scholarly journals Multi-Scale Target-Specified Sub-Model Approach for Fast Large-Scale High-Resolution 2D Urban Flood Modelling

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 259
Author(s):  
Guohan Zhao ◽  
Thomas Balstrøm ◽  
Ole Mark ◽  
Marina B. Jensen
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Large Scale ◽  
Goodness Of Fit ◽  
Significant Loss ◽  
Urban Flood ◽  
Multi Scale ◽  
The Impact ◽  
Mean Square Errors ◽  
Model Approach

The accuracy of two-dimensional hydrodynamic models (2D models) is improved when high-resolution Digital Elevation Models (DEMs) are used. However, the entailed high spatial discretisation results in excessive computational expenses, thus prohibiting their implementation in real-time forecasting especially at a large scale. This paper presents a sub-model approach that adapts 1D static models to tailor high-resolution 2D model grids relevant to specified targets, such that the tailor-made 2D hydrodynamic sub-models yield fast processing without significant loss of accuracy via a GIS-based multi-scale simulation framework. To validate the proposed approach, model experiments were first designed to separately test the impact of two outcomes (i.e., the reduced computational domains and the optimised boundary conditions) towards final 2D prediction results. Then, the robustness of the sub-model approach was evaluated by selecting four focus areas with distinct catchment terrain morphologies as well as distinct rainfall return periods of 1–100 years. The sub-model approach resulted in a 45–553 times faster processing with a 99% reduction in the number of computational cells for all four cases; the goodness of fit regarding predicted flood extents was above 0.88 of F2, flood depths yield Root Mean Square Errors (RMSE) below 1.5 cm and the discrepancies of u- and v-directional velocities at selected points were less than 0.015 ms−1. As such, this approach reduces the 2D models’ computing expenses significantly, thus paving the way for large-scale high-resolution 2D real-time forecasting.

scholarly journals Targets-specified grids-tailored sub-model approach for fast large-scale high-resolution 2D urban flood modelling

2020 ◽  
Author(s):  
Guohan Zhao ◽  
Thomas Balstrøm ◽  
Ole Mark ◽  
Marina B. Jensen
Keyword(s):  
High Resolution ◽  
Boundary Conditions ◽  
Real Time ◽  
Large Scale ◽  
Screening Method ◽  
Urban Flood ◽  
The Impact ◽  
Mean Square Errors ◽  
Impact Zones ◽  
Model Approach

Abstract. The accuracy of two-dimensional urban flood models (2D models) is improved when high-resolution Digital Elevation Models (DEMs) is used, but the entailed high spatial discretisation results in excessive computational expenses, thus prohibiting the use of 2D models in real-time forecasting at a large scale. This paper presents a sub-model approach to tailoring high-resolution 2D model grids according to specified targets, and thus such tailor-made sub-model yields fast processing without significant loss of accuracy. Among the numerous sinks detected from full-basin high-resolution DEMs, the computationally important ones are determined using a proposed Volume Ratio Sink Screening method. Also, the drainage basin is discretised into a collection of sub-impact zones according to those sinks' spatial configuration. When adding full-basin distributed static rainfall, the drainage basin's flow conditions are modelled as a 1D static flow by using a fast-inundation spreading algorithm. Next, sub-impact zones relevant to the targets' local inundation process can be identified by tracing the 1D flow continuity, and thus suggest the critical computational cells from the high-resolution model grids on the basis of the spatial intersection. In MIKE FLOOD's 2D simulations, those screened cells configure the reduced computational domains as well as the optimised boundary conditions, which ultimately enables the fast 2D prediction in the tailor-made sub-model. To validate the method, model experiments were designed to test the impact of the reduced computational domains and the optimised boundary conditions separately. Further, the general applicability and the robustness of the sub-model approach were evaluated by targeting at four focus areas representing different catchment terrain morphologies as well as different rainfall return periods of 1–100 years. The sub-model approach resulted in a 45–553 times faster processing with a 99 % reduction in the number of computational cells for all four cases; the predicted flood extents, depths and flow velocities showed only marginal discrepancies with Root Mean Square Errors (RMSE) below 1.5 cm. As such, this approach reduces the 2D models' computing expenses significantly, thus paving the way for large-scale high-resolution 2D real-time forecasting.

scholarly journals Synchronization in 5G networks: a hybrid Bayesian approach toward clock offset/skew estimation and its impact on localization

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Meysam Goodarzi ◽  
Darko Cvetkovski ◽  
Nebojsa Maletic ◽  
Jesús Gutiérrez ◽  
Eckhard Grass
Keyword(s):  
Bayesian Approach ◽  
Large Scale ◽  
Hybrid Approach ◽  
Position Estimation ◽  
5G Networks ◽  
Clock Skew ◽  
Depth Analysis ◽  
The Impact ◽  
Mean Square Errors ◽  
Clock Offset

AbstractClock synchronization has always been a major challenge when designing wireless networks. This work focuses on tackling the time synchronization problem in 5G networks by adopting a hybrid Bayesian approach for clock offset and skew estimation. Furthermore, we provide an in-depth analysis of the impact of the proposed approach on a synchronization-sensitive service, i.e., localization. Specifically, we expose the substantial benefit of belief propagation (BP) running on factor graphs (FGs) in achieving precise network-wide synchronization. Moreover, we take advantage of Bayesian recursive filtering (BRF) to mitigate the time-stamping error in pairwise synchronization. Finally, we reveal the merit of hybrid synchronization by dividing a large-scale network into local synchronization domains and applying the most suitable synchronization algorithm (BP- or BRF-based) on each domain. The performance of the hybrid approach is then evaluated in terms of the root mean square errors (RMSEs) of the clock offset, clock skew, and the position estimation. According to the simulations, in spite of the simplifications in the hybrid approach, RMSEs of clock offset, clock skew, and position estimation remain below 10 ns, 1 ppm, and 1.5 m, respectively.

scholarly journals Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3232 ◽  
Author(s):  
Yan Liu ◽  
Qirui Ren ◽  
Jiahui Geng ◽  
Meng Ding ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Training Data ◽  
Land Resources ◽  
Remote Sensing Images ◽  
Training Strategy ◽  
The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

scholarly journals Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

2017 ◽  
Vol 10 (3) ◽  
pp. 1383-1402 ◽  
Author(s):  
Paolo Davini ◽  
Jost von Hardenberg ◽  
Susanna Corti ◽  
Hannah M. Christensen ◽  
Stephan Juricke ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Rainfall Variability ◽  
Global Climate Model ◽  
Small Scale ◽  
Low Resolution ◽  
The Impact ◽  
Stochastic Physics

Abstract. The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth system model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 up to 16 km). The project includes more than 120 simulations in both a historical scenario (1979–2008) and a climate change projection (2039–2068), together with coupled transient runs (1850–2100). A total of 20.4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5 PB of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. About 140 TB of post-processed data are stored on the CINECA supercomputing centre archives and are freely accessible to the community thanks to an EUDAT data pilot project. This paper presents the technical and scientific set-up of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given. An improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increase is observed. It is also shown that including stochastic parameterisation in the low-resolution runs helps to improve some aspects of the tropical climate – specifically the Madden–Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small-scale processes on the large-scale climate variability either explicitly (with high-resolution simulations) or stochastically (in low-resolution simulations).

scholarly journals Continuous high-resolution midlatitude-belt simulations for July–August 2013 with WRF

2017 ◽  
Vol 10 (5) ◽  
pp. 2031-2055 ◽  
Author(s):  
Thomas Schwitalla ◽  
Hans-Stefan Bauer ◽  
Volker Wulfmeyer ◽  
Kirsten Warrach-Sagi
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Temperature Fields ◽  
Surface Model ◽  
Limited Area ◽  
Data Set ◽  
Latitude Belt ◽  
Simulated Precipitation ◽  
The Impact

Abstract. Increasing computational resources and the demands of impact modelers, stake holders, and society envision seasonal and climate simulations with the convection-permitting resolution. So far such a resolution is only achieved with a limited-area model whose results are impacted by zonal and meridional boundaries. Here, we present the setup of a latitude-belt domain that reduces disturbances originating from the western and eastern boundaries and therefore allows for studying the impact of model resolution and physical parameterization. The Weather Research and Forecasting (WRF) model coupled to the NOAH land–surface model was operated during July and August 2013 at two different horizontal resolutions, namely 0.03 (HIRES) and 0.12° (LOWRES). Both simulations were forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis data at the northern and southern domain boundaries, and the high-resolution Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) data at the sea surface.The simulations are compared to the operational ECMWF analysis for the representation of large-scale features. To analyze the simulated precipitation, the operational ECMWF forecast, the CPC MORPHing (CMORPH), and the ENSEMBLES gridded observation precipitation data set (E-OBS) were used as references.Analyzing pressure, geopotential height, wind, and temperature fields as well as precipitation revealed (1) a benefit from the higher resolution concerning the reduction of monthly biases, root mean square error, and an improved Pearson skill score, and (2) deficiencies in the physical parameterizations leading to notable biases in distinct regions like the polar Atlantic for the LOWRES simulation, the North Pacific, and Inner Mongolia for both resolutions.In summary, the application of a latitude belt on a convection-permitting resolution shows promising results that are beneficial for future seasonal forecasting.

scholarly journals Real-time simulation of large-scale HTS systems: multi-scale and homogeneous models using the T–A formulation

2019 ◽  
Vol 32 (6) ◽  
pp. 065003 ◽  
Author(s):  
Edgar Berrospe-Juarez ◽  
Víctor M R Zermeño ◽  
Frederic Trillaud ◽  
Francesco Grilli
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Real Time Simulation ◽  
Multi Scale ◽  
Time Simulation ◽  
Homogeneous Models

scholarly journals Pine wilt disease detection in high-resolution UAV images using object-oriented classification

2021 ◽  
Author(s):  
Zhao Sun ◽  
Yifu Wang ◽  
Lei Pan ◽  
Yunhong Xie ◽  
Bo Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Digital Camera ◽  
Object Oriented ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Segmentation Algorithm ◽  
Tree Crown ◽  
Multi Scale ◽  
Pine Wilt

AbstractPine wilt disease (PWD) is currently one of the main causes of large-scale forest destruction. To control the spread of PWD, it is essential to detect affected pine trees quickly. This study investigated the feasibility of using the object-oriented multi-scale segmentation algorithm to identify trees discolored by PWD. We used an unmanned aerial vehicle (UAV) platform equipped with an RGB digital camera to obtain high spatial resolution images, and multi-scale segmentation was applied to delineate the tree crown, coupling the use of object-oriented classification to classify trees discolored by PWD. Then, the optimal segmentation scale was implemented using the estimation of scale parameter (ESP2) plug-in. The feature space of the segmentation results was optimized, and appropriate features were selected for classification. The results showed that the optimal scale, shape, and compactness values of the tree crown segmentation algorithm were 56, 0.5, and 0.8, respectively. The producer’s accuracy (PA), user’s accuracy (UA), and F1 score were 0.722, 0.605, and 0.658, respectively. There were no significant classification errors in the final classification results, and the low accuracy was attributed to the low number of objects count caused by incorrect segmentation. The multi-scale segmentation and object-oriented classification method could accurately identify trees discolored by PWD with a straightforward and rapid processing. This study provides a technical method for monitoring the occurrence of PWD and identifying the discolored trees of disease using UAV-based high-resolution images.

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