Supervised neural network recognition of habitat zones of rice invertebrates

This study aimed to evaluate effectiveness and performance of several supervised neural network models and make pattern recognition on invertebrate habitat zones. Probabilistic, general regression, and linear neural networks, and discriminant analysis were used to recognize both known and unknown invertebrate habitat zones. The results showed that neural network models were better than traditional discriminant analysis in the recognition of known habitat zones. There was not distinctive variation in recognition from different neural network models. Sensitivity analysis indicated that the learning rate of the neural network would influence recognized results. An unknown invertebrate species from Lepidoptera was recognized to be soil-dweller (dryland) by both neural network models and discriminant analysis. In sensitivity analysis it was additionally recognized to be the type of plant canopy (terrestrial). Overall the species was estimated to be a soil-dweller (dryland) or live on plant canopy (terrestrial). It was concluded that neural network models can perform better than conventional statistic models in pattern recognition, but a comprehensive comparison among various models is necessary in order to achieve a high reliable recognition and prediction. Furthermore, sensitivity analysis can lead to an in-depth grasp on the mechanism in the recognition and is thus needed.     

Automatic Interpretation of Magnetic Data Using Euler Deconvolution with Nonlinear Background

The voluminous gravity and magnetic data sets demand automatic interpretation techniques like Naudy, Euler and Werner deconvolution. Of these techniques, the Euler deconvolution has become a popular choice because the method assumes no particular geological model. However, the conventional approach to solving Euler equation requires tentative values of the structural index preventing it from being fully automatic and assumes a constant background that can be easily violated if the singular points are close to each other. We propose a possible solution to these problems by simultaneously estimating the source location, depth and structural index assuming nonlinear background. The Euler equation is solved in a nonlinear fashion using the optimization technique like conjugate gradient. This technique is applied to a published synthetic data set where the magnetic anomalies were modeled for a complex assemblage of simple magnetic bodies. The results for close by singular points are superior to those obtained by assuming linear background. We also applied the technique to a magnetic data set collected along the western continental margin of India. The results are in agreement with the regional magnetic interpretation and the bathymetric expressions.     

Smithsonian Institution’s Global Volcanism Network

Information included in this summary is based on more detailed reports published in the Bulletin of the Global Volcanism Network, vol. 31, no. 3, March 2006 (on the Internet at ). Edited by scientists at the Smithsonian, this bulletin includes reports provided by a worldwide network of correspondents. The reports contain the names and contact information for all sources. Please note that these reports are preliminary and subject to change as events are studied in more detail. The Global Volcanism Program welcomes further reports of current volcanism, seismic unrest, monitoring data, and field observations.     

The numerical solution of the Advection-Dispersion Equation: A review of some basic principles

The simulation of solute transport in rivers is frequently based on numerical models of the Advection-Dispersion Equation. The construction of reliable computational schemes, however, is not necessarily easy. The paper reviews some of the most important issues in this regard, taking the finite volume method as the basis of the simulation, and compares the performance of several types of scheme for a simple case of the transport of a patch of solute along a uniform river. The results illustrate some typical (and well known) deficiencies of explicit schemes and compare the contrasting performance of implicit and semi-Lagrangian versions of the same schemes. It is concluded that the latter have several benefits over the other types of scheme.     

The structure of the mid- and high-latitude ionosphere during the November 2004 storm event obtained from GPS observations

GPS data from the International GNSS Service (IGS) network were used to study the development of the severe geomagnetic storm of November 7–12, 2004, in the total electron content (TEC) on a global scale. The TEC maps were produced for analyzing the storm. For producing the maps over European and North American sectors, GPS measurements from more than 100 stations were used. The dense network of GPS stations provided TEC measurements with a high temporal and spatial resolution. To present the temporal and spatial variation of TEC during the storm, differential TEC maps relative to a quiet day (November 6, 2004) were created. The features of geomagnetic storm attributed to the complex development of ionospheric storm depend on latitude, longitude and local time. The positive, as well as negative effects were detected in TEC variations as a consequence of the evolution of the geomagnetic storm. The maximal effect was registered in the subauroral/auroral ionosphere during substorm activity in the evening and night period. The latitudinal profiles obtained from TEC maps for Europe gave rise to the storm-time dynamic of the ionospheric trough, which was detected on November 7 and 9 at latitudes below 50°N. In the report, features of the response of TEC to the storm for European and North American sectors are analyzed.     

A data-based regional scale autoregressive rainfall-runoff model: a study from the Odra River

This paper aims to compare the performances of multivariate autoregressive (MAR) techniques and univariate autoregressive (AR) methods applied to regional scale rainfall-runoff modelling. We focus on the case study from the upper and middle reaches of the Odra River with its main tributaries in SW Poland. The rivers drain both the mountains (the Sudetes) and the lowland (Nizina Śląska). The region is exposed to extreme hydrologic and meteorological events, especially rain-induced and snow-melt floods. For the analysis, four hydrologic and meteorological variables are chosen, i.e., discharge (17 locations), precipitation (7 locations), thickness of snow cover (7 locations) and groundwater level (1 location). The time period is November 1971–December 1981 and the temporal resolution of the time series is of 1 day. Both MAR and AR models of the same orders are fitted to various subsets of the data and subsequently forecasts of discharge are derived. In order to evaluate the predictions the stepwise procedure is applied to make the validation independent of the specific sample path of the stochastic process. It is shown that the model forecasts peak discharges even 2–4 days in advance in the case of both rain-induced and snow-melt peak flows. Furthermore, the accuracy of discharge predictions increases if one analyses the combined data on discharge, precipitation, snow cover, and groundwater level instead of the pure discharge multivariate time series. MAR-based discharge forecasts based on multivariate data on discharges are more accurate than AR-based univariate predictions for a year with a flood, however, this relation is reverse in the case of the free-of-flooding year. In contrast, independently of the occurrence of floods within a year, MAR-based discharge forecasts based on discharges, precipitation, snow cover, and groundwater level are more precise than AR-based predictions.     

Groundwater-level Anomalies Associated with a Hypothetical Preslip Prior to the Anticipated Tokai Earthquake: Detectability Using the Groundwater Observation Network of the Geological Survey of Japan,AIST

We infer the groundwater-level anomalies associated with a hypothetical preslip prior to the anticipated M 8 Tokai earthquake, and evaluate the detectability of the anomalies using data from seven groundwater wells. We evaluate the detectability of the anomalies under the following assumptions: (1) an Mw 5.5–6.5 aseismic preslip event occurs at the plate boundary in and around the hypothetical focal zone of the Tokai earthquake; (2) the total amount of the strain step at each observation associated with the preslip can be calculated by tensile and shear faulting based on the dislocation model; (3) a normalized strain history associated with the preslip is defined from the results of numerical simulations based on rate- and state-dependent friction laws; and (4) the groundwater-level anomaly prior to the earthquake is proportional to the estimated history of the strain change associated with the preslip. We investigate the detection time of the anomaly at seven wells given an Mw 5.5, 6.0, or 6.5 aseismic preslip at one of the 272 grid points in and around the area of the hypothetical focal zone of the Tokai earthquake. As a result, over the time interval between 1 and 48 hours prior to the hypothetical Tokai earthquake, we are able to detect at each of the seven wells a hypothetical Mw 6.5 preslip at 10–86 of the 272 grid points, an Mw 6 preslip at 0–19 grid points, and an Mw 5.5 preslip at 0–5 grid points.