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

1
Department of Civil Engineering, Shri Madhwa Vadiraja Institute of Technology and Management, Bantakal-574115, Udupi, India
2
Visvesvaraya Technological University, Belagavi, Karnataka 590018, India
3
Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, Mangalore-575025, India
4
Department of Civil Engineering, Near East University, P.O. Box 99138, Nicosia, North Cyprus, Mersin 10, Turkey
5
Department of Water Engineering, Faculty of Agriculture, University of Tabriz, 5166616471 Tabriz, Iran
6
Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, Sweden
7
Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
*
Authors to whom correspondence should be addressed.
Water 2019, 11(4), 742; https://doi.org/10.3390/w11040742
Received: 1 February 2019 / Revised: 26 March 2019 / Accepted: 2 April 2019 / Published: 10 April 2019
(This article belongs to the Special Issue Water Resources Management Strategy Under Global Change)
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Abstract

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. View Full-Text
Keywords: dew point temperature; firefly algorithm; gravitational search algorithm; humid climate; hybrid models; nature-inspired optimization; semi-arid region dew point temperature; firefly algorithm; gravitational search algorithm; humid climate; hybrid models; nature-inspired optimization; semi-arid region
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Naganna, S.R.; Deka, P.C.; Ghorbani, M.A.; Biazar, S.M.; Al-Ansari, N.; Yaseen, Z.M. Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms. Water 2019, 11, 742.

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