Estimating Daily Dew Point Temperature Using Machine Learning Algorithms

Qasem, Sultan Noman; Samadianfard, Saeed; Nahand, Hamed Sadri; Mosavi, Amir; Shamshirband, Shahaboddin; Chau, Kwok Wing

doi:10.3390/w11030582

Cited by 91 publications

(51 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is no straightforward guideline for splitting the training and testing data in machine learning modeling [38][39][40][41][42][43][44][45][46]. For instance, the study of Choubin [47] used a total of 63% of their data for model development, whereas Qasem et al, [48] utilized 67% of data, Asadi et al, [41], Samadianfard et al, [49,50], and Dodangeh et al, [51] used 70%, and Zounemat-Kermani et al, [52] implemented 80% of total data to develop their models. Thus, to develop the studied models for PE estimation, we divided the data into training (70%) and testing (30%).…”

Section: Resultsmentioning

confidence: 99%

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

et al. 2020

Self Cite

View full text Add to dashboard Cite

Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters.

show abstract

Section: Resultsmentioning

confidence: 99%

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although the application of machine learning for prediction of pollutants and mercury emissions is well established within the scientific community, the potential of novel machine learning models (e.g., ensembles and hybrids) has still not been explored for mercury prediction. In particular, a wide range of novel hybrid machine learning methods has recently been developed to deliver higher accuracy and performance [47,76,77]. For instance, the hybrid model of the ANFIS-PSO-which is an integration of an adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO)-has shown promising results [78].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Among them, machine learning methods have been reported to deliver higher performance in terms of accuracy, robustness, and lower computational power in dealing with uncertainties and big data [38][39][40][41]. Several surveys report that ensemble and hybrid models are the future trends in machine learning due to the fact of their optimized algorithms for higher efficiency [42][43][44][45][46][47][48]. Hybrid machine learning models are shown to deliver higher performance in air pollution modeling and prediction [49][50][51][52][53][54].…”

Section: Introductionmentioning

confidence: 99%

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

et al. 2019

Self Cite

View full text Add to dashboard Cite

Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type.

show abstract

“…Dragomir Although the application of machine learning for prediction of pollutants and mercury emissions is well established within the scientific communities, the potential of the novel machine learning models (e.g., ensembles and hybrids) is still not explored for mercury prediction. In particular, a wide range of novel hybrid machine learning methods has been recently developed to deliver higher accuracy and performance [47,76,77]. For instance, the hybrid model of ANFIS-PSO which is an integration of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) has shown to deliver promising results [78].…”

Section: Previous Investigationsmentioning

confidence: 99%

Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Shamshirband¹,

Hadipoor²,

Baghban³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations' boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of ANFIS-PSO model, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively.Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

show abstract

Estimating Daily Dew Point Temperature Using Machine Learning Algorithms

Cited by 91 publications

References 30 publications

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Contact Info

Product

Resources

About