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Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region.

The study of wave-structure interactions involving nonlinear forces would often make use of the popular hybrid frequency–time domain method. In the hybrid method, the frequency-domain analysis could firstly provide the reliable and accurate dynamic parameters and responses; then these parameters and responses are transformed to the parameters to establishing the basic time-domain equation. Additionally, with the addition of the required linear and nonlinear forces, the time-domain analysis can be used to solve for the practical problems. However, the transformation from the frequency domain to the time domain is not straightforward, and the implementation of the time-domain equation could become increasingly complicated when different modes of motion are coupled. This research presents a systematic introduction on how to implement the time-domain analysis for floating structures, including the parameter transformations from the frequency domain to the time domain, and the methods for calculating and approximating the impulse functions and the fluid-memory effects, with special attention being paid to the coupling terms among the different motion modes, and the correctness of the time-domain-equation implementation. The main purpose of this article is to provide relevant information for those who wish to build their own time-domain analyses with the open-source hydrodynamic analysis packages, although commercial packages are available for time-domain analyses.

Frequency analysis on extreme hydrological and meteorological events under the effect of climate change is performed in the island of Crete. Data from Regional Climate Model simulations (RCMs) that follow three Representative Concentration Pathways (RCP2.6, RCP4.5, RCP8.5) are used in the analysis. The analysis was performed for the 1985–2100 time period, divided into three equal-duration time slices (1985–2010, 2025–2050, and 2075–2100). Comparison between the results from the three time slices for the different RCMs under different RCP scenarios indicate that drought events are expected to increase in the future. The meteorological and hydrological drought indices, relative Standardized Precipitation Index (SPI) and Standardized Runoff index (SRI), are used to identify the number of drought events for each RCM. Results from extreme precipitation, extreme flow, meteorological and hydrological drought frequency analysis over Crete show that the impact of climate change on the magnitude of 100 years return period extreme events will also increase, along with the magnitude of extreme precipitation and flow events.

The authors wish to make the following corrections to this paper [...]

The purpose of this study is to examine the use of particle swarm optimization algorithm to train a feed-forward multi-layer artificial neural network, which can simulate the hydraulic head change at an observation well. Artificial neural networks are models which belong in the field of artificial intelligence, and they are designed to mimic the learning process of the human brain. A typical network consists of input nodes, where the data are imported to the network, the nodes of the hidden layers and the output nodes. The nodes of the hidden layers and the output nodes are computational nodes, while all nodes are connected to each other by the corresponding synaptic weight. The purpose of this method is to draw a relationship between input and output values, adjusting the synaptic weight during the training process, in order to minimize the squared mean error. The trained network is then tested to verify its accuracy.Particle swarm optimization is a relatively new evolutionary algorithm, which is used to find optimal solutions to numerical and quantitative problems. It was developed by Eberhart and Kennedy in 1995 and since then it has been applied in various scientific fields. The swarm consists of particles moving in the search space defined by the problem. Each particle represents a solution and is defined by the vector of its position (xi) and the vector of its velocity (vi), where the position vector corresponds to parameters of the problem. The solution is defined as the result of an objective function, defined by the user, in this case the training error. At each iteration of the algorithm, each particle updates its velocity taking into account its previous speed, its best position and the best position of the swarm.Three different variations of particle swarm optimization algorithm are considered, and the one chosen was GLBest – PSO, where the distance between the best solution found so far and the best solution of each particle plays a major role in the updating of each particle’s velocity.The algorithm is implemented using field data from the region of Agia, Chania, and the results are compared with those derived when using the most widely used training method, the back propagation algorithm. The particle swarm optimization algorithm shows an improvement of 9.3% and 18% in training and test errors respectively, compared to the errors of the back propagation algorithm. The trained neural network can predict the hydraulic head change at a well, underestimating it in some cases. The maximum divergence for the observed values is 0.35m. When the hydraulic head change is converted into hydraulic head, using the observed hydraulic head of the previous day, the deviations of simulated values from the actual hydraulic head appear comparatively smaller, with an average deviation of 0.041m.The trained neural network was also used for midterm prediction. In this case the hydraulic head of the first day of the simulation is used together with the hydraulic head change derived from the simulation, without using the hydraulic head of each previous day in the well. The values obtained by this process were smaller than the observed ones, while the maximum difference is about 1m. This error, however, does not accumulate during the two hydrological years simulated, and the error at the end of the simulation period is minimal.Finally, three climate change scenarios were examined. These scenarios foresee a reduction by 12%, 37% and increase by 13% for the precipitation and an increase by 1.9o C, 2.7o C and 1o C in average temperature respectively in Crete by 2040. In order to study this scenario data time series were created for the period 2010-2020, using a stochastic weather generator. The prediction results indicate a severe negative effect on the groundwater level only when the reduction in precipitation is 37%, while in the other cases the results vary from neutral to positive.