Estimation Of Perceptible Water Vapor Of Atmosphere Using Artificial Neural Network, Support Vector Machine And Multiple Linear Regression Algorithm And Their Comparative Study

被引：0
|
作者：
Shastri, Niket [1 ]
Pathak, Kamlesh [2 ]
机构：
[1] Sarvajnik Coll Engn & Technol, Dept Phys, Surat, Gujarat, India
[2] Sardar Vallabhbhai Inst Technol, Dept Phys, Surat, Gujarat, India
来源：
2ND INTERNATIONAL CONFERENCE ON CONDENSED MATTER AND APPLIED PHYSICS (ICC-2017) | 2018年 / 1953卷
关键词：
D O I：
10.1063/1.5033289
中图分类号：
O59 [应用物理学];
学科分类号：
摘要：
The water vapor content in atmosphere plays very important role in climate. In this paper the application of GPS signal in meteorology is discussed, which is useful technique that is used to estimate the perceptible water vapor of atmosphere. In this paper various algorithms like artificial neural network, support vector machine and multiple linear regression are use to predict perceptible water vapor. The comparative studies in terms of root mean square error and mean absolute errors are also carried out for all the algorithms.
引用
收藏
页数：4
相关论文
共 50 条
  • [21] APPROXIMATING SWAT MODEL USING ARTIFICIAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE
    Zhang, Xuesong
    Srinivasan, Raghavan
    Van Liew, Michael
    JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, 2009, 45 (02): : 460 - 474
  • [22] Automated plant identification using artificial neural network and support vector machine
    Jye, Kho Soon
    Manickam, Sugumaran
    Malek, Sorayya
    Mosleh, Mogeeb
    Dhillon, Sarinder Kaur
    FRONTIERS IN LIFE SCIENCE, 2017, 10 (01): : 98 - 107
  • [23] DIMENSIONAL PREDICTION FOR FDM MACHINES USING ARTIFICIAL NEURAL NETWORK AND SUPPORT VECTOR REGRESSION
    Lyu, Jiaqi
    Manoochehri, Souran
    PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2019, VOL 1, 2020,
  • [24] A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction
    Ghorbani, Mohammad Ali
    Zadeh, Hojat Ahmad
    Isazadeh, Mohammad
    Terzi, Ozlem
    ENVIRONMENTAL EARTH SCIENCES, 2016, 75 (06)
  • [25] A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction
    Mohammad Ali Ghorbani
    Hojat Ahmad Zadeh
    Mohammad Isazadeh
    Ozlem Terzi
    Environmental Earth Sciences, 2016, 75
  • [26] Modeling the viscosity of nanofluids using artificial neural network and Bayesian support vector regression
    Alade, Ibrahim Olanrewaju
    Abd Rahman, Mohd Amiruddin
    Hassan, Amjed
    Saleh, Tawfik A.
    JOURNAL OF APPLIED PHYSICS, 2020, 128 (08)
  • [27] Forecasting Daily Electricity Consumption in Thailand Using Regression, Artificial Neural Network, Support Vector Machine, and Hybrid Models
    Pannakkong, Warut
    Harncharnchai, Thanyaporn
    Buddhakulsomsiri, Jirachai
    ENERGIES, 2022, 15 (09)
  • [28] Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression, and multivariate discriminant analysis
    Kim, Soo Y.
    SERVICE INDUSTRIES JOURNAL, 2011, 31 (03): : 441 - 468
  • [29] Comparative Prediction of Stream Water Total Nitrogen from Land Cover Using Artificial Neural Network and Multiple Linear Regression Approaches
    Amiri, B. J.
    Nakane, K.
    POLISH JOURNAL OF ENVIRONMENTAL STUDIES, 2009, 18 (02): : 151 - 160
  • [30] A novel neural network using the genetic algorithm and structure of the support vector machine
    Ogawa K.
    Mori N.
    IEEJ Transactions on Electronics, Information and Systems, 2020, 140 (07) : 810 - 819
12345