Browsing by Author "Kacimi, Ilias"
Now showing 1 - 5 of 5
Results Per Page
Sort Options
- Contribution of remote sensing technologies to a holistic coastal and marine environmental management framework: a reviewPublication . El Mahrad, Badr; Newton, Alice; Icely, John; Kacimi, Ilias; Abalansa, Samuel; Snoussi, MariaCoastal and marine management require the evaluation of multiple environmental threats and issues. However, there are gaps in the necessary data and poor access or dissemination of existing data in many countries around the world. This research identifies how remote sensing can contribute to filling these gaps so that environmental agencies, such as the United Nations Environmental Programme, European Environmental Agency, and International Union for Conservation of Nature, can better implement environmental directives in a cost-e ective manner. Remote sensing (RS) techniques generally allow for uniform data collection, with common acquisition and reporting methods, across large areas. Furthermore, these datasets are sometimes open-source, mainly when governments finance satellite missions. Some of these data can be used in holistic, coastal and marine environmental management frameworks, such as the DAPSI(W)R(M) framework (Drivers–Activities–Pressures–State changes–Impacts (on Welfare)–Responses (as Measures), an updated version of Drivers–Pressures–State–Impact–Responses. The framework is a useful and holistic problem-structuring framework that can be used to assess the causes, consequences, and responses to change in the marine environment. Six broad classifications of remote data collection technologies are reviewed for their potential contribution to integrated marine management, including Satellite-based Remote Sensing, Aerial Remote Sensing, Unmanned Aerial Vehicles, Unmanned Surface Vehicles, Unmanned Underwater Vehicles, and Static Sensors. A significant outcome of this study is practical inputs into each component of the DAPSI(W)R(M) framework. The RS applications are not expected to be all-inclusive; rather, they provide insight into the current use of the framework as a foundation for developing further holistic resource technologies for management strategies in the future. A significant outcome of this research will deliver practical insights for integrated coastal and marine management and demonstrate the usefulness of RS to support the implementation of environmental goals, descriptors, targets, and policies, such as theWater Framework Directive, Marine Strategy Framework Directive, Ocean Health Index, and United Nations Sustainable Development Goals. Additionally, the opportunities and challenges of these technologies are discussed.
- Differentiation of multi-parametric groups of groundwater bodies through discriminant analysis and machine LearningPublication . Mohsine, Ismail; Kacimi, Ilias; Valles, Vincent; Leblanc, Marc; El Mahrad, Badr; Dassonville, Fabrice; Kassou, Nadia; Bouramtane, Tarik; Abraham, Shiny; Touiouine, Abdessamad; Jabrane, Meryem; Touzani, Meryem; Barry, Abdoul Azize; Yameogo, Suzanne; Barbiero, LaurentIn order to facilitate the monitoring of groundwater quality in France, the groundwater bodies (GWB) in the Provence-Alpes-Cote d'Azur region have been grouped into 11 homogeneous clusters on the basis of their physico-chemical and bacteriological characteristics. This study aims to test the legitimacy of this grouping by predicting whether water samples belong to a given sampling point, GWB or group of GWBs. To this end, 8673 observations and 18 parameters were extracted from the Size-Eaux database, and this dataset was processed using discriminant analysis and various machine learning algorithms. The results indicate an accuracy of 67% using linear discriminant analysis and 69 to 83% using ML algorithms, while quadratic discriminant analysis underperforms in comparison, yielding a less accurate prediction of 59%. The importance of each parameter in the prediction was assessed using an approach combining recursive feature elimination (RFE) techniques and random forest feature importance (RFFI). Major ions show high spatial range and play the main role in discrimination, while trace elements and bacteriological parameters of high local and/or temporal variability only play a minor role. The disparity of the results according to the characteristics of the GWB groups (geography, altitude, lithology, etc.) is discussed. Validating the grouping of GWBs will enable monitoring and surveillance strategies to be redirected on the basis of fewer, homogeneous hydrogeological units, in order to optimize sustainable management of the resource by the health agencies.
- Exploring multiscale variability in groundwater quality: a comparative analysis of spatial and temporal patterns via clusteringPublication . Mohsine, Ismail; Kacimi, Ilias; Abraham, Shiny; Valles, Vincent; Barbiero, Laurent; Dassonville, Fabrice; Bahaj, Tarik; Kassou, Nadia; Touiouine, Abdessamad; Jabrane, Meryem; Touzani, Meryem; El Mahrad, Badr; Bouramtane, TarikDefining homogeneous units to optimize the monitoring and management of groundwater is a key challenge for organizations responsible for the protection of water for human consumption. However, the number of groundwater bodies (GWBs) is too large for targeted monitoring and recommendations. This study, carried out in the Provence-Alpes-Cote d'Azur region of France, is based on the intersection of two databases, one grouping together the physicochemical and bacteriological analyses of water and the other delimiting the boundaries of groundwater bodies. The extracted dataset contains 8627 measurements from 1143 observation points distributed over 63 GWB. Data conditioning through logarithmic transformation, dimensional reduction through principal component analysis, and hierarchical classification allows the grouping of GWBs into 11 homogeneous clusters. The fractions of unexplained variance (FUV) and ANOVA R-2 were calculated to assess the performance of the method at each scale. For example, for the total dissolved load (TDS) parameter, the temporal variance was quantified at 0.36 and the clustering causes a loss of information with an R-2 going from 0.63 to 0.4 from the scale of the sampling point to that of the GWB cluster. The results show that the logarithmic transformation reduces the effect of outliers and improves the quality of the GWB clustering. The groups of GWBs are homogeneous and clearly distinguishable from each other. The results can be used to define specific management and protection strategies for each group. The study also highlights the need to take into account the temporal variability of groundwater quality when implementing monitoring and management programs.
- Mapping the pollution plume using the self-potential geophysical method: case of Oum Azza Landfill, Rabat, MoroccoPublication . Touzani, Meryem; Mohsine, Ismail; Ouardi, Jamila; Kacimi, Ilias; Morarech, Moad; El Bahajji, Mohamed Habib; Bouramtane, Tarik; Tiouiouine, Abdessamad; Yameogo, Suzanne; El Mahrad, BadrThe main landfill in the city of Rabat (Morocco) is based on sandy material containing the shallow Mio-Pliocene aquifer. The presence of a pollution plume is likely, but its extent is not known. Measurements of spontaneous potential (SP) from the soil surface were cross-referenced with direct measurements of the water table and leachates (pH, redox potential, electrical conductivity) according to the available accesses, as well as with an analysis of the landscape and the water table flows. With a few precautions during data acquisition on this resistive terrain, the results made it possible to separate the electrokinetic (~30%) and electrochemical (~70%) components responsible for the range of potentials observed (70 mV). The plume is detected in the hydrogeological downstream of the discharge, but is captured by the natural drainage network and does not extend further under the hills.
- Social-environmental analysis for the management of coastal lagoons in North AfricaPublication . El Mahrad, Badr; Abalansa, Samuel; Newton, Alice; Icely, John; Snoussi, Maria; Kacimi, IliasThis study provides an overview of 11 lagoons in North Africa, from the Atlantic to the Eastern Mediterranean. Lagoons are complex, transitional, coastal zones providing valuable ecosystem services that contribute to the welfare of the human population. The main economic sectors in the lagoons included fishing, shellfish harvesting, and salt and sand extraction, as well as maritime transport. Economic sectors in the areas around the lagoons and in the watershed included agriculture, tourism, recreation, industrial, and urban development. Changes were also identified in land use from reclamation, changes in hydrology, changes in sedimentology from damming, inlet modifications, and coastal engineering. The human activities in and around the lagoons exert multiple pressures on these ecosystems and result in changes in the environment, affecting salinity, dissolved oxygen, and erosion; changes in the ecology, such as loss of biodiversity; and changes in the delivery of valuable ecosystem services. Loss of ecosystem services such as coastal protection and seafood affect human populations that live around the lagoons and depend on them for their livelihood. Adaptive management frameworks for social–ecological systems provide options that support decision makers with sciencebased knowledge to deliver sustainable development for ecosystems. The framework used to support the decision makers for environmental management of these 11 lagoons is Drivers–Activities–Pressures–State Change–Impact (on Welfare)–Responses (as Measures).