Browsing by Subject "Hyperparameter optimisation"

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    Analysis of the hyperparameter optimisation of four machine learning satellite imagery classification methods
    (Springer, 2024-04-05) Alonso Sarría, Francisco; Valdivieso Ros, Carmen; Gomariz Castillo, Francisco; Geografía
    The classification of land use and land cover (LULC) from remotely sensed imagery in semi-arid Mediterranean areas is a challenging task due to the fragmentation of the landscape and the diversity of spatial patterns. Recently, the use of deep learning (DL) for image analysis has increased compared to commonly used machine learning (ML) methods. This paper compares the performance of four algorithms, Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Convolutional Network (CNN), using multi-source data, applying an exhaustive optimisation process of the hyperparameters. The usual approach in the optimisation process of a LULC classification model is to keep the best model in terms of accuracy without analysing the rest of the results. In this study, we have analysed such results, discovering noteworthy patterns in a space defined by the mean and standard deviation of the validation accuracy estimated in a 10-fold cross validation (CV). The point distributions in such a space do not appear to be completely random, but show clusters of points that facilitate the discovery of hyperparameter values that tend to increase the mean accuracy and decrease its standard deviation. RF is not the most accurate model, but it is the less sensitive to changes in hyperparameters. Neural Networks, tend to increase commission and omission errors of the less represented classes because their optimisation lead the model to learn better the most frequent classes. On the other hand, RF and MLP prediction layers are the most accurate from a general qualitative point of view.
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    Open Access
    Estimation of soil properties using machine learning techniques to improve hydrological modeling in a semiarid environment: Campo de Cartagena (Spain)
    (Springer, 2025-03-11) Alonso Sarria, Francisco; Blanco Bernardeau, Arantzazu; Gomariz Castillo, Francisco; Romero Díaz, María Asunción; Geografía
    Soils are a key element in the hydrological cycle through a number of soil properties that are complex to estimate and exhibit considerable spatial variability. Therefore, several techniques have been proposed for their estimation and mapping from point data along a given study area. In this work, four machine learning methods: Random Forest, Support Vector Machines, XGBoost and Multilayer Perceptrons, are used to predict and map the proportions of organic carbon, clay, silt and sand in the soils of the Campo de Cartagena (SE Spain). These models depend on a number of hyperparameters that need to be optimised to maximise accuracy, although this process can lead to overtraining, which affects the generalisability of the models. In this work it was found that neural networks gave the best results in validation, but on the test data the methods based on decision trees, random forest and xgboost were more accurate, although the differences were generally not significant. Accuracy values, as usual for soil variables, were not high. The RMSE values were 8.040 for SOC, 7.049 for clay, 10.227 for silt and 13.561 for loam. The layers obtained were then used to obtain annual curve number layers whose ability to reproduce runoff hydrographs was compared with the official CN layer. For high flow events, the CN layers obtained in this study gave better results (NSE=0.807, PBIAS=-4.7 and RMSE=0.4) than the official CN layers (NSE=-2.28, PBIAS=135.82 and RMSE=1.8).