Abstract:Groundwater (GW) is a critical freshwater resource for billions of individuals worldwide. Rapid anthropogenic exploitation has increasingly deteriorated GW quality and quantity. Reliable estimation of complex hydrochemical properties of GW is crucial for sustainable development. Real field and experimental studies in an agricultural area from the significant sandstone aquifers (Wajid Aquifer) were conducted. For the modelling purpose, three types of computational models, including the emerging Hammerstein–Wien… Show more
“…Total Hardness (TH) is crucial in evaluating groundwater suitability for domestic and industrial purposes (El-Rawy and Ismail, 2019). Although water hardness has no recognized adverse effects, it increases detergent consumption during cleaning, and some evidence suggests a role in heart disease (Benaafi et al 2022). Using Equation ( 8), the total hardness (TH) of the groundwater was calculated.…”
Section: Suitability For Irrigation Purposesmentioning
Globally, groundwater has globally emerged as a crucial freshwater source for domestic, irrigation, and industrial needs. The evaluation of groundwater quality in the Toshka region is imperative to ensure its suitability for the extensive agricultural and industrial activities underway in this promising, groundwater-dependent development area. This is particularly significant as Egypt increasingly relies on groundwater reserves to address freshwater deficits and to implement mega-development projects in barren lands. In this study, fifty-two samples were collected from the recently drilled wells tapping into the Nubian Sandstone Aquifer (NSA) in the Toshka region. Groundwater quality was assessed through hydrochemical analysis, Piper diagram, and various indicators such as Na%, SAR, RSC, KR, MH and PI. The hydrochemical analysis revealed improved groundwater quality characteristics, attributed to continuous recharge from Lake Nasser. The Piper diagram categorised most of the water samples as "secondary salinity" water type. Almost all wells proved suitable for irrigation with only two wells unsuitable based on MH values and six wells based on KR values. Considering Total Hardness (TH) values, all samples were classified as "Soft", indicating their suitability for domestic and industrial purposes. Water Quality Index (WQI) results concluded that all samples met WHO and FAO guidelines for drinking and irrigation, respectively. Spatial distribution maps, constructed using GIS, facilitate the interpretation of the results. Regular monitoring of quality parameters is essential to detect any deviation from permissible limits.
“…Total Hardness (TH) is crucial in evaluating groundwater suitability for domestic and industrial purposes (El-Rawy and Ismail, 2019). Although water hardness has no recognized adverse effects, it increases detergent consumption during cleaning, and some evidence suggests a role in heart disease (Benaafi et al 2022). Using Equation ( 8), the total hardness (TH) of the groundwater was calculated.…”
Section: Suitability For Irrigation Purposesmentioning
Globally, groundwater has globally emerged as a crucial freshwater source for domestic, irrigation, and industrial needs. The evaluation of groundwater quality in the Toshka region is imperative to ensure its suitability for the extensive agricultural and industrial activities underway in this promising, groundwater-dependent development area. This is particularly significant as Egypt increasingly relies on groundwater reserves to address freshwater deficits and to implement mega-development projects in barren lands. In this study, fifty-two samples were collected from the recently drilled wells tapping into the Nubian Sandstone Aquifer (NSA) in the Toshka region. Groundwater quality was assessed through hydrochemical analysis, Piper diagram, and various indicators such as Na%, SAR, RSC, KR, MH and PI. The hydrochemical analysis revealed improved groundwater quality characteristics, attributed to continuous recharge from Lake Nasser. The Piper diagram categorised most of the water samples as "secondary salinity" water type. Almost all wells proved suitable for irrigation with only two wells unsuitable based on MH values and six wells based on KR values. Considering Total Hardness (TH) values, all samples were classified as "Soft", indicating their suitability for domestic and industrial purposes. Water Quality Index (WQI) results concluded that all samples met WHO and FAO guidelines for drinking and irrigation, respectively. Spatial distribution maps, constructed using GIS, facilitate the interpretation of the results. Regular monitoring of quality parameters is essential to detect any deviation from permissible limits.
“…Consequently, they offer statistics on how well a model matches the data, and they range between 0 to 1. The higher the value of R 2 and R, the better the result of the model, and it will signify that the created models have a high degree of precision [37], [38], [38]- [43], [28]. These metrics are theoretically stated in Equations ( 10) - (13).…”
Section: Assessment Of the Models Accuracymentioning
The purpose of this paper is to present a machine-learning approach for forecasting short-term load demand in Kano. Artificial Neural Network (ANN) and Support Vector Machine (SVM) are applied to develop the model. Three independent variables are selected as inputs, and one output is used to discover the level of relationship among the variables that are independent. This approach can ascertain a more precise prediction of the short-term load demand compared to expensive and rigorous experimental techniques. The correlation coefficient (R), coefficient of determination (R2), Mean Square Error (MSE), and Root Mean Square Error (RMSE) were used as indicators to evaluate the prediction accuracy of the selected algorithms. ANN gives a close accurate output as follows: R=0.97539, R2=0.951385, MSE=0.003674 and RMSE=0.060369.
“…It is based on Gaussian processes, which are flexible and nonparametric approaches to modelling relationships between input features and output values. GPR provides predictions and a measure of uncertainty associated with those predictions, making it valuable for decision making and uncertainty quantification [48]. A Gaussian process is a collection of random variables where any finite subset of these variables follows a joint Gaussian distribution.…”
Educational management is the combination of human and material resources that supervises, plans, and responsibly executes an educational system with outcomes and consequences. However, when seeking improvements in interprofessional education and collaborative practice through the management of health professions, educational modules face significant obstacles and challenges. The primary goal of this study was to analyse data collected from discussion sessions and feedback from respondents concerning interprofessional education (IPE) management modules. Thus, this study used an explanatory and descriptive design to obtain responses from the selected group via a self-administered questionnaire and semi-structured interviews, and the results were limited to averages, i.e., frequency distributions and summary statistics. The results of this study reflect the positive responses from both subgroups and strongly support the further implementation of IPE in various aspects and continuing to improve and develop it. Four different artificial intelligence (AI) techniques were used to model interprofessional education improvement through educational management, using 20 questions from the questionnaire as the variables (19 input variables and 1 output variable). The modelling performance of the nonlinear and linear models could reliably predict the output in both the calibration and validation phases when considering the four performance metrics. These models were shown to be reliable tools for evaluating and modelling interprofessional education through educational management. Gaussian process regression (GPR) outperformed all the models in both the training and validation stages.
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