Search

This paper uses a Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) re-analysis to identify long-term Mediterranean Sea Offshore Wind (OW) classification possible locations. In particular, an OW classification based on the last 40-years period OW speeds highlighted the best areas for potential Offshore Wind Turbine Generators (OWTG) installations in the Mediterranean basin. Preliminary, long-term OW classification results show that several Mediterranean basin zones in the Aegean Sea, Gulf of Lyon, the Northern Morocco and Tunisia regions have attractive OW potential. Secondly, a combined forecasting model based on the wavelet decomposition method and long-term memory neural network has been developed to predict the short-term wind speed considering the last ten years of hourly data for Mediterranean areas. The results of the proposed model for wind speed prediction have been compared with other single models, Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), highlighting a higher level of accuracy. Finally, three Weibull fitting algorithms have been provided to analyze the wind energy potential in the Mediterranean basin. • Offshore wind classification using 40 years of re-analysis data and learning models. • Offshore wind speed assessment and mapping of the Mediterranean Sea hot region's. • A prediction model based on wavelet transform and long short-term memory network. • The evaluated model based on re-analysis data for offshore wind hot region's. [ABSTRACT FROM AUTHOR]

This paper presents a study focused on the analysis of phytosanitary treatment sales in the Souss Massa region of Morocco. The objective of the study is to predict the sales of agricultural products, particularly crop protection solutions, aiming to optimize supply chain operations and meet customer demand effectively. Data for this study are collected from multiple sources, including the Enterprise Resource Planning (ERP) system called Microsoft Dynamics AXAPTA used by a leading agricultural company operating in the region. Information such as the date of sale, farming type, climate, and specific sales locations within the Sous Massa region is gathered. Machine learning techniques are applied for forecasting. Various regression models, including the Gradient Boosting Regressor algorithm, are employed to determine the most accurate predictor. Evaluation of the models reveals promising results, with a Mean Absolute Error (MAE) of 0.0035 and a Root Mean Square Error (RMSE) of 0.0066. The results obtained by applying various regression models, including the Gradient Boosting Regressor algorithm, demonstrate promising prediction scores. These findings contribute to the field of sales prediction in the agricultural industry while considering the impact of climate conditions, farming practices, and regional factors. [ABSTRACT FROM AUTHOR]

• A suitable PV forecasting framework is successfully established. • Based on deep learning mechanism, the features of time series can be better used. • A pluralistic and scientific evaluation system is constructed. • It is proved theoretically that proposed model can achieve pareto optimal solution. Owing to the continuous increase in the proportion of solar generation accounting for the total global generation, real-time management of solar power has become indispensable. Moreover, accurate prediction of photovoltaic power is emerging as an important link to support grid operations and reflect real-life scenarios. Various studies have led to the design of several forecasting models. Nevertheless, most predictors do not focus on the effects of the factors of photovoltaic modules on the forecast results. To fill this gap, in this paper, a novel multivariable hybrid prediction system combining signal decomposition, artificial intelligence models, deep learning models, and a swarm intelligence optimization strategy is proposed. This system fully utilizes independent variable features (including the module temperature) to efficiently enhance the precision and efficiency of photovoltaic forecasting. In particular, it is proved that a Pareto-optimal solution can be obtained using the designed system. Using three datasets obtained from Safi-Morocco, the presented system is verified by comparative experiments, and its remarkable advantages in terms of forecasting are demonstrated. Specifically, using the three datasets, the symmetric mean absolute percentage errors obtained by the presented forecast system are 2.129%, 2.335%, and 3.654%, respectively, which are significantly lower than those achieved with other comparison models. Furthermore, a comprehensive and rational evaluation methodology is employed to assess the predictive capability of the developed system. The evaluation results show that the system is effective in improving the forecasting efficiency and outperforms other benchmark models. [ABSTRACT FROM AUTHOR]

Solar energy forecasting represents a key element in increasing the competitiveness of solar power plants in the energy market and reducing the dependence on fossil fuels in economic and social development. This paper presents an approach for predicting solar energy, based on machine and deep learning techniques. The relevance of the studied models was evaluated for real-time and short-term solar energy forecasting to ensure optimized management and security requirements in this field while using an integral solution based on a single tool and an appropriate predictive model. The datasets we used in this study, represent data from 2016 to 2018 and are related to Errachidia which is a semi-desert climate province in Morocco. Pearson correlation coefficient was deployed to identify the most relevant meteorological inputs from which the models should learn. RF and ANN have provided high accuracies against LR and SVR, which have reported very significant errors. ANN has shown good performance for both real-time and short-term predictions. The key findings were compared with Pirapora in Brazil, which is a tropical climate region, to show the quality and reproducibility of the study. [Display omitted] • The efficiency of Pearson correlation coefficient was investigated. • The accuracy of LR, RF, SVR and ANN for forecasting solar energy was evaluated. • The performance was evaluated by MAE, MSE, RMSE, Max Error, R-squared and NRMSE. • RF and ANN models show high level of accuracy for real-time solar energy prediction. • ANN outperforms all of the other models for short-term solar energy prediction. [ABSTRACT FROM AUTHOR]

The process of phosphate extraction can significantly benefit from the advances in spectral analysis and Artificial Intelligence to reduce the cost of the drilling operation. The ambiguities caused by the apparent similarities between different layers and by the existing mineralogical alterations complexify the delineation of phosphate layers with conventional vision systems. In this paper, we established a spectral signature database of representative samples collected from the Ben Guerir deposit in Morocco, over the 250–2500 nm spectral range (mid-ultraviolet to mid-infrared). The aim is to build a spectral database of the samples and select an optimal waveband set capable of good discrimination, which can be used in a multispectral inspection system for an accurate delimitation of the soil layers during the drilling operation. First, the reflectance signatures of the extracted soil samples were collected. Second, principal component analysis (PCA) loadings were investigated to determine the most informative feature subspaces. A Bhattacharyya distance (B-distance) separability test was then implemented to select the most dissociating combinations of 3 bands from these subspaces. Finally, a machine learning classification test was used to evaluate the capacity of the selected features to discriminate phosphate samples. The results demonstrate the impact of selecting an informative reduced feature set and show good discrimination rates based on the combined information of wavelengths from the Ultraviolet (UV) or the Near-infrared (NIR) spectral ranges. • A new spectral dataset of a sedimentary phosphate deposit is created (250–2500 nm). • An approach to select relevant features for better classification is proposed. • Informative subsets of the features are analytically selected. • Phosphate classification exceeded 90% accuracy with 3 selected features only. • The obtained features can be used for an on-site multispectral embedded system. [ABSTRACT FROM AUTHOR]

Using conventional methods to evaluate the irrigation water quality is usually expensive and laborious for the farmers, particularly in developing countries. However, the applications of artificial intelligence models can overcome this issue through forecasting and evaluating the irrigation water quality indexes of aquifer systems using physical parameters as features. This paper aims forecasting the Total Dissolved Solid (TDS), Potential Salinity (PS), Sodium Adsorption Ratio (SAR), Exchangeable Sodium Percentage (ESP), Magnesium Adsorption Ratio (MAR), and the Residual Sodium Carbonate (RSC) parameters through Electrical Conductivity (EC), Temperature (T), and pH as inputs. To achieve this purpose, we developed and evaluated Adaptive Boosting (Adaboost), Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) models using 520 samples of data related to fourteen Groundwater quality parameters in Berrechid aquifer, Morocco. The results revealed that the overall prediction performances of Adaboost and RF models are higher than those of SVR and ANN. However, the generalization ability and sensitivity to the inputs analyses show that the ANN and SVR models are more generalizable and less sensitive to input variables than Adaboost and RF. Globally, the developed models are valuable in forecasting the irrigation water quality parameters and could help the farmers and decision-makers in managing the irrigation water strategies. The developed approaches in this study have been revealed promising in low-cost and real-time forecast of groundwater quality through the use of physical parameters as input variables. ga1 • Conventional methods have some limitations in evaluating IWQ. • Developing 4 ML models for forecasting the IWQ parameters. • RF and Adaboost models are more accurate than SVR and ANN ones. • Uncertainty and sensitivity analyses were carried out. [ABSTRACT FROM AUTHOR]