scholarly journals Kernel Extreme Learning Machine: An Efficient Model for Estimating Daily Dew Point Temperature Using Weather Data

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2600
Author(s):  
Meysam Alizamir ◽  
Sungwon Kim ◽  
Mohammad Zounemat-Kermani ◽  
Salim Heddam ◽  
Nam Won Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Relative Humidity ◽  
Extreme Learning Machine ◽  
Air Temperature ◽  
Dew Point ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Input Parameters ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Accurate estimation of dew point temperature (Tdew) has a crucial role in sustainable water resource management. This study investigates kernel extreme learning machine (KELM), boosted regression tree (BRT), radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), and multivariate adaptive regression spline (MARS) models for daily dew point temperature estimation at Durham and UC Riverside stations in the United States. Daily time scale measured hydrometeorological data, including wind speed (WS), maximum air temperature (TMAX), minimum air temperature (TMIN), maximum relative humidity (RHMAX), minimum relative humidity (RHMIN), vapor pressure (VP), soil temperature (ST), solar radiation (SR), and dew point temperature (Tdew) were utilized to investigate the applied predictive models. Results of the KELM model were compared with other models using eight different input combinations with respect to root mean square error (RMSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE) statistical indices. Results showed that the KELM models, using three input parameters, VP, TMAX, and RHMIN, with RMSE = 0.419 °C, NSE = 0.995, and R2 = 0.995 at Durham station, and seven input parameters, VP, ST, RHMAX, TMIN, RHMIN, TMAX, and WS, with RMSE = 0.485 °C, NSE = 0.994, and R2 = 0.994 at UC Riverside station, exhibited better performance in the modeling of daily Tdew. Finally, it was concluded from a comparison of the results that out of the five models applied, the KELM model was found to be the most robust by improving the performance of BRT, RBFNN, MLPNN, and MARS models in the testing phase at both stations.

Extreme learning machine based prediction of daily dew point temperature

2015 ◽  
Vol 117 ◽  
pp. 214-225 ◽  
Author(s):  
Kasra Mohammadi ◽  
Shahaboddin Shamshirband ◽  
Shervin Motamedi ◽  
Dalibor Petković ◽  
Roslan Hashim ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Dew Point ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Learning Machine

scholarly journals Estimate of The Dynamical Change of Air TemperAture, Relative Humidity and Dew Point TemperaTure for Some Selected Station in Iraq

2021 ◽  
Vol 910 (1) ◽  
pp. 012010
Author(s):  
Wedyan G. Nassif ◽  
Sundus H. Jaber ◽  
Salwa S. Naif ◽  
Osama T. Al-Taai
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Meteorological Factors ◽  
Geographical Area ◽  
Dew Point ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Relative Value ◽  
Low Humidity ◽  
Dynamical Change

Abstract Relative humidity can be inferred from the dew point values. When the air temperature and dew point temperatures are very close, the air has high relative humidity. The converse is true when there is a large difference between the air temperature and the dew point temperature, indicating the presence of low humidity air. To understand the expected changes in the climatic elements in the atmosphere, changes in temperature behavior, dew point, and relative humidity have been studied This study used data obtained from the European Center (ECMWF), which includes monthly and annual mean temperatures, dew, and relative humidity during the period (1988-2018) for selected stations in Iraq. The highest values of temperature and dew were recorded in July and August, and they were accompanied by a decrease in relative humidity. The highest value of relative humidity was recorded in December and January, accompanied by a decrease in temperature and dew, as we note through the results that there is an inverse relationship between relative humidity, temperature, and dew point Relative humidity changes when the temperature rises or falls, and the relative humidity may be higher in the morning when the temperature drops. The lowest amount of relative humidity during the day is when the temperature rises, the highest temperature value was recorded on 21July 2017 (12:00 PM) for Basra Station, while the highest relative value is humidity in Basra Governorate. Mosul station on January 21, 2014 (12:00 AM), and the reason is due to meteorological factors and the nature of the geographical area.

scholarly journals Label Rectification Learning through Kernel Extreme Learning Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qiang Cai ◽  
Fenghai Li ◽  
Yifan Chen ◽  
Haisheng Li ◽  
Jian Cao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Extreme Learning Machine ◽  
Classification Performance ◽  
Considerable Progress ◽  
Strong Representation ◽  
Kernel Extreme Learning Machine ◽  
Classification Tasks ◽  
Learning Machine

Along with the strong representation of the convolutional neural network (CNN), image classification tasks have achieved considerable progress. However, majority of works focus on designing complicated and redundant architectures for extracting informative features to improve classification performance. In this study, we concentrate on rectifying the incomplete outputs of CNN. To be concrete, we propose an innovative image classification method based on Label Rectification Learning (LRL) through kernel extreme learning machine (KELM). It mainly consists of two steps: (1) preclassification, extracting incomplete labels through a pretrained CNN, and (2) label rectification, rectifying the generated incomplete labels by the KELM to obtain the rectified labels. Experiments conducted on publicly available datasets demonstrate the effectiveness of our method. Notably, our method is extensible which can be easily integrated with off-the-shelf networks for improving performance.

scholarly journals Estimating Daily Dew Point Temperature Using Machine Learning Algorithms

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 582 ◽  
Author(s):  
Sultan Noman Qasem ◽  
Saeed Samadianfard ◽  
Hamed Sadri Nahand ◽  
Amir Mosavi ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Gene Expression Programming ◽  
Machine Learning Algorithms ◽  
Dew Point ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
M5 Model Tree ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Input Parameters

In the current study, the ability of three data-driven methods of Gene Expression Programming (GEP), M5 model tree (M5), and Support Vector Regression (SVR) were investigated in order to model and estimate the dew point temperature (DPT) at Tabriz station, Iran. For this purpose, meteorological parameters of daily average temperature (T), relative humidity (RH), actual vapor pressure (Vp), wind speed (W), and sunshine hours (S) were obtained from the meteorological organization of East Azerbaijan province, Iran for the period 1998 to 2016. Following this, the methods mentioned above were examined by defining 15 different input combinations of meteorological parameters. Additionally, root mean square error (RMSE) and the coefficient of determination (R2) were implemented to analyze the accuracy of the proposed methods. The results showed that the GEP-10 method, using three input parameters of T, RH, and S, with RMSE of 0.96°, the SVR-5, using two input parameters of T and RH, with RMSE of 0.44, and M5-15, using five input parameters of T, RH, Vp, W, and S with RMSE of 0.37 present better performance in the estimation of the DPT. As a conclusion, the M5-15 is recommended as the most precise model in the estimation of DPT in comparison with other considered models. As a conclusion, the obtained results proved the high capability of proposed M5 models in DPT estimation.

Observed changes in relative humidity and dew point temperature in coastal regions of Iran

2012 ◽  
Vol 110 (3) ◽  
pp. 385-393 ◽  
Author(s):  
P. Hosseinzadeh Talaee ◽  
A. A. Sabziparvar ◽  
Hossein Tabari
Keyword(s):  
Relative Humidity ◽  
Dew Point ◽  
Coastal Regions ◽  
Point Temperature ◽  
Dew Point Temperature
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