Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation

Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset

Bias adjustment of satellite-based precipitation estimation using gauge observations: A case study in Chile

Satellite-based precipitation estimates (SPEs) are promising alternative precipitation data for climatic and hydrological applications, especially for regions where ground-based observations are limited. However, existing satellite-based rainfall estimations are subject to systematic biases. This study aims to adjust the biases in the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) rainfall data over Chile, using gauge observations as reference. A novel bias adjustment framework, termed QM-GW, is proposed based on the nonparametric quantile mapping approach and a Gaussian weighting interpolation scheme. The PERSIANN-CCS precipitation estimates (daily, 0.04°×0.04°) over Chile are adjusted for the period of 2009–2014. The historical data (satellite and gauge) for 2009–2013 are used to calibrate the methodology; nonparametric cumulative distribution functions of satellite and gauge observations are estimated at every 1°×1° box region. One year (2014) of gauge data was used for validation. The results show that the biases of the PERSIANN-CCS precipitation data are effectively reduced. The spatial patterns of adjusted satellite rainfall show high consistency to the gauge observations, with reduced root-mean-square errors and mean biases. The systematic biases of the PERSIANN-CCS precipitation time series, at both monthly and daily scales, are removed. The extended validation also verifies that the proposed approach can be applied to adjust SPEs into the future, without further need for ground-based measurements. This study serves as a valuable reference for the bias adjustment of existing SPEs using gauge observations worldwide

Analyzing Herd Behavior in Global Stock Markets: An Intercontinental Comparison

Herd behavior is an important economic phenomenon, especially in the context of the recent financial crises. In this paper, herd behavior in global stock markets is investigated with a focus on intercontinental comparison. Since most existing herd behavior indices do not provide a comparative method, we propose a new herd behavior index and demonstrate its desirable properties through simple theoretical models. As for empirical analysis, we use global stock market data from Morgan Stanley Capital International to study herd behavior especially during periods of financial crises in detail

A high resolution coupled hydrologic–hydraulic model (HiResFlood-UCI) for flash flood modeling

HiResFlood-UCI was developed by coupling the NWS's hydrologic model (HL-RDHM) with the hydraulic model (BreZo) for flash flood modeling at decameter resolutions. The coupled model uses HL-RDHM as a rainfall-runoff generator and replaces the routing scheme of HL-RDHM with the 2D hydraulic model (BreZo) in order to predict localized flood depths and velocities. A semi-automated technique of unstructured mesh generation was developed to cluster an adequate density of computational cells along river channels such that numerical errors are negligible compared with other sources of error, while ensuring that computational costs of the hydraulic model are kept to a bare minimum. HiResFlood-UCI was implemented for a watershed (ELDO2) in the DMIP2 experiment domain in Oklahoma. Using synthetic precipitation input, the model was tested for various components including HL-RDHM parameters (a priori versus calibrated), channel and floodplain Manning n values, DEM resolution (10 m versus 30 m) and computation mesh resolution (10 m+ versus 30 m+). Simulations with calibrated versus a priori parameters of HL-RDHM show that HiResFlood-UCI produces reasonable results with the a priori parameters from NWS. Sensitivities to hydraulic model resistance parameters, mesh resolution and DEM resolution are also identified, pointing to the importance of model calibration and validation for accurate prediction of localized flood intensities. HiResFlood-UCI performance was examined using 6 measured precipitation events as model input for model calibration and validation of the streamflow at the outlet. The Nash–Sutcliffe Efficiency (NSE) obtained ranges from 0.588 to 0.905. The model was also validated for the flooded map using USGS observed water level at an interior point. The predicted flood stage error is 0.82 m or less, based on a comparison to measured stage. Validation of stage and discharge predictions builds confidence in model predictions of flood extent and localized velocities, which are fundamental to reliable flash flood warning

Neural Natural Language Inference Models Enhanced with External Knowledge

Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.Comment: Accepted by ACL 201