scholarly journals Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1231 ◽  
Author(s):  
Abobakr Saeed Abobakr Yahya ◽  
Ali Najah Ahmed ◽  
Faridah Binti Othman ◽  
Rusul Khaleel Ibrahim ◽  
Haitham Abdulmohsin Afan ◽  
...  
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Oxygen Demand ◽  
Quality Analysis ◽  
Support Vector ◽  
Prediction Errors ◽  
Ungauged Catchments ◽  
Machine Model ◽  
River Catchment ◽  
Proposed Model

Water quality analysis is a crucial step in water resources management and needs to be addressed urgently to control any pollution that may adversely affect the ecosystem and to ensure the environmental standards are being met. Thus, this work is an attempt to develop an efficient model using support vector machine (SVM) to predict the water quality of Langat River Basin through the analysis of the data of six parameters of dual reservoirs that are located in the catchment. The proposed model could be considered as an effective tool for identifying the water quality status for the river catchment area. In addition, the major advantage of the proposed model is that it could be useful for ungauged catchments or those lacking enough numbers of monitoring stations for water quality parameters. These parameters, namely pH, Suspended Solids (SS), Dissolved Oxygen (DO), Ammonia Nitrogen (AN), Chemical Oxygen Demand (COD), and Biochemical Oxygen Demand (BOD) were provided by the Malaysian Department of Environment (DOE). The differences between dual scenarios 1 and 2 depend on the information from prior stations to forecast DO levels for succeeding sites (Scenario 2). This scheme has the capacity to simulate water-quality accurately, with small prediction errors. The resulting correlation coefficient has maximum values of 0.998 and 0.979 after the application of Scenario 1. The approach with Type 1 SVM regression along with 10-fold cross-validation methods worked to generate precise results. The MSE value was found to be between 0.004 and 0.681, with Scenario 1 showing a better outcome.

Estimation of CO2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model

2018 ◽  
Vol 141 (4) ◽  
Author(s):  
Qihong Feng ◽  
Ronghao Cui ◽  
Sen Wang ◽  
Jin Zhang ◽  
Zhe Jiang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Diffusion Coefficient ◽  
Co2 Storage ◽  
Co2 Injection ◽  
Support Vector ◽  
Hybrid Technique ◽  
Machine Model ◽  
Proposed Model ◽  
Wide Range

Diffusion coefficient of carbon dioxide (CO2), a significant parameter describing the mass transfer process, exerts a profound influence on the safety of CO2 storage in depleted reservoirs, saline aquifers, and marine ecosystems. However, experimental determination of diffusion coefficient in CO2-brine system is time-consuming and complex because the procedure requires sophisticated laboratory equipment and reasonable interpretation methods. To facilitate the acquisition of more accurate values, an intelligent model, termed MKSVM-GA, is developed using a hybrid technique of support vector machine (SVM), mixed kernels (MK), and genetic algorithm (GA). Confirmed by the statistical evaluation indicators, our proposed model exhibits excellent performance with high accuracy and strong robustness in a wide range of temperatures (273–473.15 K), pressures (0.1–49.3 MPa), and viscosities (0.139–1.950 mPa·s). Our results show that the proposed model is more applicable than the artificial neural network (ANN) model at this sample size, which is superior to four commonly used traditional empirical correlations. The technique presented in this study can provide a fast and precise prediction of CO2 diffusivity in brine at reservoir conditions for the engineering design and the technical risk assessment during the process of CO2 injection.

scholarly journals Support vector machine for the prediction of heating energy use

2018 ◽  
Vol 22 (Suppl. 4) ◽  
pp. 1171-1181 ◽  
Author(s):  
Aleksandra Sretenovic ◽  
Radisa Jovanovic ◽  
Vojislav Novakovic ◽  
Natasa Nord ◽  
Branislav Zivkovic
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Energy Use ◽  
Support Vector Machine Model ◽  
District Heating ◽  
Support Vector ◽  
University Campus ◽  
Machine Model ◽  
Proposed Model ◽  
The Individual

Prediction of a building energy use for heating is very important for adequate energy planning. In this paper the daily district heating use of one university campus was predicted using the support vector machine model. Support vector machine is the artificial intelligence method that has recently proved that it can achieve comparable, or even better prediction results than the much more used artificial neural networks. The proposed model was trained and tested on the real, measured data. The model accuracy was compared with the results of the previously published models (various neural networks and their ensembles) on the same database. The results showed that the support vector machine model can achieve better results than the individual neural networks, but also better than the conventional and multistage ensembles. It is expected that this theoretically well-known methodology finds wider application, especially in prediction tasks.

scholarly journals Distributed Systematic Grid-Connected Inverter Using IGBT Junction Temperature Predictive Control Method: An Optimization Approach

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 825
Author(s):  
Zhengping Wang ◽  
Guoyi Li ◽  
Ming-Lang Tseng ◽  
Wai-Peng Wong ◽  
Boying Liu
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Learning Ability ◽  
Junction Temperature ◽  
Support Vector ◽  
Machine Model ◽  
Swarm Optimization ◽  
Proposed Model ◽  
Grid Connected Inverter ◽  
Chicken Swarm Optimization

Distributed systematic grid-connected inverter practice needs to improve insulated gate bipolar transistor (IGBT) stability to ensure the safe operation. This study is to ensure the safety and reliability operation of the IGBT module in symmetry to meet the reliable and stable distributed systematic grid-connected inverter practice and the junction temperature is a parameter to assess its operating state. It is difficult to accurately acquire the IGBT junction temperature to be solved by a single method of combining the test and the modeling. The saturation voltage drop or collector current and module junction temperature data under different power cycles are measured by the power cycle test and the single pulse test. The improved chicken swarm optimization increases the chickens diversity and self-learning ability. The prediction model of the improved chicken swarm optimization-support vector machine is proposed to forecast the module junction temperature. The result showed to compare with the particle swarm optimization-support vector machine model and chicken swarm optimization-support vector machine model and showed the coincidence degree between the proposed model prediction value and the true value is higher. The mean absolute error ratio indicates the proposed model has a smaller error and a better prediction performance. The proposed model has a positive impact on improving the distributed systematic grid-connected inverter industrial development and promotes the new energy usage.

scholarly journals Fuel Consumption Using OBD-II and Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Tamer Abukhalil ◽  
Harbi AlMahafzah ◽  
Malek Alksasbeh ◽  
Bassam A. Y. Alqaralleh
Keyword(s):  
Support Vector Machine ◽  
Fuel Consumption ◽  
Support Vector ◽  
Machine Model ◽  
Proposed Model ◽  
Relationship Model ◽  
Multiple Vehicles ◽  
Test Route ◽  
Vehicle Information ◽  
The Relationship

This paper presents a method to estimate gasoline fuel consumption using the onboard vehicle information system OBD-II (Onboard Diagnoses-II). Multiple vehicles were used on a test route so that their consumption can be compared. The relationships between fuel consumption and both of the engine speed are measured in RPM (revolutions per minute), and the throttle position sensor (TPS). The relationships are expressed as polynomial equations. The method which is composed of an SVM (support vector machine) classifier combined with Lagrange interpolation, is used to define the relationship between the two engine parameters and the overall fuel consumption. The relationship model is plotted using a surface fitting tool. In the experimental section, the proposed method is tested using the vehicles on a major highway between two cities in Jordan. The proposed model gets its sample data from the engine’s RPM, TPS, and fuel consumption. The method successfully has given precise fuel consumption with square root mean difference of 2.43, and the figures are compared with the values calculated by the conventional method.

scholarly journals PREDICTION OF GOVERNMENT-OWNED BUILDING ENERGY CONSUMPTION BASED ON AN RRELIEFF AND SUPPORT VECTOR MACHINE MODEL

2015 ◽  
Vol 21 (6) ◽  
pp. 748-760 ◽  
Author(s):  
Hyojoo Son ◽  
Changmin Kim ◽  
Changwan Kim ◽  
Youngcheol Kang
Keyword(s):  
Support Vector Machine ◽  
Energy Consumption ◽  
Environmental Impact ◽  
Building Energy ◽  
Support Vector ◽  
Government Agencies ◽  
Design Phase ◽  
Building Energy Consumption ◽  
Machine Model ◽  
Proposed Model

Accurate prediction of the energy consumption of government-owned buildings in the design phase is vital for government agencies, as it enables formulation of the early phases of development of such buildings with a view to reducing their environmental impact. The aim of this study was to identify the variables that are associated with energy consumption in government-owned buildings and to propose a predictive model based on those variables. The proposed approach selects relevant variables using the RReliefF variable selection algorithm. The support vector machine (SVM) method is used to develop a model of energy consumption based on the identified variables. The proposed approach was analyzed and validated on data for 175 government-owned buildings derived from the 2003 Commercial Building Energy Consumption Survey (CBECS) database. The experimental results revealed that the proposed model is able to predict the energy consumption of government-owned buildings in the design phase with a reasonable level of accuracy. The proposed model could be beneficial in guiding government agencies in developing early strategies and proactively reducing the environmental impact of a building, thereby achieving a high degree of sustainability of buildings constructed for government agencies.

scholarly journals A Hybrid Global Local Adaptive Particle Swarm Optimization-Based Support Vector Machine Model for Human Facial Authentication

2020 ◽  
Vol 55 (1) ◽  
Author(s):  
Salam Allawi Hussein ◽  
Alyaa Abduljawad Mahmood ◽  
Mohammed Iqbal Dohan
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Support Vector Machine Model ◽  
Particle Swarm ◽  
Support Vector ◽  
Machine Model ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Inertia Weight ◽  
Proposed Model

A new facial authentication model called global local adaptive particle swarm optimization-based support vector machine, was proposed in this paper. The proposed model aimed to solve the problem of finding the preeminent parameters of support vector machine in order to come out with a powerful human facial authentication technique. The conventional particle swarm optimization algorithm was utilized with support vector machine to explore the preeminent parameters of support vector machine. However, the particle swarm optimization support vector machine model has some limitations in selecting the velocity coefficient and inertia weight. One of the best approaches, which is used to solve the velocity coefficient problem, is adaptive acceleration particle swarm optimization. Also, the global-local best inertia weight is used efficiently for selecting the inertia weight. Therefore, the global local adaptive particle swarm optimization-based support vector machine model was proposed based on combining adaptive acceleration particle swarm optimization, global-local best inertia weight, and support vector machine. The proposed model used the principal component analysis approach for feature extraction, as well as global local adaptive particle swarm optimization for finding the preeminent parameters of support vector machine. In the experiments, two datasets (YALEB and CASIAV5) were used, and the suggested model was compared with particle swarm optimization support vector machine and adaptive acceleration particle swarm optimization support vector machine methods. The comparison was via accuracy, computational time, and optimal parameters of support vector machine. Our model can be used for security applications and apply for human facial authentication.

Research on Evaluation of the City Environmental Pollution Based on Support Vector Machine

2014 ◽  
Vol 651-653 ◽  
pp. 1460-1463
Author(s):  
Li Na Zhang ◽  
Bo Yang
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Noise Pollution ◽  
Support Vector ◽  
Machine Model ◽  
Research On Evaluation ◽  
Environment Quality ◽  
Quality Water ◽  
The Index System ◽  
The City

With the industrialization of society progress, human discharged a variety of pollutants into the nature, these pollutants have been excess of the self-purification capacity of the natural environment. City air quality, water quality, noise pollution and people's daily life are closely linked. Various types of pollution make the city haze frequency, declining water quality, noise enhanced, so that people living in city pay an attention to environment quality. Based on the statistical data of 2013 Chinese statistical yearbook, Corresponding relationship is established the index system of environmental quality and various types of pollution factors in this paper, according to the index of pollutants using the support vector machine model, to evaluate the environment quality of main city in China, these data can provide a basis of decision-making for managers.

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

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