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. The objective of this study is to suggest potential suitable disposal sites using fuzzy logic and analytical hierarchy process (fuzzy-AHP) method integrated with geographic information system (GIS) techniques. For this purpose, thirteen factors affecting the selection process were involved. The results showed that 5% of the studied area is considered extremely suitable and scattered in the central-eastern parts, while 9% is considered almost unsuitable and distributed in the northern and southern parts. Thereafter, these results were validated using the area under the curve (AUC) of the receiver operating characteristics (ROC). The AUC found was 57.1%, which is a moderate prediction's accuracy because the existing sites used in the validation's process were randomly selected. These results can assist relevant authorities and stakeholders for setting new solid waste disposal sites in Kenitra province.

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. ilex were identified, and at each site, measurements were taken for a set of 12 geo-environmental parameters including slope, aspect, elevation, geology, distance to stream, rainfall, slope length, plan curvature, profile curvature, erodibility, soil erosion, and land use/land cover. Subsequently, six machine learning algorithms were applied to model optimal areas for reforestation. In terms of models' performance, the results were compared, and the best were obtained by Bagging (area under the curve (AUC) = 0.98) and Naive Bayes (AUC = 0.97). Extremely favorable areas represent 8% and 17% of the study area according to Bagging and NB respectively, located to the west where geological unit of Bathonian-Bajocian with low erodibility index (K) and where rainfall varies between 250 and 300 mm/year. This work provides a roadmap for decision-makers to increase the chances of successful reforestation at lower cost and in less time.

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. Remote sensing data was also used to monitor the watermelon cultivation expansion. Results show a rapid expansion of agricultural areas from just 185.11 ha in 2007 to 2560.1 ha in 2018. The groundwater level declined rapidly by about 10 m below ground level during the 5 years of the study period. Additionally, the decline was accompanied by a significant increase in electrical conductivity (salinity) values over the same time interval from 1077.55 to 1211.9 µS/cm. As a consequence of the continuous overexploitation and unsustainable management, a lot of wells have run dry and there have been drinking water shortages in the city of Zagora, the closest city nearby. Results can help target efforts to improve the implementation of conservation strategies to ensure the sustainability of water use and food production in this region of Morocco.