Optimizing wind farm layout for enhanced electricity extraction using a new hybrid PSO-ANN method

With the growing need for renewable energy,wind farms are playing an important role in generating clean power from wind resources.The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency.This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization(PSO)and artificial neural networks(ANNs).The PSO method was used to explore the solution space and develop preliminary turbine layouts,and the ANN model was used to fine-tune the placements based on the predicted energy generation.The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits.The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches,giving exciting prospects for developing more efficient and sustainable wind farms.The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques.Furthermore,this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency.A notable increase in power generation is observed across various scenarios.The percentage increase in the power generation ranged from approximately 7.7%to 11.1%.Owing to its versatility and adaptability to site-specific conditions,the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.

Comparing and Evaluating Probabilistic and Deterministic Spatial Interpolation Methods for Groundwater Level of Haouz in Morocco

Growing water scarcity is one of the major challenges of the 21st century, especially in arid and semi-arid climates such as our study area. The efficient, sustainable and integrated groundwater management plays a key role for conserving this vital resource. In order to overcome this issue, the study of aquifer system’s behavior seems necessary. For this purpose, the areal piezometric level map is an essential tool. As piezometric level data are spatially limited in sample points, the?spatial interpolation and geostatistics are the best way to produce the needed map. Several methods exist allowing to approach real values with varying degrees of accuracy. This work aims to compare?and evaluate spatial interpolation methods for groundwater level of Haouz using a dataset of 39 piezometers. The deterministic methods used in this study are Inverse Distance Weighted (IDW) and Radial Basis Functions (RBF) and the probabilistic ones are ordinary kriging (OK), simple kriging (SK) and universal kriging (UK). This study shows the difficulty of having a key role to choose the suitable method for given input dataset. The best model remains the one that, after comparing several methods, offers the best accuracy, which is assessed using Cross-validation and statistical indicators. The results reveals that ordinary kriging with trend removal technique is the optimal method in this case. It indicates the superiority of this technique with a decrease in Root Mean Square Error (RMSE) up to 61.67%. It underestimates groundwater level with an average of 2.8%, which is reliable. The areal piezometric level and associated prediction standard error maps give additional information and recommendations that characterize the studied aquifer system and will ultimately improve sustainable groundwater management.

Study of Coagulation Process with Lime in Treatment of Landfill Leachate from Fkih Ben Salah City (Morocco)

The leachates are the seat of complex processes which give them a heterogeneous character. Their compositions vary according to several factors: nature of the waste, conditions of their deposition, climatic conditions, their durations of stay, etc. They contain important quantities of organic, mineral matters even of bacteria, which require their treatment in order to safeguard the environment. To do this, several methods are used, such as membrane techniques (reverse osmosis, nanofiltration, etc.), biological techniques (activated sludge, SBR, etc.) and physicochemical techniques (Coagulation-flocculation, adsorption on activated carbon, etc.). Among these techniques, the leachate treatment by coagulation process with the lime showed interesting reduction of the various pollutants: 92.95% of turbidity, 88.23% of suspended matter, 89.89% of COD, 90.83% of BOD5, 78.39% of Fe, 77.78% of Mo, 38.29% of Cd, 48.75% of Al, 50.24% of S2-, 20.57% of K+, 27.24% of phosphorus and 19.53% of Cl-. Based on these results, the coagulation with the lime reveals interesting because it allows to reduce at a lesser cost the pollutants present in leachates.

A Sensor Web for Real-Time Groundwater Data Monitoring in Morocco

This article presents research and development of an interoperable platform to facilitate, monitor and coordinate groundwater data sharing. This system was orchestrated by a number of services described by Open Geospatial Consortium (OGC) such as Sensor Observation Service (SOS) and other services for the use of mapping data, Web Feature Services (WFS), Web Map Service (WMS), and catalogue services (CSW). An important activity for our project was the establishment of a portal for geographic data and services. Geoportal developed for this project will promote and facilitate access to groundwater data and share theme more openly. Otherwise our system has been designed to provide a powerful tool that enhances the ability of regional staff to monitor near real-time groundwater data (i.e.?piezometric level) and as a result will help provide a more effective response to environmental upsets.

Toward a Multi-Dimensional Ontology Model for Urban Planning

In the recent past years, the major challenge facing scientists and researchers in the field of knowledge engineering is classifying and sharing geographic data with both computer and human. Ontology is one of the most important classification schemes that aim to make data machine-interpretable. In the literature, all ontology based models developed in the field of urban planning have some limits. First, they describe the nature of each parcel of the soil while ignoring other important components of urban planning such as services, infrastructure … Secondly, these ontologies are developed according to legislation and regulations of the zone studied so they can’t be used by some urban territories that have specific urban law such as Moroccan country. This paper presents a new multi-dimensional ontology model called LUP specifically developed to overcome this flaw. The main goal is to provide semantic land use descriptions according to four dimensions: zoning, services, infrastructure and easement and to define all LUP concepts within the Moroccan urban law. We illustrate the use of our proposed model with a case study by mapping a land use planning document within the area of Ainchock municipality of Casablanca city according to our model concepts.

YOLOv2 Deep Learning Model and GIS Based Algorithms for Vehicle Tracking

The latest advances in Deep Learning based methods and computational capabilities provide new opportunities for vehicle tracking. In this study, YOLOv2 (You Only Look Once—version 2) is used as an open source Convolutional Neural Network (CNN), to process high-resolution satellite images, in order to generate the spatio-temporal GIS (Geographic Information System) tracks of moving vehicles. At first step, YOLOv2 is trained with a set of images of 1024 × 1024 resolution from the VEDAI database. The model showed satisfactory results, with an accuracy of 91%, and then at second step, is used to process aerial images extracted from aerial video. The output vehicle bounding boxes have been processed and fed into the GIS based LinkTheDots algorithm, allowing vehicles identification and spatio-temporal tracks generation in GIS format.