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Characterisation of basic water balance parameters of Debrecen
35-40Views:180This work aims to develop a hydrological modelling tool to help managers make the right decisions for Debrecen, in the face of water scarcity and the increase in agricultural and domestic needs over time. The methodology was based on the creation of a climatic database, at monthly time steps, from 2016 to 2019, and cartographic (land use, digital elevation model, and hydrological network). As a next step, the watershed was delimitated into sub-basins to determine the shape and the physiographic characteristics of sub-watersheds. Finally, a hydrological study was prepared by calculating the time of concentration to build a database of water resources in the study area. This water resource will be used as an input parameter for urban farming.
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An advanced classification method for urban land cover classification
51-57Views:59This manuscript presents a detailed comparative analysis of three advanced classification techniques that were used between 2018 and 2020 to classify land cover using Landsat8 imagery, namely Support Vector Machine (SVM), Maximum Likelihood Classification (MLSC), and Random Forests (RF). The study focuses on evaluating the accuracy of these methods by comparing the classified maps with a higher-resolution ground truth map, utilising 500 randomly selected points for assessment.
The obtained results show that, compared to MLSC and RT, the Support Vector Machine (SVM) approach performs better. The SVM model demonstrates enhanced precision in land cover classification, showcasing its effectiveness in discerning subtle differences in landscape features.
Furthermore, using the precise classification results produced by the SVM method, this study examines the temporal variations in land cover between 2018 and 2020. The results provide insight into dynamic land cover changes and highlight the significance of applying reliable classification techniques for thorough temporal analysis with Landsat8 images.