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Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms
Department of Civil Engineering, Shri Madhwa Vadiraja Institute of Technology and Management, Bantakal, Udupi, India. Visvesvaraya Technological University, Belagavi, Karnataka, India.
Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, Mangalore, India.
Department of Civil Engineering, Near East University, Nicosia, Turkey. Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.
Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.
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2019 (English)In: Water, ISSN 2073-4441, E-ISSN 2073-4441, water, Vol. 11, no 4, article id 742Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Switzerland: MDPI, 2019. Vol. 11, no 4, article id 742
Keywords [en]
dew point temperature; firefly algorithm; gravitational search algorithm; humid climate;
National Category
Engineering and Technology Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-73544DOI: 10.3390/w11040742ISI: 000473105700112Scopus ID: 2-s2.0-85065016433OAI: oai:DiVA.org:ltu-73544DiVA, id: diva2:1303674
Note

Validerad;2019;Nivå 2;2019-04-15 (svasva)

Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-08-16Bibliographically approved

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Al-Ansari, Nadhir

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