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

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 742 ◽  
Author(s):  
Sujay Naganna ◽  
Paresh Deka ◽  
Mohammad Ghorbani ◽  
Seyed Biazar ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Optimization Algorithms ◽  
Back Propagation ◽  
Absolute Error ◽  
Dew Point ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Support Vector ◽  
Back Propagation Algorithm ◽  
Point Temperature ◽  
Dew Point Temperature

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.

scholarly journals Establishing Coupled Models for Estimating Daily Dew Point Temperature Using Nature-Inspired Optimization Algorithms

Hydrology ◽  
2022 ◽  
Vol 9 (1) ◽  
pp. 9
Author(s):  
Saeid Mehdizadeh ◽  
Babak Mohammadi ◽  
Farshad Ahmadi
Keyword(s):  
Sunshine Duration ◽  
Optimization Algorithms ◽  
Hybrid Models ◽  
Dew Point ◽  
Estimation Accuracy ◽  
Coupled Models ◽  
Inference System ◽  
Point Temperature ◽  
Dragonfly Algorithm ◽  
Dew Point Temperature

Potential of a classic adaptive neuro-fuzzy inference system (ANFIS) was evaluated in the current study for estimating the daily dew point temperature (Tdew). The study area consists of two stations located in Iran, namely the Rasht and Urmia. The daily Tdew time series of the studied stations were modeled through the other effective variables comprising minimum air temperature (Tmin), extraterrestrial radiation (Ra), vapor pressure deficit (VPD), sunshine duration (n), and relative humidity (RH). The correlation coefficients between the input and output parameters were utilized to determine the most effective inputs. Furthermore, novel hybrid models were proposed in this study in order to increase the estimation accuracy of Tdew. For this purpose, two optimization algorithms named bee colony optimization (BCO) and dragonfly algorithm (DFA) were coupled on the classic ANFIS. It was concluded that the hybrid models (i.e., ANFIS-BCO and ANFIS-DFA) demonstrated better performances compared to the classic ANFIS. The full-input pattern of the coupled models, specifically the ANFIS-DFA, was found to present the most accurate results for both the selected stations. Therefore, the developed hybrid models can be proposed as alternatives to the classic ANFIS to accurately estimate the daily Tdew.

scholarly journals Estimating Daily Dew Point Temperature Using Machine Learning Algorithms

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 582 ◽  
Author(s):  
Sultan Noman Qasem ◽  
Saeed Samadianfard ◽  
Hamed Sadri Nahand ◽  
Amir Mosavi ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Gene Expression Programming ◽  
Machine Learning Algorithms ◽  
Dew Point ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
M5 Model Tree ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Input Parameters

In the current study, the ability of three data-driven methods of Gene Expression Programming (GEP), M5 model tree (M5), and Support Vector Regression (SVR) were investigated in order to model and estimate the dew point temperature (DPT) at Tabriz station, Iran. For this purpose, meteorological parameters of daily average temperature (T), relative humidity (RH), actual vapor pressure (Vp), wind speed (W), and sunshine hours (S) were obtained from the meteorological organization of East Azerbaijan province, Iran for the period 1998 to 2016. Following this, the methods mentioned above were examined by defining 15 different input combinations of meteorological parameters. Additionally, root mean square error (RMSE) and the coefficient of determination (R2) were implemented to analyze the accuracy of the proposed methods. The results showed that the GEP-10 method, using three input parameters of T, RH, and S, with RMSE of 0.96°, the SVR-5, using two input parameters of T and RH, with RMSE of 0.44, and M5-15, using five input parameters of T, RH, Vp, W, and S with RMSE of 0.37 present better performance in the estimation of the DPT. As a conclusion, the M5-15 is recommended as the most precise model in the estimation of DPT in comparison with other considered models. As a conclusion, the obtained results proved the high capability of proposed M5 models in DPT estimation.

scholarly journals Improving efficiency and lowering operating costs of evaporative cooling

2021 ◽  
Vol 338 ◽  
pp. 01027
Author(s):  
Jan Taler ◽  
Bartosz Jagieła ◽  
Magdalena Jaremkiewicz
Keyword(s):  
Electricity Consumption ◽  
Open Circuit ◽  
Dew Point ◽  
Operating Costs ◽  
Water Evaporation ◽  
Total Water ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Adiabatic Cooling ◽  
Positive Effect

Cooling towers, or so-called evaporation towers, use the natural effect of water evaporation to dissipate heat in industrial and comfort installations. Water, until it changes its state of aggregation, from liquid to gas, consumes energy (2.257 kJ/kg). By consuming this energy, it lowers the air temperature to the wet-bulb temperature, thanks to which the medium can be cooled below the ambient temperature. Evaporative solutions are characterized by continuous water evaporation (approx. 1.5% of the total water flow) and low electricity consumption (high EER). Evaporative (adiabatic) cooling also has a positive effect on the reduction of electricity consumption of cooled machines. Lowering the relative humidity (RH) by approx. 2% lowers the wet-bulb temperature by approx. 0.5°C, which increases the efficiency of the tower, operating in an open circuit, expressed in kW, by approx. 5%, while reducing water consumption and treatment costs. The use of the M-Cycle (Maisotsenko cycle) to lower the temperature of the wet thermometer to the dew point temperature will reduce operating costs and increase the efficiency of cooled machines.

Abutment scour in clear-water and live-bed conditions by GMDH network

2013 ◽  
Vol 67 (5) ◽  
pp. 1121-1128 ◽  
Author(s):  
Mohammad Najafzadeh ◽  
Gholam-Abbas Barani ◽  
Masoud Reza Hessami Kermani
Keyword(s):  
Back Propagation ◽  
Support Vector ◽  
Group Method ◽  
Scour Depth ◽  
Back Propagation Algorithm ◽  
Clear Water ◽  
Sediment Size ◽  
Svm Model ◽  
Vector Machines ◽  
Abutment Scour

In the present study, the Group Method of Data Handling (GMDH) network has been utilized to predict abutments scour depth for both clear-water and live-bed conditions. The GMDH network was developed using a Back Propagation algorithm (BP). Input parameters that were considered as effective variables on abutment scour depth included properties of sediment size, geometry of bridge abutments, and properties of approaching flow. Training and testing performances of the GMDH network were carried out using dimensionless parameters that were collected from the literature. The testing results were compared with those obtained using the Support Vector Machines (SVM) model and the traditional equations. The GMDH network predicted the abutment scour depth with lower error (RMSE (root mean square error) = 0.29 and MAPE (mean absolute percentage of error) = 0.99) and higher (R = 0.98) accuracy than those performed using the SVM model and the traditional equations.

Sign in / Sign up

Export Citation Format

Share Document