
Predicted Values ◽  
The Mathematical Model

Abstract Each project management system aims to complete the project within its identified objectives: budget, time, and quality. It is achieving the project within the defined deadline that required careful scheduling, that be attained early. Due to the nature of unique repetitive construction projects, time contingency and project uncertainty are necessary for accurate scheduling. It should be integrated and flexible to accommodate the changes without adversely affecting the construction project’s total completion time. Repetitive planning and scheduling methods are more effective and essential. However, they need continuous development because of the evolution of execution methods, essentially based on the repetitive construction projects’ composition of identical production units. This study develops a mathematical model to forecast repetitive construction projects using the Support Vector Machine (SVM) technique. The software (WEKA 3.9.1©2016) has been used in the process of developing the mathematical model. The number of factors affecting the planning and scheduling of the repetitive projects has been identified through a questionnaire that analyzed its results using SPSS V22 software. Three accuracy measurements, correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), were used to check the mathematical model and to compare the actual values with predicted values. The results showed that the SVM technique was more precise than those calculated by the conventional methods and was found the best generalization with R 97 %, MAE 3.6 %, and RMSE 7 %.

scholarly journals Estimating daily reference evapotranspiration using hybrid gamma test-least square support vector machine, gamma test-ANN, and gamma test-ANFIS models in an arid area of Iran

2018 ◽  
Vol 11 (1) ◽  
pp. 217-240 ◽  
Author(s):  
Akram Seifi ◽  
Hossien Riahi
Keyword(s):  
Support Vector Machine ◽  
Absolute Error ◽  
Least Square ◽  
Support Vector ◽  
Empirical Equations ◽  
Gamma Test ◽  
Hybrid Approaches ◽  
Input Variables ◽  
Predicted Values ◽  
Better Than

Abstract In this study, a hybrid model of least square support vector machine-gamma test (LSSVM-GT) is proposed for estimating daily ETo under arid conditions of Zahedan station, Iran. Gamma test was used for selecting the best input vectors for models. The estimated ETo by LSSVM-GT model with different kernels of RBF, linear and polynomial, were compared with other hybrid approaches including ANN-GT, ANFIS-GT, and empirical equations. The gamma test revealed that climate variables of minimum and maximum air temperature and wind speed are the most important parameters. The LSSVM model performed better than the ANFIS and ANN models when similar meteorological input variables are used. Also, the performance of the three models of LSSVM, ANFIS, and ANN were better than the empirical equations such as Blaney–Criddle and Hargreaves–Samani. The RMSE, MAE, and R2 for the best input vector by LSSVM were 0.1 mm day−1, 0.13 mm day−1, and 0.99, respectively. The threshold of relative absolute error of 95% predicted values by LSSVM, ANN, and ANFIS models were about 8.4%, 9.4%, and 24%, respectively. Based on the comparison of the overall performances, the developed LSSVM-GT approach is greatly capable of providing favorable predictions with high precision in arid regions of Iran.

scholarly journals Research on Application of Regression Least Squares Support Vector Machine on Performance Prediction of Hydraulic Excavator

2014 ◽  
Vol 2014 ◽  
pp. 1-4 ◽  
Author(s):  
Zhan-bo Chen
Keyword(s):  
Mathematical Model ◽  
Support Vector Machine ◽  
Least Squares ◽  
Performance Prediction ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Hydraulic Excavator ◽  
Simulation Results ◽  
Research On Application ◽  
The Mathematical Model

In order to improve the performance prediction accuracy of hydraulic excavator, the regression least squares support vector machine is applied. First, the mathematical model of the regression least squares support vector machine is studied, and then the algorithm of the regression least squares support vector machine is designed. Finally, the performance prediction simulation of hydraulic excavator based on regression least squares support vector machine is carried out, and simulation results show that this method can predict the performance changing rules of hydraulic excavator correctly.

scholarly journals Effect of Dimensionality Reductions Technique in Modelling and Forecasting River Flow

2018 ◽  
Vol 7 (4.30) ◽  
pp. 573
Author(s):  
Shuhaida Ismail ◽  
Ani Shabri ◽  
Aida Mustapha ◽  
Siraj Mohammed Pandhiani
Keyword(s):  
Support Vector Machine ◽  
River Flow ◽  
Principal Component ◽  
Absolute Error ◽  
Least Square ◽  
Support Vector ◽  
Accurate Information ◽  
Statistical Measures ◽  
Future Value ◽  
Predicted Values

The ability of obtain accurate information on future river flow is a fundamental key for water resources planning, and management. Traditionally, single models have been introduced to predict the future value of river flow. This paper investigates the ability of Principal Component Analysis as dimensionality reduction technique and combined with single Support Vector Machine and Least Square Support Vector Machine, referred to as PCA-SVM and PCA-LSSVM. This study also presents comparison between the proposed models with single models of SVM and LSSVM. These models are ranked based on four statistical measures namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Correlation Coefficient ( ), and Correlation of Efficiency (CE). The results shows that PCA combined with LSSVM has better performance compared to other models. The best ranked models are then measured using Mean of Forecasting Error (MFE) to determine its forecast rate. PCA-LSSVM proven to be better model as it also indicates a small percentage of under-predicted values compared to the observed river flow values of 0.89% for Tualang river while over-predicted by 2. 08% for Bernam river. The study concludes by recommending the PCA as dimension reduction approach combined with LSSVM for river flow forecasting due to better prediction results and stability than those achieved from single models  

Simulation of oscillatory processes in the MVSTUDIUM package

2022 ◽  
Vol 14 (4) ◽  
pp. 139-148
Author(s):  
Aleksandr Poluektov ◽  
Konstantin Zolnikov ◽  
V. Antsiferova
Keyword(s):  
Mathematical Model ◽  
Friction Force ◽  
Computer Models ◽  
3D Models ◽  
Oscillatory Process ◽  
Suspension Point ◽  
Factors Affecting ◽  
2D And 3D ◽  
The Mathematical Model ◽  
Oscillatory Processes

The mathematical model and algorithms of oscillatory movements are considered. Various factors affecting the oscillatory process are considered. Oscillatory movements are constructed in the MVSTUDIUM modeling environment. The schemes of three computer models demonstrating oscillatory processes are determined: a model of a pendulum with a non-movable suspension point, a model of a pushing pendulum with friction force and a model of a breaking pendulum. Classes are being built to execute models with embedded properties, as well as with the ability to export the created classes to other models, and embed classes created by the program developer into the model. Creation of 2D and 3D models of oscillatory processes, an experiment behavior map and a virtual stand.

scholarly journals Analysis on Present Mathematical Model for Predicting the Crop Production

2020 ◽  
Vol 9 (12) ◽  
pp. 168-170
Keyword(s):  
Mathematical Model ◽  
Mathematical Models ◽  
Real World ◽  
Crop Production ◽  
Ghg Emissions ◽  
Data Set ◽  
Factors Affecting ◽  
The Government ◽  
The Mathematical Model ◽  
Government Website

India is a worldwide agriculture business powerhouse. Future of agriculture-based products depends on the crop production. A mathematical model might be characterized as a lot of equations that speak to the conduct of a framework. By using mathematical model in agriculture field, we can predict the production of crop in particular area. There are various factors affecting crops such as Rainfall, GHG Emissions, Temperature, Urbanization, climate, humidity etc. A mathematical model is a simplified representation of a real-world system. It forms the system using mathematical principles in the form of a condition or a set of conditions. Suppose we need to increase the crop production, at that time the mathematical model plays a major role and our work can be easier, more significant by using the mathematical model. Through the mathematical model we predict the crop production in upcoming years. .AI, ML, IOT play a major role to predict the future of agriculture, but without mathematical models it is not possible to predict crop production accurately. To solve the real-world agriculture problem, mathematical models play a major role for accurate results. Correlation Analysis, Multiple Regression analysis and fuzzy logic simulation standards have been utilized for building a grain production benefit depending model from crop production. Prediction of crop is beneficiary to the farmer to analyze the crop management. By using the present agriculture data set which is available on the government website, we can build a mathematical model.

Coke Rate Prediction Model Based on Genetic Algorithm Optimized Support Vector Machine

2015 ◽  
Vol 740 ◽  
pp. 600-603
Author(s):  
You Jun Yue ◽  
Yan Fei Hu ◽  
Hui Zhao ◽  
Hong Jun Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Absolute Error ◽  
Support Vector ◽  
Model Parameters ◽  
Coke Rate ◽  
Rate Model ◽  
Iron And Steel ◽  
Model Based ◽  
Blast Furnace Coke

The accurate prediction model’s establishing of the blast furnace coke rate is important for optimizing the integrated production indicators of iron and steel enterprise. For the problem of accuracy of the model of coke rate, This paper established blast coke rate modeling with support vector machine algorithm, the model parameters of support vector machine was optimized by genetic algorithm, then a coke rate model based on support vector machine with the best parameters was built. Simulation results showed that: the forecasting model’s outcome, average absolute error and the mean relative error, was small which is based on genetic algorithm optimized SVM. coke rate model based on Genetic algorithm optimized support vector machine has high degree of accuracy and a certain practicality.

Application of Support Vector Machine Modeling on Phase Distribution in the Riser of an LSCFB Reactor

2014 ◽  
Vol 12 (1) ◽  
pp. 123-134 ◽  
Author(s):  
Shaikh A. Razzak ◽  
Muhammad I. Hossain ◽  
Syed M. Rahman ◽  
Mohammad M. Hossain
Keyword(s):  
Support Vector Machine ◽  
Circulating Fluidized Bed ◽  
Phase Distribution ◽  
Solid Phase ◽  
Correlation Coefficients ◽  
Absolute Error ◽  
Pilot Scale ◽  
Support Vector ◽  
Cross Sectional ◽  
Machine Modeling

Abstract Support vector machine (SVM) modeling approach is applied to predict the solids holdups distribution of a liquid–solid circulating fluidized bed (LSCFB) riser. The SVM model is developed/trained using experimental data collected from a pilot-scale LSCFB reactor. Two different size glass bead particles (500 μm (GB-500) and 1,290 μm (GB-1290)) are used as solid phase, and water is used as liquid phase. The trained model successfully predicted the experimental solids holdups of the LSCFB riser under different operating parameters. It is observed that the model predicted cross-sectional average of solids holdups in the axial directions and radial flow structure are well agreement with the experimental values. The goodness of the model prediction is verified by using different statistical performance indicators. For the both sizes of particles, the mean absolute error is found to be less than 5%. The correlation coefficients (0.998 for GB-500 and 0.994 for GB-1290) also show favorable indications of the suitability of SVM approach in predicting the solids holdup of the LSCFB system.

Application of Independent Component Analysis in Short-Term Power Forecasting of Wind Farm

2011 ◽  
Vol 63-64 ◽  
pp. 124-128
Author(s):  
Guo Chu Chen ◽  
Peng Wang ◽  
Jin Shou Yu
Keyword(s):  
Support Vector Machine ◽  
Independent Component Analysis ◽  
Least Squares ◽  
Wind Farm ◽  
Absolute Error ◽  
Component Analysis ◽  
Independent Component ◽  
Support Vector ◽  
Number Of Factors ◽  
Power Forecasting

For the difficult problems of measuring and forecasting values interfered by a number of factors, this paper proposed a method of power forecasting based on independent component analysis and least squares support vector machine, and results are modified using the regression. Each independent component from source signals is predicted using least squares support vector machine, the final prediction results obtained by modifying the preliminary predicting power according to the relationship between wind speed and its power. Using the data from a wind farm on the Northeast China wind farm, the simulation results show that this method has higher prediction accuracy, and the mean absolute error from 9.25% down to 5.48%, compared with the simple least squares support vector machine models.

scholarly journals Organization of going in for physical training in sanatorium-preventive clinic

2001 ◽  
Vol 82 (4) ◽  
pp. 247-250
Author(s):  
A. A. Davliev
Keyword(s):  
Mathematical Model ◽  
Motor Activity ◽  
Physical Training ◽  
Physical Development ◽  
Physical Work ◽  
State Of Health ◽  
Factors Affecting ◽  
Motion Activity ◽  
Physical Work Ability ◽  
The Mathematical Model

The factors determining the motor activity are revealed and the adequate system of measures on drawing workers of industrial works in going in for physical training in the sanatorium-preventive clinic is developed. Two basic groups of methodical approaches: general clinic therapeutic examination to estimate the state of health and generally accepted in sport medicine the methods to estimate physical development, physical work ability and states of motion apparatus were used. The mathematical model of motion activity and health level in multidimensional relations with factors affecting them was constructed.

scholarly journals Support Vector Machine to Predict Electricity Consumption in the Energy Management Laboratory

2021 ◽  
Vol 5 (3) ◽  
pp. 466-473
Author(s):  
Azam Zamhuri Fuadi ◽  
Irsyad Nashirul Haq ◽  
Edi Leksono
Keyword(s):  
Support Vector Machine ◽  
Energy Management ◽  
Mean Squared Error ◽  
Measurement Data ◽  
Electricity Consumption ◽  
Absolute Error ◽  
Support Vector ◽  
Mean Error ◽  
Squared Error ◽  
Electrical Loads

Predicted electricity consumption is needed to perform energy management. Electricity consumption prediction is also very important in the development of intelligent power grids and advanced electrification network information. we implement a Support Vector Machine (SVM) to predict electrical loads and results compared to measurable electrical loads. Laboratory electrical loads have their own characteristics when compared to residential, commercial, or industrial, we use electrical load data in energy management laboratories to be used to be predicted. C and Gamma as searchable parameters use GridSearchCV to get optimal SVM input parameters. Our prediction data is compared to measurement data and is searched for accuracy based on RMSE (Root Square Mean Error), MAE (Mean Absolute Error) and MSE (Mean Squared Error) values. Based on this we get the optimal parameter values C 1e6 and Gamma 2.97e-07, with the result RSME (Root Square Mean Error) ; 0.37, MAE (meaning absolute error); 0.21 and MSE (Mean Squared Error); 0.14.

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