Predicting seasonal and hydro-meteorological impact in environmental variables modelling via Kalman filtering

This study focuses on the potential improvement of environmental variables modelling by using linear state-space models, as an improvement of the linear regression model, and by incorporating a constructed hydro-meteorological covariate. The Kalman filter predictors allow to obtain accurate predictions of calibration factors for both seasonal and hydro-meteorological components. This methodology can be used to analyze the water quality behaviour by minimizing the effect of the hydrological conditions. This idea is illustrated based on a rather extended data set relative to the River Ave basin (Portugal) that consists mainly of monthly measurements of dissolved oxygen concentration in a network of water quality monitoring sites. The hydro-meteorological factor is constructed for each monitoring site based on monthly precipitation estimates obtained by means of a rain gauge network associated with stochastic interpolation (kriging). A linear state-space model is fitted for each homogeneous group (obtained by clustering techniques) of water monitoring sites. The adjustment of linear state-space models is performed by using distribution-free estimators developed in a separate section.     

Ab initio quantum-mechanical modeling of pyrophyllite [Al2Si4O10(OH)2] and talc [Mg3Si4O10(OH)2] surfaces

Bulk and slab geometry optimizations and calculations of the electrostatic potential at the surface of both pyrophyllite [Al₂Si₄O₁₀(OH)₂] and talc [Mg₃Si₄O₁₀(OH)₂] were performed at Hartree–Fock and DFT level. In both pyrophyllite and talc cases, a modest (001) surface relaxation was observed, and the surface preserves the structural features of the crystal: in the case of pyrophyllite the tetrahedral and octahedral sheets are strongly distorted with respect to the ideal hexagonal symmetry (and basal oxygen are located at different heights along the direction normal to the basal plane), whereas the structure of talc deviates slightly from the ideal hexagonal symmetry (almost co-planar basal oxygen). The calculated distortions are fully consistent with those experimentally observed. Although the potentials at the surface of pyrophyllite and talc are of the same order of magnitude, large topological differences were observed, which could possibly be ascribed to the differences between the surface structures of the two minerals. Negative values of the potential are located above the basal oxygen and at the center of the tetrahedral ring; above silicon the potential is always positive. The value of the potential minimum above the center of the tetrahedral ring of pyrophyllite is ?0.05 V (at 2 Å from the surface), whereas in the case of talc the minimum is ?0.01 V, at 2.7 Å. In the case of pyrophyllite the minimum of potential above the higher basal oxygen is located at 1.1 Å and it has a value of ?1.25 V, whereas above the lower oxygen the value of the potential at the minimum is ?0.2 V, at 1.25 Å; the talc exhibits a minimum of ?0.75 V at 1.2 Å, above the basal oxygen.     

Quantifying the relationship between structural deformation and the morphology of submarine channels on the Niger Delta continental slope

The processes and deposits of deep‐water submarine channels are known to be influenced by a wide variety of controlling factors, both allocyclic and autocyclic. However, unlike their fluvial counterparts whose dynamics are well‐studied, the factors that control the long‐term behaviour of submarine channels, particularly on slopes undergoing active deformation, remain poorly understood. We combine seismic techniques with concepts from landscape dynamics to investigate quantitatively how the growth of gravitational‐collapse structures at or near the seabed in the Niger Delta have influenced the morphology of submarine channels along their length from the shelf edge to their deep‐water counterpart. From a three dimensional (3D), time‐migrated seismic‐reflection volume, which extends over 120 km from the shelf edge to the base of slope, we mapped the present‐day geomorphic expression of two submarine channels and active structures at the seabed, and created a Digital Elevation Model (DEM). A second geomorphic surface and DEM raster—interpreted to closer approximate the most recent active channel geometries—were created through removing the thickness of hemipelagic drape across the study area. The DEM rasters were used to extract the longitudinal profiles of channel systems with seabed expression, and we evaluate the evolution of channel widths, depths and slopes at fixed intervals downslope as the channels interact with growing structures. Results show that the channel long profiles have a relatively linear form with localized steepening associated with seabed structures. We demonstrate that channel morphologies and their constituent architectural elements are sensitive to active seafloor deformation, and we use the geomorphic data to infer a likely distribution of bed shear stresses and flow velocities from the shelf edge to deep water. Our results give new insights into the erosional dynamics of submarine channels, allow us to quantify the extent to which submarine channels can keep pace with growing structures, and help us to constrain the delivery and distribution of sediment to deep‐water settings.     

Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study

The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning (ML) techniques have received less attention despite their potential for producing highly accurate predictions of future urban extents. The aim of this study is to investigate two ML techniques, namely radial basis function network (RBFN) and multi-layer perceptron (MLP) networks, for modeling urban change. By predicting urban change for 2010, the models’ performance is evaluated by comparing results with a reference map and by using a set of pertinent statistical measures, such as average spatial distance deviation and figure of merit. The application of these techniques employs the case study area of Mumbai, India. The results show that both models, which were tested using the same explanatory variables, produced promising results in terms of predicting the size and extent of future urban areas. Although a close match between RBFN and MLP is observed, RBFN demonstrates higher spatial accuracy of prediction. Accordingly, RBFN was utilized to simulate urban change for 2020 and 2030. Overall, the study provides evidence that RBFN is a robust and efficient ML technique and can therefore be recommended for land use change modeling.     

Contextual neural gas for spatial clustering and analysis

This study aims to introduce contextual Neural Gas (CNG), a variant of the Neural Gas algorithm, which explicitly accounts for spatial dependencies within spatial data. The main idea of the CNG is to map spatially close observations to neurons, which are close with respect to their rank distance. Thus, spatial dependency is incorporated independently from the attribute values of the data. To discuss and compare the performance of the CNG and GeoSOM, this study draws from a series of experiments, which are based on two artificial and one real-world dataset. The experimental results of the artificial datasets show that the CNG produces more homogenous clusters, a better ratio of positional accuracy, and a lower quantization error than the GeoSOM. The results of the real-world dataset illustrate that the resulting patterns of the CNG are theoretically more sound and coherent than that of the GeoSOM, which emphasizes its applicability for geographic analysis tasks.     

Wearables and Location Tracking Technologies for Mental-State Sensing in Outdoor Environments

Advances in commercial wearable devices are increasingly facilitating the collection and analysis of everyday physiological data. This article discusses the theoretical and practical aspects of using such ambulatory devices for the detection of episodic changes in physiological signals as a marker for mental state in outdoor environments. A pilot study was conducted to evaluate the feasibility of using commercial wearables in combination with location tracking technologies. The study measured physiological signals for fifteen participants, including heart rate, heart rate variability, and skin conductance. Participants’ signals were recorded during an outdoor walk that was tracked using a Global Positioning System logger. The walk was designed to pass through various types of environments including green, blue, and urban spaces, as well as a more stressful road crossing. The data that were obtained were used to demonstrate how biosensor information can be contextualized and enriched using location information. Significant episodic changes in physiological signals under real-world conditions were detectable in the stressful road crossing but not in the other types of environments. The article concludes that despite challenges and limitations of current off-the-shelf wearables, the utilization of these devices offers novel opportunities for evaluating episodic changes in physiological signals as a marker for mental state during everyday activities including in outdoor environments. Key Words: electrodermal activity, GPS, mental state, stress, wearable.     

Seismic activity along the central america volcanic arc: is it related to subduction of the Cocos plate?

We determine seismic strain rate of tectonic earthquakes along the Central America Volcanic Arc. We then compare this result to those obtained from earthquakes related to the convergence of the Cocos and Caribbean plates and to earthquakes in the back-arc region of northern Central America.

The seismic strain-rate tensor for shallow-focus earthquakes along the Central America volcanic arc since 1700, has a compressive eigenvector with a magnitude of 0.7 × 10⁻⁸ year⁻¹, and oriented in a 357° azimuth. The extensive eigenvector is oriented in a 86° azimuth, with a magnitude of 0.82 × 10⁻⁸ year⁻¹. When only Centroid Moment-tensor solutions (CMT) are considered, the respective eigenvectors are 1.2 × 10⁻⁸ year⁻¹ and 1.0 × 10⁻⁸ year⁻¹.

The compressive eigenvector from the seismic strain-rate tensor for earthquakes along the Cocos-Caribbean convergent margin is 2.0 × 10⁻⁸ year⁻¹, plunging at 25°, and oriented in a 29° azimuth. Its magnitude and direction are similar to those of the compressive eigenvector for earthquakes along the volcanic arc. The extensive eigenvector along the convergent margin, on the other hand, has a large vertical component. The compressive and extensive eigevenvectors are 4.9 × 10⁻⁸ year⁻¹ and 4.6 × 10⁻⁸ year⁻¹, using only CMTs as the database.

Earthquakes along the grabens of northern Central America yield a seismic strain-rate tensor whose extensive eigenvector has a magnitude of 2.4 × 10⁻⁸ year⁻¹, oriented in a 109° azimuth. Magnitude and direction are similar to those of the extensive eigenvector for earthquakes along the volcanic arc. The compressive eigenvector along the grabens is practically vertical.

Similarities in magnitudes and directions for compressive and extensive eigenvectors suggest to us that the strain field along the Central America volcanic arc is the result of compression along the convergent Cocos-Caribbean margin, and extension in the back-arc region, along the grabens of northern Central America. This field is resolved as strike-slip faulting along the arc.