Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

Shabani, Sevda; Samadianfard, Saeed; Sattari, Mohammad Taghi; Mosavi, Amir; Shamshirband, Shahaboddin; Kmeť, Tibor; Várkonyi-Kóczy, Annamária R.

doi:10.3390/atmos11010066

Cited by 111 publications

(41 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…𝔼 denotes the expected value of the argument in the brackets.

μ ((), x) = double-struckE ([], f ((), x))

k ((), x, x^{'}) = double-struckE ([], ((), f ((), x) - μ ((), x)) ((), f ((), x') - μ ((), x')))

This implies that the behavior of GP is completely defined by kernel functionk ( x , x ′ ). Kernel function is important in defining a smooth and flexible f ( x ) and its selection is very essential during training process of a GPR model (Shabani et al, 2020). Generally, different kernel functions in the literature are defined (Tolba et al, 2019), such as Squared‐Exponential kernel (Equation 15), Rational Quadratic kernel [Equation (16)], Periodic kernel [Equation (17)] and Matérn kernel [Equation (18)].

k_{SE} ((), x, x^{'}) = σ^{2} \exp ((), - {(x - x^{'})}^{2} / 2 l^{2})

k_{RQ} ((), x, x^{'}) = σ^{2} {(1 + {((), x - x^{'})}^{2} / 2 a {normall}^{2})}^{- normalα}

k_{{normalP}_{er}} ((), x, x^{'}) = σ^{2} \exp ((), \frac{2 \sin^{2} (π (x - x^{'}) / P)}{l^{2}})

…”

Section: Methodsmentioning

confidence: 99%

Drying characteristics of fermented‐cooked cassava chips used in the production of complementary food: Mathematical and Gaussian process regression modeling approaches

Okonkwo

Olaniran

Adeyi

et al. 2021

J Food Process Engineering

View full text Add to dashboard Cite

The hot air drying characteristics of fermented‐cooked (FC) cassava chips were investigated at a temperature of 50, 60, and 70°C and the fan speed of 0.5, 0.9, and 1.3 m/s. Proximate compositions, functional characteristics, and moisture diffusion parameters of the FC cassava chips were also studied. Furthermore, the applicability of mathematical (MM) and Gaussian process regression (GPR) – based modeling approaches for modeling drying kinetics of the chips was analyzed. Effective diffusivity (Deff) increased with an increase in temperature and fan speed and ranged between 1.1 × 108 and 6 × 108 m2/s. The activation energy (Ea) decreased with fan speed level up to 0.9 m/s and fluctuated between 0.9 and 1.3 m/s. Ea was from 46 to 57 KJ/mol. The drying rate decreased with an increase in temperature and fan speed. Process variables also showed a significant effect on the proximate compositions. FTIR result revealed that drying affected the functional characteristics of the chips. The GPR‐based model showed superiority and can therefore be used for optimization and control monitoring which are necessary for product standardization. Practical Applications Drying of food materials happens to be a major unit operation in the most food process line, prompting the establishment of its drying conditions important. Drying kinetic modeling is very crucial for accurate control of the drying process in the industries. This study shows that GPR‐ based models performed better than mathematical models for modeling the drying curve of FC cassava chips. It also shows how some processing operation affects the functional properties of the chips. GPR‐models are useful in developing robust control systems for industrial drying processes.

show abstract

“…𝔼 denotes the expected value of the argument in the brackets.

μ ((), x) = double-struckE ([], f ((), x))

k ((), x, x^{'}) = double-struckE ([], ((), f ((), x) - μ ((), x)) ((), f ((), x') - μ ((), x')))

k_{SE} ((), x, x^{'}) = σ^{2} \exp ((), - {(x - x^{'})}^{2} / 2 l^{2})

k_{RQ} ((), x, x^{'}) = σ^{2} {(1 + {((), x - x^{'})}^{2} / 2 a {normall}^{2})}^{- normalα}

k_{{normalP}_{er}} ((), x, x^{'}) = σ^{2} \exp ((), \frac{2 \sin^{2} (π (x - x^{'}) / P)}{l^{2}})

…”

Section: Methodsmentioning

confidence: 99%

Drying characteristics of fermented‐cooked cassava chips used in the production of complementary food: Mathematical and Gaussian process regression modeling approaches

Okonkwo

Olaniran

Adeyi

et al. 2021

J Food Process Engineering

View full text Add to dashboard Cite

show abstract

“…Evaporation is the significant content for meteorological science, water resources evaluation, and hydrological cycle [1,2]. Accurate simulation of evaporation contributes to many aspects including hydrology and water resources management, agricultural activities, irrigation scheduling, and water conservation, especially in arid regions [3,4].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm

Dong

Zeng

et al. 2021

Water

View full text Add to dashboard Cite

Accurate estimation of pan evaporation (Ep) is vital for the development of water resources and agricultural water management, especially in arid and semi-arid regions where it is restricted to set up the facilities and measure pan evaporation accurately and consistently. Besides, using pan evaporation estimating models and pan coefficient (kp) models is a classic method to assess the reference evapotranspiration (ET0) which is indispensable to crop growth, irrigation scheduling, and economic assessment. This study estimated the potential of a novel hybrid machine learning model Coupling Bat algorithm (Bat) and Gradient boosting with categorical features support (CatBoost) for estimating daily pan evaporation in arid and semi-arid regions of northwest China. Two other commonly used algorithms including random forest (RF) and original CatBoost (CB) were also applied for comparison. The daily meteorological data for 12 years (2006–2017) from 45 weather stations in arid and semi-arid areas of China, including minimum and maximum air temperature (Tmin, Tmax), relative humidity (RH), wind speed (U), and global solar radiation (Rs), were utilized to feed the three models for exploring the ability in predicting pan evaporation. The results revealed that the new developed Bat-CB model (RMSE = 0.859–2.227 mm·d−1; MAE = 0.540–1.328 mm·d−1; NSE = 0.625–0.894; MAPE = 0.162–0.328) was superior to RF and CB. In addition, CB (RMSE = 0.897–2.754 mm·d−1; MAE = 0.531–1.77 mm·d−1; NSE = 0.147–0.869; MAPE = 0.161–0.421) slightly outperformed RF (RMSE = 1.005–3.604 mm·d−1; MAE = 0.644–2.479 mm·d−1; NSE = −1.242–0.894; MAPE = 0.176–0.686) which had poor ability to operate the erratic changes of pan evaporation. Furthermore, the improvement of Bat-CB was presented more comprehensively and obviously in the seasonal and spatial performance compared to CB and RF. Overall, Bat-CB has high accuracy, robust stability, and huge potential for Ep estimation in arid and semi-arid regions of northwest China and the applications of findings in this study have equal significance for adjacent countries.

show abstract

“…Currently, data-based models have been applied in many events related to hydrology and meteorology. For example, in the simulation of inflow to the reservoir for hydroelectric or irrigation purposes [10,11], estimation and comparison of air temperatures [12], seasonal and annual drought forecast [13], rainfall-runoff forecasting [14], prediction of long-term maximum precipitation [15], groundwater level prediction [16], obtaining reservoir operation rules [17], and class A pan evaporation estimation [18].…”

Section: Introductionmentioning

confidence: 99%

Soil Temperature Estimation with Meteorological Parameters by Using Tree-Based Hybrid Data Mining Models

et al. 2020

Self Cite

View full text Add to dashboard Cite

The temperature of the soil at different depths is one of the most important factors used in different disciplines, such as hydrology, soil science, civil engineering, construction, geotechnology, ecology, meteorology, agriculture, and environmental studies. In addition to physical and spatial variables, meteorological elements are also effective in changing soil temperatures at different depths. The use of machine-learning models is increasing day by day in many complex and nonlinear branches of science. These data-driven models seek solutions to complex and nonlinear problems using data observed in the past. In this research, decision tree (DT), gradient boosted trees (GBT), and hybrid DT–GBT models were used to estimate soil temperature. The soil temperatures at 5, 10, and 20 cm depths were estimated using the daily minimum, maximum, and mean temperature; sunshine intensity and duration, and precipitation data measured between 1993 and 2018 at Divrigi station in Sivas province in Turkey. To predict the soil temperature at different depths, the time windowing technique was used on the input data. According to the results, hybrid DT–GBT, GBT, and DT methods estimated the soil temperature at 5 cm depth the most successfully, respectively. However, the best estimate was obtained with the DT model at soil depths of 10 and 20 cm. According to the results of the research, the accuracy rate of the models has also increased with increasing soil depth. In the prediction of soil temperature, sunshine duration and air temperature were determined as the most important factors and precipitation was the most insignificant meteorological variable. According to the evaluation criteria, such as Nash-Sutcliffe coefficient, R, MAE, RMSE, and Taylor diagrams used, it is recommended that all three (DT, GBT, and hybrid DT–GBT) data-based models can be used for predicting soil temperature.

show abstract

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

Cited by 111 publications

References 46 publications

Drying characteristics of fermented‐cooked cassava chips used in the production of complementary food: Mathematical and Gaussian process regression modeling approaches

Drying characteristics of fermented‐cooked cassava chips used in the production of complementary food: Mathematical and Gaussian process regression modeling approaches

Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm

Soil Temperature Estimation with Meteorological Parameters by Using Tree-Based Hybrid Data Mining Models

Contact Info

Product

Resources

About