Extreme learning machine based prediction of daily dew point temperature

Mohammadi, Kasra; Shamshirband, Shahaboddin; Motamedi, Shervin; Petković, Dalibor; Hashim, Roslan; Gocić, Milan

doi:10.1016/j.compag.2015.08.008

Cited by 113 publications

(28 citation statements)

References 50 publications

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“…Without any doubt, there exist several soft computing models that have shown excellent performance in modeling dew point temperature [24,28]. However, researchers have been extremely zealous to navigate through new methodologies for the sake of attaining more reliable and robust models for solving any kind of complex nonlinear problems.…”

Section: Resultsmentioning

confidence: 99%

“…A generalized regression neural network (GRNN) and multilayer perceptron (MLP) neural network using single and multiple variable input combinations were developed by Kim et al (2015) [23] to find the best input combination that estimates daily DPT with high accuracy. Recently, similar studies conducted using extreme learning machine (ELM) [24], adaptive neurofuzzy inference system (ANFIS) [25], support vector machine (SVM) [26], gene expression programming (GEP), and multivariate adaptive regression splines (MARS) [27] estimated/modeled DPT with sufficient levels of accuracy. Genetic algorithm (GA) based least square SVM and ANFIS models developed by Baghban et al (2016) [28] predict the moist air DPT over an extensive range of relative humidity and temperature.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms

Naganna

Deka

Ghorbani

et al. 2019

Water

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Dew point temperature (DPT) is known to fluctuate in space and time regardless of the climatic zone considered. The accurate estimation of the DPT is highly significant for various applications of hydro and agro–climatological researches. The current research investigated the hybridization of a multilayer perceptron (MLP) neural network with nature-inspired optimization algorithms (i.e., gravitational search (GSA) and firefly (FFA)) to model the DPT of two climatically contrasted (humid and semi-arid) regions in India. Daily time scale measured weather information, such as wet bulb temperature (WBT), vapor pressure (VP), relative humidity (RH), and dew point temperature, was used to build the proposed predictive models. The efficiencies of the proposed hybrid MLP networks (MLP–FFA and MLP–GSA) were authenticated against standard MLP tuned by a Levenberg–Marquardt back-propagation algorithm, extreme learning machine (ELM), and support vector machine (SVM) models. Statistical evaluation metrics such as Nash Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE) were used to validate the model efficiency. The proposed hybrid MLP models exhibited excellent estimation accuracy. The hybridization of MLP with nature-inspired optimization algorithms boosted the estimation accuracy that is clearly owing to the tuning robustness. In general, the applied methodology showed very convincing results for both inspected climate zones.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms

Naganna

Deka

Ghorbani

et al. 2019

Water

View full text Add to dashboard Cite

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“…Mohammadi et al [35] develop a predictive model for the task of inferring the dew point temperature, i.e., the temperature at which water vapor in the air condenses into liquid, since by predicting this, it is also possible to determine whether it will rain or snow at a particular date. To achieve that, the authors exploit Extreme Learning Machine (ELM), i.e., feed-forward neural networks, in which the weights of the nodes within a hidden layer do not require any kind of tuning.…”

Section: Ml-based Forecasting On Numerical Datamentioning

confidence: 99%

A Survey on Intelligent Agricultural Information Handling Methodologies

et al. 2019

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The term intelligent agriculture, or smart farming, typically involves the incorporation of computer science and information technologies into the traditional notion of farming. The latter utilizes plain machinery and equipment used for many decades and the only significant improvement made over the years has been the introduction of automation in the process. Still, at the beginning of the new century, there are ways and room for further vast improvements. More specifically, the low cost of rather advanced sensors and small-scale devices, now even connected to the Internet of Things (IoT), allowed them to be introduced in the process and used within agricultural production systems. New and emerging technologies and methodologies, like the utilization of cheap network storage, are expected to advance this development. In this sense, the main goals of this paper may be summarized as follows: (a) To identify, group, and acknowledge the current state-of-the-art research knowledge about intelligent agriculture approaches, (b) to categorize them according to meaningful data sources categories, and (c) to describe current efficient data processing and utilization aspects from the perspective of the main trends in the field.

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“…Artificial Neural Network (ANN) is one of the most attractive methods of computational intelligence to cope with non-linearity and time varying geodetic data due to its ability to learn and adapt to new dynamic environments. Numerous studies have shown successful implementation of ANN in the geodetic disciplines including but are not limited to deformation studies (Li & Kong, 2014;Huang, Wu, & Ziggah, 2016), meteorological studies (Mohammadi et al, 2015;Durmaz & Karslioglu, 2011), hydrological studies (Tiwari, J. Adamowski, & K. Adamowski, 2016;Deo & Şahin, 2016;Deo, Tiwari, Adamowski, & Quilty, 2017), tidal estimation (Okwuashi & Ndehedehe, 2017), change detection (Pal, 2009;Chang, Han, Yao, Chen, & Xu, 2010), geoid determination (Kavzoglu & Saka, 2005;Sorkhabi, 2015), and gravity field modelling (Turgut, 2016). Additionally, extensive studies on the suitability of ANN for coordinate transformation in both 2D and 3D have also been duly investigated by several authors (Tierra et al, 2008;Zaletnyik, 2004;Lin & Wang, 2006;Tierra, De Freitas, & Guevara, 2009;Tierra & Romero, 2014;Gullu, 2010;Gullu et al, 2011;Turgut, 2010;Mihalache, 2012;Yilmaz & Gullu, 2012;Konakoğlu, Cakir, & Gökalp, 2016;Ziggah, Youjian, Tierra, Konate, & Hui, 2016;Kumi-Boateng & Ziggah, 2017).…”

Section: Introductionmentioning

confidence: 99%

Least Squares Support Vector Machine Model for Coordinate Transformation

Ziggah

Issaka

et al. 2019

Geodesy and Cartography

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In coordinate transformation, the main purpose is to provide a mathematical relationship between coordinates related to different geodetic reference frames. This gives the geospatial professionals the opportunity to link different datums together. Review of previous studies indicates that empirical and soft computing models have been proposed in recent times for coordinate transformation. The main aim of this study is to present the applicability and performance of Least Squares Support Vector Machine (LS-SVM) which is an extension of the Support Vector Machine (SVM) for coordinate transformation. For comparison purpose, the SVM and the widely used Backpropagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), 2D conformal and affine methods were also employed. To assess how well the transformation results fit the observed data, the root mean square of the residual horizontal distances and standard deviation were used. From the results obtained, the LS-SVM and RBFNN had comparable results and were better than the other methods. The overall statistical findings produced by LS-SVM met the accuracy requirement for cadastral surveying applications in Ghana. To this end, the proposed LS-SVM is known to possess promising predictive capabilities and could efficiently be used as a supplementary technique for coordinate transformation.

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Extreme learning machine based prediction of daily dew point temperature

Cited by 113 publications

References 50 publications

Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms

Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms

A Survey on Intelligent Agricultural Information Handling Methodologies

Least Squares Support Vector Machine Model for Coordinate Transformation

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