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Monitoring and understanding Land Use/Land Cover (LU/LC) is critical for sustainable development, as it can impact various environmental, social, and economic systems. For example, deforestation and land degradation can lead to soil erosion, loss of biodiversity, and greenhouse gas emissions, affecting the quality of soil, air, and water resources. The present research examined changes in (LU/LC) within the underdeveloped regions of Balochistan and Sindh provinces, which are situated in Pakistan. In order to monitor temporal variations of LU/LC, we employed Geographic Information System (GIS) technique, to conduct an analysis of satellite imagery obtained from the Landsat 8 Operational Land Imager (OLI) during the time period spanning from 2013 to 2023. In order to obtain an accurate LU/LC classification, we used principal component analysis (PCA) and a supervised classification approach using the maximum likelihood algorithm (MLC). According to the results of our study, there was a decrease in the extent of water bodies (− 593.24 km2) and vegetation (− 68.50 km2) by − 3.43% and − 0.40% respectively. In contrast, the area occupied by settlements in the investigated region had a 2.23% rise, reaching a total of 385.66 square kilometers. Similarly, the extent of barren land also expanded by 1.60%, encompassing a total area of 276.04 square kilometers, during the course of the last decade. The overall accuracy (94.25% and 95.75%) and K value (91.75% and 93.50%) were achieved during the year 2013 and 2023 respectively. The enhancement of agricultural output in Pakistan is of utmost importance in order to improve the income of farmers, mitigate food scarcity, stimulate economic growth, and facilitate the expansion of exports. To enhance agricultural productivity, it is recommended that the government undertake targeted initiatives that aimed at enhancing water infrastructure and optimizing land use to foster a sustainable ecological framework. Integrating the sustainable ecological framework provides a foundation for informed decision-making and effective resource management. By identifying areas of urban expansion, agricultural intensification, or alterations in natural LU/LC, stakeholders can design targeted conservation strategies, mitigating potential environmental degradation and promoting biodiversity conservation. In conclusion, the integration of GIS and Remote Sensing (RS) may effectively facilitate the monitoring of land use patterns over a period of time. This combined approach offers valuable insights and recommendations for the judicious and optimal management of land resources, as well as informing policy decisions. [ABSTRACT FROM AUTHOR]

Wind energy is critical to traditional energy sources replacement in France and throughout the world. Wind energy generation in France is quite unevenly spread across the country. Despite its considerable wind potential, the research region is among the least productive. The region is a very complicated location where socio-environmental, technological, and topographical restrictions intersect, which is why energy production planning studies in this area have been delayed. In this research, the methodology used for identifying appropriate sites for future wind farms in this region combines GIS with MCDA approaches such as AHP. Six determining factors are selected: the average wind speed, which has a weight of 38%; the protected areas, which have a relative weight of 26%; the distance to electrical substations and road networks, both of which have a significant influence on relative weights of 13%; and finally, the slope and elevation, which have weights of 5% and 3%, respectively. Only one alternative was investigated (suitable and unsuitable). The spatial database was generated using ArcGIS and QGIS software; the AHP was computed using Excel; and several treatments, such as raster data categorization and weighted overlay, were automated using the Python programming language. The regions identified for wind turbines installation are defined by a total of 962,612 pixels, which cover a total of 651 km2 and represent around 6.98% of the research area. The theoretical wind potential calculation results suggest that for at least one site with an area bigger than 400 ha, the energy output ranges between 182.60 and 280.20 MW. The planned sites appear to be suitable; each site can support an average installed capacity of 45 MW. This energy benefit will fulfill the region's population's transportation, heating, and electrical demands. [ABSTRACT FROM AUTHOR]

Drought is a complex and devastating natural disaster that needs to be constantly investigated. In this study, standardized precipitation indexes (SPI-3 and SPI-6) were computed using daily precipitation data collected from six meteorological stations in a Mediterranean coastal basin (Northwestern Morocco) during the period from 1984 to 2021. Subsequently, we examined the spatio-temporal distribution of three agricultural indices namely Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). Additionally, to predict SPI at two scales (SPI-3 and SPI-6), we investigated the ability of four machine learning (ML) models such as random subspace, random forest, M5P, and REPTree. ML model's performance was evaluated using statistical metrics such as R2, MAE, RMSE, RAE, and RRSE. As per SPI results, 1995, 1999, 2005, 2015, and 2017 were observed as severe driest years. Agricultural drought' magnitude differs over time and space from 1984 to 2021. Besides, findings showed that the REPTree model achieved the best performance during the testing and validation phases, with R2 (0.64–0.85), MAE (0.37–0.58), RMSE (0.51–0.74), RAE (48.10–75.34%) and RRSE (53.24–76.68%). In contrast, RF was found to offer the lowest performance accuracy, despite outperforming during the training phase. Moreover, SPI-6 has higher prediction accuracy than SPI-3. Furthermore, our findings offer a reliable model for drought prediction, which may further assist policymakers and authorities in developing better adaptation and mitigation strategies to reduce drought-related losses. In future research, we suggest exploring alternative ensemble or hybrid ML algorithms to further improve prediction accuracy and capability. • Agricultural and meteorological droughts were assessed using different indexes (VCI, TCI, VHI, SPI-3 and SPI-6). • Investigation of four machine learning abilities (RSS, RF, M5P, and REPTree) to predict SPI-3 and SPI-6. • Agricultural drought' magnitude differs over time and space from 1984 to 2021. • REPTree is a suitable model for predicting drought in a Mediterranean watershed. [ABSTRACT FROM AUTHOR]

The Senegal-Mauritanian Basin onshore is the largest among the northwest African Atlantic margin basins, covering approximately 350,000 km2 and extending from Morocco to Guinea Bissau. Although valuable information is provided on the Senegalese part, data on other parts of the basin are scarce. The study aims to visualise the subsurface structure and describe the distribution patterns of Meso-Cenozoic deposits in the Moroccan portion of the Senegalo-Mauritania basin. The dataset integrates information from 750 vertical electrical soundings, hydrogeological drilling data, and wireline logging into a Geographic Information System (GIS) platform and 3D modelling software. A new three-dimensional model has been created to represent the topmost layer of the Palaeozoic substratum. Additionally, thickness maps and geo-electric profiles have been developed for distinct geo-electric intervals, including the lower, middle, upper, and surface layers. The study presents the geometric properties of two aquifers, namely, the ''upper and lower'': the Continental Terminal sandy and sandstones, dating back to the upper Miocene to Pliocene period, and the Paleocene sand to shaly sand. The upper aquifer has a thickness of 20 m in the eastern region, whereas it thickens to nearly 100 m towards the west, approaching the coastline. Similarly, the lower aquifer's thickness ranges from 100 m to 400 m in the same direction. The resistivity values range from 5 to 240 Ω m, providing an average of 120 Ω m. Four discontinuities have been identified in the sections, which may indicate lateral facies modifications or synsedimentary faults, correlating with Atlantic margin structuration and Meso-Cenozoic sea-level variations. The parameters' range for various mappings, models, and quantitative descriptions of reservoir properties. The new structural and hydro-stratigraphic model provides novel insights for researching this critical area. • Investigated the Tertiary aquifer system in Morocco's Bir Guendouz area • Use of a set of technique based on data analysis and interpretation from various sources, particularly geophysical data • Identify two significant hydrogeological potential aquifer layers, namely R1 and R2 • Demonstrate a gentle east-west dip in the upper aquifer ceiling and a more pronounced deepening in the same direction in the lower aquifer • Provided valuable data for decision-making and future water resource development projects in Morocco's southern region, as well as transboundary water management and development. [ABSTRACT FROM AUTHOR]