We acknowledge that the modelling assumption
Comparison between different spatial interpolation methods for the development of sediment distribution maps in coastal areas Sediment grain size and its spatial distribution is a very important aspect for many applications and processes that occur in the coastal zone. One of these is coastal erosion which is strongly dependent on sediment distribution and transportation. To highlight this fact, surficial coastal sediments were collected from a densely populated coastal zone in Western Greece, which suffers extensive erosion, and grain size distribution was thoroughly analysed, to predict the spatial distribution of the median grain size diameter (D50) and produce sediment distribution maps. Four different geostatistical interpolation techniques (Ordinary Kriging, Simple Kriging, Empirical Bayesian Kriging and Universal Kriging) and three deterministic (Radial Basis Function, Local Polynomial Interpolation, and Inverse Distance Weighting) were employed for the construction of the respective surficial sediment distribution maps with the use of GIS. Moreover, a comparative study between the deterministic and geostatistical approaches was applied and the performance of each interpolation method was evaluated using cross-validation and estimating the Pearson Corellation and the coefficient of determination (R2). The best interpolation technique for this research proved to be the Ordinary Kriging for the shoreline materials and the Empirical Bayesian Kriging (EBK) for the seabed materials since both had the lowest prediction errors and the highest R2. Coastal zones are one of the most complex and dynamic systems since their landform changes rapidly (timeframe of days and weeks) due to the combined action of tidal flows, currents and waves on coastal sediments (Raper et al. 2005). Moreover, grain size analysis and textural characteristics of surficial coastal sediments provide useful information to define and reveal the hydrodynamic condition as well as the deposition process. The modelling of many environmental and engineering applications in the coastal zone as well as for risk assessment against coastal hazards requires the knowledge of the grain size and the distribution of the surficial coastal sediments. As a result, measurements of grain size parameters are important for the understanding and calculation of sediment transport and critical parameters for modelling coastal erosion and vulnerability (Boumboulis et al. 2021), offshore and geotechnical engineering (Zananiri and Vakalas 2019), coastal zone management and coastal protection works such as beach nourishment. Hence, maps of surficial sediment spatial distribution in coastal and nearshore zone are important to provide information about the processes and mechanisms of the environment for sustainable management and protection.
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An evolutionary-assisted machine learning model for global solar radiation prediction in Minas Gerais region, southeastern Brazil Solar radiation prediction is necessary for designing photovoltaic systems, assessment of regional climate and crop growth modeling. However, this estimate depends on expensive devices, namely pyranometer and pyranometer. Considering the difficulty of acquiring these devices, predicting such values through mathematical and computational models is a convenient approach where costs can be reduced. In particular, machine learning methods have been successfully and widely applied for this task. However, the choice of the correct machine learning model, its parameters sets, and the variables used influence obtained results. This work presents a methodology that optimizes the aforementioned points to efficiently predict solar radiation in the state of Minas Gerais, Brazil. Currently, no work presents a computational model for the entire state. For this, data from 51 cities in Minas Gerais are used, obtained by the automatic weather stations of the National Institute of Meteorology. Two machine learning models, Artificial Neural Network and Multivariate Adaptive Regression Spline, were optimized through a Simple Genetic Algorithm, and the results compared to those available in the literature. The best results were found at the Guanhães station, with R2 of 0.867 and RMSE of 1.68 MJ m−2