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2 2024

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Assessment of Torrential Correction Thresholds on Ourika Sub-Watershed of the Tensift Watershed (High Atlas of Marrakech, Morocco)

L’Arfouni Ilham, Algouti Ahmed, Algouti Abdella, Es-Sadiq Rachid

Ecological Engineering & Environmental Technology

2024

Vol. 25, iss. 7

171--185

The Ourika sub-watershed is composed of about twenty different watersheds with diverse lithology, slope, and structural organization. In order to better characterize the basin, we inventoried and extensively assessed the different types of thresholds implemented in each micro-watershed. The present study focused on the area located between Meltsen and Sidi Ali Oufarés faults, which includes several micro-watersheds that have been modified by the installation of structures. We selected 12 micro-watersheds from the main tributaries draining this zone, based on the level of risk: four micro-watersheds on the right bank from upstream to downstream (Wigrane and Walighane, Tachmacht, and Touggalkhir), and eight micro-watersheds on the left bank from upstream to downstream (Imintaddarte, Oussane, Tikhfert, Tighazrit, Igri Foudene, Asni, Taljarft, and Tarzaza). The results of our study allowed us to detect and inventory 545 erosion protection structures made of masonry, gabions, and dry stone. However, the majority of these structures were damaged in several micro-watersheds due to steep slopes, torrential rainfall, and especially the solid sediment load resulting from the erosion of easily erodible old alluvial cones. This study serves as a warning to various stakeholders and decision-makers to ensure proper management in this mountainous system. The distribution of these thresholds is as follows: 62 masonry thresholds, accounting for 13.37%; 247 gabion thresholds, accounting for 45.32%; and 236 dry stone thresholds, accounting for 43.30%. The assessment of these structures revealed anomalies such as the loss of 17.43% of embankment structures and the destruction of certain thresholds.

Analysis of Soil Water Erosion Risk Using Machine Learning Technics – A Case Study of Ourika Watershed in Morocco

L’Arfouni Ilham, Algouti Ahmed, Algouti Abdellah, Es-Sadiq Rachid

Ecological Engineering & Environmental Technology

2024

Vol. 25, iss. 10

324--338

Soil erosion is a major environmental problem with detrimental consequences. In this article, we present a detailed study on the analysis of soil water erosion using machine learning (ML) techniques in the Oued Ourika watershed in Morocco. We collected data on various factors that may influence the mechanisms of soil water erosion events. Subsequently, we developed machine learning models to predict the potential for soil water erosion based on these factors. Finally, field studies were conducted compared to the obtained results. A historical inventory of water erosion has been created through fieldwork, satellite imagery, and historical water erosion events. Models were constructed using the training data, and their performance and accuracy in predicting susceptibility to water erosion were evaluated using the validation data. This data division allowed for a fair assessment of the models’ ability to make accurate predictions. Using a Geographic Information System (GIS) and programming in the R language, four supervised machine learning algorithms were applied, including k-nearest neighbor (KNN), extreme gradient boosting (XGB), random forest (RF), and naive bayes (NB). The results show that the NB model exhibited the highest accuracy in predicting and evaluating the effectiveness of these algorithms in forecasting susceptibility to water erosion in the study area. Accuracy was assessed using the area under the curve (AUC) metric, with an AUC of 98%. The XGB algorithm had an AUC of 96%, followed by RF with an AUC of 87%, and KNN with an AUC of 84%. Thus, the Naive Bayes model proved to be the best for determining susceptibility to water erosion in the study area. The analysis of water erosion reveals that 43% of the total area of the Oued Ourika watershed is exposed to a high to very high risk of erosion in the Ourika region. These findings can assist regional and local authorities in reducing the risk of water erosion and implementing effective measures to prevent potential damages. The goal is to protect the communities and infrastructure located along the course of the Ourika. Overall analysis of natural disasters, the accuracy of the results heavily depends on the availability and quality of data, which must encompass an adequate number of parameters.