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.
Highlights
Dew point temperature (DPT) is a weather condition that happens when the air is fully saturated with water vapor and the number of water molecules evaporating from any surface is in equilibriumWater 2019, 11, 742; doi:10.3390/w11040742 www.mdpi.com/journal/waterWater 2019, 11, 742 with the number of molecules condensing [1]
The input/output (I/O) structure formulated for the development of multilayer perceptron (MLP)–firefly algorithm (FFA), MLP–gravitational search algorithm (GSA), and the standalone AI models were based on the correlated weather information with the target variable—DPT (Table 1)
MLP–gravitational search algorithm (MLP–GSA)) in estimating daily DPT were evaluated against support vector machine (SVM) and extreme learning machine (ELM) model results reported in Deka et al (2018) [26], since the models developed in this study used the same data and model
Summary
Dew point temperature (DPT) is a weather condition that happens when the air is fully saturated with water vapor and the number of water molecules evaporating from any surface is in equilibrium. The effect of different climatic variables (sunshine hours, air temperature, wind speed, relative humidity, and saturation vapor pressure) on daily DPT estimation was examined by Kisi et al (2013) [21] using different learning algorithms of neural network and adaptive neural fuzzy inference systems (ANFIS). In the present study, two hybrid approaches, namely the MLP neural network coupled with the gravitational search and firefly optimizer algorithms (MLP–GSA and MLP–FFA) are introduced to enhance the efficiency of daily DPT estimates of semi-arid (Hyderabad) and humid (Bajpe) regions of India. The weather information, including wet bulb temperature, relative humidity, and vapor pressure, are used as model inputs to estimate daily DPT. MLP systems, related to the estimation of daily DPT, is compared to those obtained in our previous study from the use of SVM and ELM [26], allowing a comparative study of all the methods
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