Predicting Dew Point Using Optimized Least Square Support Vector Machine Models

Mohammad Ehteram (), Akram Seifi () and Fatemeh Barzegari Banadkooki ()
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Mohammad Ehteram: Semnan University, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering
Akram Seifi: Vali-e-Asr University of Rafsanjan, Department of Water Science and Engineering, College of Agriculture
Fatemeh Barzegari Banadkooki: Payame Noor University, Agricultural Department

Chapter Chapter 18 in Application of Machine Learning Models in Agricultural and Meteorological Sciences, 2023, pp 187-196 from Springer

Abstract: Abstract Dew point prediction (DPT) is an important topic in agriculture and water resource management. In this chapter, robust soft computing models are used for estimating DPT. This study uses a standalone least square support vector machine (LSSVM) and LSSVM models to estimate the DPT. In this chapter, the LSSVM-antlion optimization algorithm (ANOA), LSSVM-dragonfly algorithm (DOA), LSSVM-crow optimization algorithm (LSSVM-COA), and LSSVM were used to estimate DPT. The different input combinations were used to predict DPT. The results indicated that the optimized LSSVM outperformed the LSSVM models. The best input variable consisted of input variables of relative humidity (RHU), average temperature (AVTEM), wind speed (WIPSE), and number of sunny hours (NOSH). The results indicated that the optimized LSSVM models outperformed the LSSVM models.

Keywords: Dew point temperature; Optimization algorithms; Least square support vector machine; Soft computing model (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-19-9733-4_18

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DOI: 10.1007/978-981-19-9733-4_18

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