干旱区生态环境知识资源中心(Arid): Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms

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DOI	10.3390/w11040742
	Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms
	Naganna, Sujay Raghavendra 1,2; Deka, Paresh Chandra 3; Ghorbani, Mohammad Ali 4,5; Biazar, Seyed Mostafa 5; Al-Ansari, Nadhir 6; Yaseen, Zaher Mundher 7
通讯作者	Deka, Paresh Chandra ; Yaseen, Zaher Mundher
来源期刊	WATER
ISSN	2073-4441
出版年	2019
卷号	11 期号:4
英文摘要	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.
英文关键词	dew point temperature firefly algorithm gravitational search algorithm humid climate hybrid models nature-inspired optimization semi-arid region
类型	Article
语种	英语
国家	India ; Cyprus ; Iran ; Sweden ; Vietnam
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000473105700112
WOS关键词	NEURAL-NETWORKS ; MULTILAYER PERCEPTRON ; FIREFLY ALGORITHM ; PREDICTION ; HUMIDITY ; AIR ; EVAPOTRANSPIRATION ; PERFORMANCE ; FORECASTS
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/219192
作者单位	1.Shri Madhwa Vadiraja Inst Technol & Management, Dept Civil Engn, Bantakal 574115, Udupi, India; 2.Visvesvaraya Technol Univ, Belagavi 590018, Karnataka, India; 3.Natl Inst Technol Karnataka, Dept Appl Mech & Hydraul, Surathkal 575025, Mangalore, India; 4.Near East Univ, Dept Civil Engn, POB 99138,Mersin 10, Nicosia, North Cyprus, Cyprus; 5.Univ Tabriz, Fac Agr, Dept Water Engn, Tabriz 5166616471, Iran; 6.Lulea Univ Technol, Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden; 7.Ton Duc Thang Univ, Fac Civil Engn, Sustainable Dev Civil Engn Res Grp, Ho Chi Minh City, Vietnam
推荐引用方式 GB/T 7714	Naganna, Sujay Raghavendra,Deka, Paresh Chandra,Ghorbani, Mohammad Ali,et al. Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms[J],2019,11(4).
APA	Naganna, Sujay Raghavendra,Deka, Paresh Chandra,Ghorbani, Mohammad Ali,Biazar, Seyed Mostafa,Al-Ansari, Nadhir,&Yaseen, Zaher Mundher.(2019).Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms.WATER,11(4).
MLA	Naganna, Sujay Raghavendra,et al."Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms".WATER 11.4(2019).