Georeferenced diagnosis of drainage structures in the city of Bouaflé using geospatial techniques and machine learning

The traditional method of collecting and processing socio-economic, health and spatial identification data on urban water infrastructure is time-consuming and costly. To reduce costs, geospatial and machine learning (Random Forest) tools were used to establish the Bouaflé Drainage Master Plan. These tools made it possible to characterise and predict the condition of the hydraulic infrastructure. The thalwegs, extracted from altimetric data using connectivity algorithms, were cross-referenced with the road network to identify the collection points for individual structures. The theoretical points were verified in the field to draw up an inventory of the structures and assess their operating condition. Population density, elevations, and the distance of structures from roads and canals are among the variables included in the prediction model. Field data collection identified 102 crossing structures and 38 gutters over 13.69 km. The prediction model has a satisfactory accuracy of over 96%. The distance to the canals significantly impacts the accuracy of the model. Structures located far from the drainage network often fail due to poor hydraulic connectivity. The high impact of population density creates significant anthropogenic pressure on infrastructure. The algorithmic approach reduced the diagnostic phase from three months to one and a half, while also identifying problems and enabling solutions to be targeted more effectively. The trained model could be applied in similar contexts, even in the absence of data on the condition of structures.