A Treatise On Electricity And Magnetism

Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters.

https://www.mdpi.com/2073-4433/11/1/66/pdf

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

Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization

Comprehensive analyses of transport phenomena and thermodynamics of complex multiphysics systems are laborious and computationally intensive. Yet, such analyses are often required during the design of thermal and process equipment. As a remedy, this paper puts forward a novel approach to the prediction of transport behaviours of multiphysics systems, offering significant reductions in the computational time and cost. This is based on machine learning techniques that utilize the data generated by computational fluid dynamics for training purposes. The physical system under investigation includes a stagnation-point flow of a hybrid nanofluid (Cu−Al2O3/Water) over a blunt object embedded in porous media. The problem further involves mixed convection, entropy generation, local thermal non-equilibrium and non-linear thermal radiation within the porous medium. The SVR (Support Machine Vector) model is employed to approximate velocity, temperature, Nusselt number and shear-stress as well as entropy generation and Bejan number functions. Further, PSO meta-heuristic algorithm is applied to propose correlations for Nusselt number and shear stress. The effects of Nusselt number, temperature fields and shear stress on the surface of the blunt-body as well as thermal and frictional entropy generation are analysed over a wide range of parameters. Further, it is shown that the generated correlations allow a quantitative evaluation of the contribution of a large number of variables to Nusselt number and shear stress. This makes the combined computational and artificial intelligence (AI) approach most suitable for design purposes.

A machine learning approach to the prediction of transport and thermodynamic processes in multiphysics systems - heat transfer in a hybrid nanofluid flow in porous media

Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm

CO2 dissolution in water at different temperature and pressure conditions is of essential interest for various environmental, geochemical, and thermodynamic related problems. The topic is of specia...

Menad Nait Amar

Papers

Modeling CO2 Solubility in Water at High Pressure and Temperature Conditions

Bottom hole pressure estimation using hybridization neural networks and grey wolves optimization

Application of hybrid support vector regression artificial bee colony for prediction of MMP in CO2-EOR process

Modeling solubility of sulfur in pure hydrogen sulfide and sour gas mixtures using rigorous machine learning methods

Prediction of CO2 diffusivity in brine using white-box machine learning