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

Meysam Alizamir,Salim Heddam,Mohammad Zounemat-Kermani,Sungwon Kim,Vijay P Singh,Nam Won Kim

doi:10.3390/w12092600

Abstract

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.

Highlights

Dew point temperature (Tdew ) plays a vital role in the elaboration and application of several ecological, hydrological, and meteorological models, especially for the quantification of evapotranspiration [1,2,3]
The kernel extreme learning machine (KELM) models were compared with the boosted regression tree (BRT), multivariate adaptive regression spline (MARS), radial basis function neural network (RBFNN), different machine learning models
The KELM models were compared with the BRT, MARS, RBFNN, and multilayer perceptron neural network (MLPNN) models with respect to the Nash–Sutcliffe efficiency (NSE) statistical indices and coefficient and MLPNN models with respect to the Nash–Sutcliffe efficiency (NSE) statistical indices and of determination (R2 ) and root mean square error (RMSE) indicators

Summary

Introduction

Dew point temperature (Tdew ) plays a vital role in the elaboration and application of several ecological, hydrological, and meteorological models, especially for the quantification of evapotranspiration [1,2,3]. Different important climatic parameters can be affected by Tdew. It has been demonstrated that Tdew can be used as an important factor for climate change studies [4]. Ali et al demonstrated a strong relationship between Tdew and extreme precipitation [5]. Bui et al reported that Tdew helped significantly to understand the relationship between precipitation and air temperature, which can be used to quantify near-surface humidity [6,7]. Several authors have paid attention to the strong relationship between Tdew and meteorological variables [8,9,10,11]

Methods

Results

Discussion

Conclusion