Integrating fuzzy-AHP and GIS for solid waste disposal site selection in Kenitra province, NW Morocco.

Selecting an optimal solid waste disposal site is one of the decisive waste management issues because unsuitable sites cause serious environmental and public health problems. In Kenitra province, northwest Morocco, sustainable disposal sites have become a major challenge due to rapid urbanization and population growth. In addition, the existing disposal sites are traditional and inappropriate.

Predicting potential reforestation areas by Quercus ilex (L.) species using machine learning algorithms: case of upper Ziz, southeastern Morocco.

The selection of appropriate areas for reforestation remains a complex task because of influence by several factors, which requires the use of new techniques. Based on the accurate outcomes obtained through machine learning in prior investigations, the current study evaluates the capacities of six machine learning techniques (MLT) for delineating optimal areas for reforestation purposes specifically targeting Quercus ilex, an important local species to protect soil and water in upper Ziz, southeast Morocco. In the initial phase, the remaining stands of Q.

Monitoring spatiotemporal variation of groundwater level and salinity under land use change using integrated field measurements, GIS, geostatistical, and remote-sensing approach: case study of the Feija aquifer, Middle Draa watershed, Moroccan Sahara.

The cultivation of watermelons has been a fast growing agriculture industry in the arid, desert regions of Morocco, relying on groundwater pumping and transformation of rangelands to farms due to growing demand for the fruit in national and international markets. This study aims to measure the impact of watermelon expansion on groundwater resources in the Feija Basin, which is one of the largest watermelon cultivation areas in Southern Morocco. Field measurements, statistics, Kriging interpolation, and regression methods were used to measure the temporal variations in the groundwater level (GL) and salinity between 2013 and 2018 to determine the correlation between different parameters.