Machine learning is the discipline of learning commands in the computer machine to predict and expect the results of real application and is currently the most promising simulation in artificial intelligence. This paper aims at using different algorithms to calculate and predict the compressive strength of extrusion 3DP concrete (cement mortar). The investigation is carried out using multi-objective grasshopper optimization algorithm (MOGOA) and artificial neural network (ANN). Given that the accuracy of a machine learning method depends on the number of data records, and for concrete 3D printing, this number is limited to few years of study, this work develops a new method by combining both methodologies into an ANNMOGOA approach to predict the compressive strength of 3D-printed concrete. Some promising results in the iteration process are achieved.
<p>Comparison of Different Algorithms and Vegetation Classes&#8217; Importance Ranking in Wildfire Susceptibility Maps.&#160;<br />Wildfire Susceptibility Maps (WSM) and the analysis of the explanatory variables affecting the model&#8217;s predictions are innovative tools to support forest protection and management plans. Namely, WSM identify areas subject to wildfire, in terms of relative spatial likelihood, on the base of the observed past events, stored in spatio-temporal inventories, and on the local environmental and anthropogenic properties of an area. Approaches based on Machine Learning (ML) are particularly suited for WSM since they are capable to make predictions on data by modelling the hidden and non-linear relationships between a set of input variables and the output observations.<br />In the present work, Authors continue a research framework developed at local scale for Liguria Region, and lately improved at national scale (Italy), consisting in the implementation of a ML-approach, based on the algorithm Random Forest, allowing to assess the susceptibility to wildfires under the influence of different variables (e.g., land cover, vegetation classes, altitude and its derivatives, nearby infrastructures). In the present study the following improvements are introduced: (i) to evaluate which ML-algorithm performs better in terms of prediction capabilities we compared Random Forest (RF), Multi-layer Perceptron (MLP), and Support Vector Machine (SVM); (ii) to evaluate the impact of different classes of local and neighbouring vegetation on wildfires occurrence we used of a more accurate map of vegetation as input local explanatory variable; (iii) to consider both the spatial and the temporal variability of the burning seasons (summer and winter) we improved the selection of the testing dataset, based on a clustering approach.&#160;<br />The output probabilistic predicted values resulting from the different ML-algorithms (RF, MLP, and SVM) allowed to elaborate the seasonal WSMs. Finally, the spatial distribution of the more susceptible areas will be presented. The performance of the three ML-algorithms was assessed by means of the AUC (Area Under the Curve) ROC (Receiver Operating Characteristics), evaluated over the testing dataset. In addition, the variable importance ranking was estimated as by-product of RF, which can handle both the typical numerical variables and native categorical variables (as for the classes of vegetation at pixel level). Vegetation resulted by far to be the most important explanatory variables; the marginal effect of each single class of vegetation was also assessed and the results will be discussed.&#160;<br />Reference&#160;<br />Trucchia, A.; Izadgoshasb, H.; Isnardi, S.; Fiorucci, P.; Tonini, M. Machine-Learning Applications in Geosciences: Comparison of Different Algorithms and Vegetation Classes&#8217; Importance Ranking in Wildfire Susceptibility. Geosciences 2022, 12, 424. https://doi.org/10.3390/geosciences12110424</p>
Susceptibility mapping represents a modern tool to support forest protection plans and to address fuel management. With the present work, we continue with a research framework developed in a pioneristic study at the local scale for Liguria (Italy) and recently adapted to the national scale. In these previous works, a random-forest-based modeling workflow was developed to assess susceptibility to wildfires under the influence of a number of environmental predictors. The main novelties and contributions of the present study are: (i) we compared models based on random forest, multi-layer perceptron, and support vector machine, to estimate their prediction capabilities; (ii) we used a more accurate vegetation map as predictor, allowing us to evaluate the impacts of different types of local and neighboring vegetation on wildfires’ occurrence; (iii) we improved the selection of the testing dataset, in order to take into account the temporal variability of the burning seasons. Wildfire susceptibility maps were finally created based on the output probabilistic predicted values from the three machine-learning algorithms. As revealed with random forest, vegetation is so far the most important predictor variable; the marginal effect of each type of vegetation was then evaluated and discussed.
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