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Groundwater pollution poses a severe threat and issue to the environment and humanity overall. That is why mitigative strategies are urgently needed. Today, studies mapping groundwater risk pollution assessment are being developed. In this study, five new hybrid/ensemble machine learning (ML) models are developed, named DRASTIC-Random Forest (RF), DRASTIC-Support Vector Machine (SVM), DRASTIC-Multilayer Perceptron (MLP), DRASTIC-RF-SVM, and DRASTIC-RF-MLP, for groundwater pollution assessment in the Saiss basin, in Morocco. The performances of these models are evaluated using the Receiver Operating Characteristic curve (ROC curve), precision, and accuracy. Based on the results of the ROC curve method, it is indicated that the use of hybrid/ensemble machine learning (ML) models improves the performance of the individual machine learning (ML) algorithms. In effect, the AUC value of the original DRASTIC is 0.51. Furthermore, both hybrid/ensemble models, DRASTIC-RF-MLP (AUC = 0.953) and DRASTIC-RF-SVM, (AUC = 0.901) achieve the best accuracy among the other models, followed by DRASTIC-RF (AUC = 0.852), DRASTIC-SVM (AUC = 0.802), and DRASTIC-MLP (AUC = 0.763). The results delineate areas vulnerable to pollution, which require urgent actions and strategies to improve the environmental and social qualities for the local population. [ABSTRACT FROM AUTHOR]

Groundwater is a most important resource in arid and semi-arid regions and is required for drinking, irrigation and industrialization. Assessing the potential zone of groundwater recharge is extremely crucial for the protection of water quality and the management of groundwater systems. To identify the groundwater potential zone in the study area, thematic layers of lithology, slope, karst degrees, land cover, lineament and drainage density were generated using topographic maps, thematic maps, field data and satellite image, and were prepared, classified, weighted and integrated in a geographic information system (GIS) environment by the means of fuzzy logic. The fuzzy membership values have been assigned to different thematic layers according to their classification on respect for their contribution and their occurrence in groundwater. Based on the generated groundwater potential map, it was found that about 8% of the investigation area was categorized as very high potential for groundwater recharge, 31% as high, 28% as moderate, 17% as low and 16% as very low potential for groundwater recharge. Finally, the results were verified using well-yield data. The highest recharge potential area is located towards the downstream regions related to more fractured and karstified limestone. [ABSTRACT FROM AUTHOR]

The studies of forest ecosystems from remotely-sensed data are of great interest to researchers because of ecosystem services provided by this ecosystem, including protection of soils and vegetation, climate stabilization, and regulation of the hydrological cycle. In this context, our study focused on the use of a spectral angle mapper (SAM) classification method for mapping species in the Azrou Forest, Central Middle Atlas, Morocco. A Sentinel-2A image combined with ground reference data were used in this research. Four classes (holm oak, cedar forest, bare soil, and others-unclassified) were selected; they represent, respectively, 27, 11, 24, and 38% of the study area. The overall accuracy of classification was estimated to be around 99.72%. This work explored the potential of the SAM classification combined with Sentinel-2A data for mapping land cover in the Azrou Forest ecosystem. [ABSTRACT FROM AUTHOR]

Gully erosion is a serious threat to the state of ecosystems all around the world. As a result, safeguarding the soil for our own benefit and from our own actions is a must for guaranteeing the long-term viability of a variety of ecosystem services. As a result, developing gully erosion susceptibility maps (GESM) is both suggested and necessary. In this study, we compared the effectiveness of three hybrid machine learning (ML) algorithms with the bivariate statistical index frequency ratio (FR), named random forest-frequency ratio (RF-FR), support vector machine-frequency ratio (SVM-FR), and naïve Bayes-frequency ratio (NB-FR), in mapping gully erosion in the GHISS watershed in the northern part of Morocco. The models were implemented based on the inventory mapping of a total number of 178 gully erosion points randomly divided into 2 groups (70% of points were used for training the models and 30% of points were used for the validation process), and 12 conditioning variables (i.e., elevation, slope, aspect, plane curvature, topographic moisture index (TWI), stream power index (SPI), precipitation, distance to road, distance to stream, drainage density, land use, and lithology). Using the equal interval reclassification method, the spatial distribution of gully erosion was categorized into five different classes, including very high, high, moderate, low, and very low. Our results showed that the very high susceptibility classes derived using RF-FR, SVM-FR, and NB-FR models covered 25.98%, 22.62%, and 27.10% of the total area, respectively. The area under the receiver (AUC) operating characteristic curve, precision, and accuracy were employed to evaluate the performance of these models. Based on the receiver operating characteristic (ROC), the results showed that the RF-FR achieved the best performance (AUC = 0.91), followed by SVM-FR (AUC = 0.87), and then NB-FR (AUC = 0.82), respectively. Our contribution, in line with the Sustainable Development Goals (SDGs), plays a crucial role for understanding and identifying the issue of "where and why" gully erosion occurs, and hence it can serve as a first pathway to reducing gully erosion in this particular area. [ABSTRACT FROM AUTHOR]

A geophysical study carried out in the framework of the water resources recognition within the Essaouira coastal area (Morocco) enabled to outline the spatial distribution of the Plio-Pleistocene and Cretaceous formations. It helped to determine the thickness and extension of the main aquifer used for irrigation purpose. The overall aim of this study is to highlight the hydrogeological structures of Essaouira Basin. A total of 45 vertical electrical soundings (VES) were measured using linear Schlumberger array configuration with a maximum half-length (AB/2) of 1000 m along eight sections of 126 km total length. The number of soundings constituting the eight VES profiles depends on the topography and the obstacles encountered. The VES data were processed and interpreted. According to the thematic maps of the study area, the results indicate the existence of three types of VES with apparent resistivity values ranging from 44 to 320 Ω m. In relationship with wells and mechanical drillings existing in the area, the analysis of the results obtained made it possible to derive from the quantitative and qualitative maps the different apparent resistivity variations of the aquifer and aquiclude geological layers. The careful examination of these maps shows that the region around the Qsob River is of interest from a hydrogeological point of view. It is represented by a significant thick layer given the large extension of the resistive Plio-Pleistocene and Cretaceous formations. Geophysical studies show that this region is partially protected from marine intrusion given its location between Tidzi Diapir and hidden diapir from Essaouira which little plays the role of barrier. It is crossed by the Qsob River which lends itself as the main source of both these layers. The electrical discontinuities emerging from the superposition of the different maps, E-W, NE-SW and NNE-SSW oriented, are abundant and dense in the north Haha region (Qsob River). Generally, the salt water circulates along these discontinuities from West to North and Northeast which explains the abundance of low apparent resistivity values recorded northeastwards. This geophysical reconnaissance is the most important for water supply of this area affected by increasingly long periods of drought. • Vertical Electrical Soundings carried out on 45 sites in the Essaouira coastal area, enabled to outline the spatial subsurface distribution of Plio-Pleistocene and Cretaceous aquifers. • Aquifers exhibit apparent resistivity and thickness values from 44 to 320 Ω m and from 15 to 56 m, respectively. • Qsob River area shows a thick layer of resistive Plio-Pleistocene and Cretaceous formations. • Isopach and isobath maps of the aquifers confirm their spatial distribution regarding saltwater intrusions. • Major faulting system, NNE-SSW, NNW-SSE and E-W oriented, accommodating small grabens, controls the aquifers. [ABSTRACT FROM AUTHOR]

• Five new ensemble models were developed for forest fire susceptibility modeling. • 10 conditioning factors were considered in this study. • RF-FR ensemble model outperformed SVM-FR, MLP-FR, CART-FR and LR-FR models. Forest fire disaster is currently the subject of intense research worldwide. The development of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous events as much as possible requires modeling and forecasting severe conditions. In this study, we developed five new hybrid machine learning algorithms namely, Frequency Ratio-Multilayer Perceptron (FR-MLP), Frequency Ratio-Logistic Regression (FR-LR), Frequency Ratio-Classification and Regression Tree (FR-CART), Frequency Ratio-Support Vector Machine (FR-SVM), and Frequency Ratio-Random Forest (FR-RF), for mapping forest fire susceptibility in the north of Morocco. To this end, a total of 510 points of historic forest fires as the forest fire inventory map and 10 independent causal factors including elevation, slope, aspect, distance to roads, distance to residential areas, land use, normalized difference vegetation index (NDVI), rainfall, temperature, and wind speed were used. The area under the receiver operating characteristics (ROC) curves (AUC) was computed to assess the effectiveness of the models. The results of conducting proposed models indicated that RF-FR achieved the highest performance (AUC = 0.989), followed by SVM-FR (AUC = 0.959), MLP-FR (AUC = 0.858), CART-FR (AUC = 0.847), LR-FR (AUC = 0.809) in the forecasting of the forest fire. The outcome of this research as a prediction map of forest fire risk areas can provide crucial support for the management of Mediterranean forest ecosystems. Moreover, the results demonstrate that these novel developed hybrid models can increase the accuracy and performance of forest fire susceptibility studies and the approach can be applied to other areas. [ABSTRACT FROM AUTHOR]