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.

Micro-costing from healthcare professional's perspective and acceptability of cutaneous leishmaniasis diagnostic tools in Morocco: A mixed-methods study.

In Morocco, cutaneous leishmaniasis (CL) represents a concern with three causative parasite species. Despite this, rapid diagnostic test (RDT) for CL is not part of the national control strategy. This study evaluates the acceptability and micro-costing of the CL Detect Rapid Test by Inbios International. The study was conducted from June 2019 to January 2020 and included 46 healthcare professionals from 40 primary healthcare centers and district labs. Data was collected by self-administered questionnaires and interviews and analysed using NVivo, Jamovi, and Python to generate a predictive model and sensitivity analysis by calculating the average Cost-Benefit Ratio for compared CL diagnostic scenarios. The exchange rate is 1 USD = 9.6 MAD (Moroccan Dirham) in November 2019. The CL-RDT received notable acceptance for its user-friendliness and time efficiency compared to traditional microscopy. Micro-costing data revealed that the average unit cost for microscopy is 15 MAD [7-31], whereas 75 MAD [52-131] for CL-RDT. Altogether, the diagnostic cost for microscopy is 115 MAD±4, marginally higher than the 102 MAD±2 for CL-RDT (p = 0,05). Sensitivity analysis identified the most cost-benefit scenarios based on a Cost-Benefit Ratio (CBR). The optimal approach involves using CL-RDT once at a primary healthcare centre (PHC) (CBR = 1.4), especially if the unitary cost is below 79 MAD. The second-best option is using CL-RDT once at a laboratory (CBR = 1.0), which is advantageous if priced under 54 MAD. However, using CL-RDT twice for the same lesion had a less favourable CBR of 0.6 and was only beneficial if priced below 09 MAD. The reference scenario was a single CL-RDT at the PHC followed by microscopy at a laboratory. In conclusion, the forthcoming CL-RDT, expected to feature enhanced sensitivity, is advocated for deployment in resource-limited endemic areas.

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.