Selecting velocity fields in urban pluvial floods from pre-calculated data

2020 ◽  
Author(s):  
Robert Sämann ◽  
Thomas Graf ◽  
Insa Neuweiler
Keyword(s):  
High Resolution ◽  
Water Level ◽  
Mean Square Error ◽  
Nearest Neighbor ◽  
Calculated Data ◽  
Velocity Fields ◽  
Water Levels ◽  
Mean Square ◽  
Run Off

<p><span>Early warning systems for floods in urban areas should forecast water levels and damage estimation to protect vulnerable regions. To estimate the danger of a flood for buildings and people, the energy of the flood has to be taken into account additionally to the water level. The energy is related to the flow velocity. For directing rescue workers or trace spreading of contaminants through flooded streets, a high resolution of the water’s energy in space and time is required. Direct numerical run-off calculation is too slow for a flood forecast in time. Therefore a database with pre-calculated events is needed and a method to select the water levels and velocity fields that are similar to a forecasted rain event. </span></p><p><span>We present a method, how to create a real-time forecast based on pre-calculated data. The selection and weighting of the pre-calculated data is based on the precipitation pattern in the flood region. A nearest neighbor approach is applied to find water levels and velocity fields from a database that are similar to the forecasting event. For the ranking of similarity, different new metrics are compared against each other. The quality of the metrics is tested with a new approach of comparing velocity fields on the surface and in the pipe system. Considering both domains is crucial for understanding the complex dynamic flow paths on the surface. An urban catchment of 5 km² with high resolution (~3 m³) triangular surface mesh and connected drainage system is used for a hydrodynamic run-off simulation. The 1D-2D coupled software HYSTEM EXTRAN is used to generate the water levels and velocity fields for strong rainfall events of the past 20 years. More than 900 events with a duration between 15 minutes and 24 hours and return periods between 10 and 100 years were calculated and stored as the “pre-calculated” dataset.</span></p><p><span>For comparing two events, the mean square error is calculated between the precipitation patterns with different approaches to select the start index and number of intervals. This number depends on the hydraulic response time, the temporal resolution and the length of the reference pattern. The quality of the nearest neighbor selection is quantified using the Nash–Sutcliffe model efficiency coefficient of pipe flow and the root mean square error of water level and velocity in significant surface cells. Additionally, the transport paths of artificial contamination spills are compared between the events to show the reproducibility of velocity fields for each metric. </span></p><p><span>Results show that the reaction time and the wetting state of the surface is very important. Single cell values correspond well between a forecasted and a dataset event. However, complex transport paths have a very high variability that is not reproducible with similar events. Further research is required to clarify if this is a result of the random walk approach or of the injection time of the particles. </span></p>

scholarly journals Construction of nearshore elevation model using open satellite data

2021 ◽  
Author(s):  
A.E. Hmelnov ◽  
A.S. Gachenko
Keyword(s):  
High Resolution ◽  
Water Level ◽  
Satellite Data ◽  
Satellite Images ◽  
Water Levels ◽  
Water Level Changes ◽  
Subpixel Accuracy ◽  
Contour Lines ◽  
Elevation Model

For the tasks considering changes of water level it is required to have a combined (above water and underwater) elevation model. And the highest accuracy requirements are imposed on the parts of the model, which produce the contour lines in the range of the actual water level changes, while the information about the underwater elevation is usually very scarce and rough. In the article we consider the possibility to obtain this part of the elevation model using open high resolution (10 m/pixel) satellite images corresponding to different water levels. Here we describe the technique, which allows us to obtain the subpixel accuracy of the resulting contour lines. And we consider the problems in the quality of the satellite images that the contour lines reveal, and some ways to deal with the problems.

Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques

2021 ◽  
Vol 25 (2) ◽  
pp. 227-234
Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Water Table ◽  
Environmental Sustainability ◽  
Model Development ◽  
Water Table Depth ◽  
Water Levels ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Inference System

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.

scholarly journals Quality of wood in the stands of poplar clones

2008 ◽  
Vol 54 (No. 1) ◽  
pp. 9-16
Author(s):  
R. Petráš ◽  
J. Mecko ◽  
V. Nociar
Keyword(s):  
Root Mean Square Error ◽  
Systematic Error ◽  
Mean Square Error ◽  
Statistical Tests ◽  
Mean Square ◽  
Poplar Clones ◽  
Mean Diameter ◽  
Empirical Material ◽  
Relative Root

The results obtained in research on the quality of raw timber by means of the structure of assortments for the stands of poplar clones Robusta and I-214 are presented in the paper. Models for an estimation of the structure of basic assortments of poplar stands were constructed separately for each clone in dependence on mean diameter, quality of stems, and damage to stems in the stand. The clone Robusta has higher proportions of higher-quality assortments than the clone I-214. The accuracy of models was determined on empirical material. It was confirmed by statistical tests that the models did not have a systematic error. The relative root mean-square error for main assortments of the clone I-214 is 15–27% and Robusta 13–24%.

Comparison of Thresholding and Edge Detection Segmentation Techniques

2013 ◽  
Vol 860-863 ◽  
pp. 2783-2786
Author(s):  
Yu Bing Dong ◽  
Hai Yan Wang ◽  
Ming Jing Li
Keyword(s):  
Image Segmentation ◽  
Root Mean Square Error ◽  
Edge Detection ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Experimental Results ◽  
Mean Square ◽  
Segmentation Method ◽  
Segmentation Algorithms

Edge detection and thresholding segmentation algorithms are presented and tested with variety of grayscale images in different fields. In order to analyze and evaluate the quality of image segmentation, Root Mean Square Error is used. The smaller error value is, the better image segmentation effect is. The experimental results show that a segmentation method is not suitable for all images segmentation.

Modified No Search Scheme based Domain Blocks Sorting Strategies for Fractal Image Coding

2014 ◽  
Vol 69 (10-11) ◽  
pp. 511-520
Author(s):  
Xing-Yuan Wang ◽  
Dou-Dou Zhang ◽  
Na Wei
Keyword(s):  
Mean Square Error ◽  
Image Coding ◽  
The Other ◽  
Mean Square ◽  
Minimum Value ◽  
Fractal Image ◽  
Fractal Image Coding ◽  
Domain Block ◽  
Partition Method

AbstractA novel fractal image coding algorithm based on domain blocks sorting strategies and modified no search scheme is proposed in this paper. On one hand, in order to improve the encoding time, a modified no search (MNS) scheme is adopted. Firstly, the image is divided into blocks of different size utilizing an adaptive quadtree partition method. Secondly, one finds the location of the best matching domain block using the MNS scheme for the range blocks, whose sizes are larger than the preset minimum value. Thirdly, the types of the range block and domain block are computed employing the proposed approach, and then the corresponding computation of mean square error (MSE) is determined. The computation of the MSE is reduced and the encoding phase speeds up. On the other hand, the range blocks with the minimal sizes are encoded applying the proposed domain blocks sorting (DBS) method. Contrast experiment results show that the proposed algorithm can obtain good quality of the reconstructed images and shorten the encoding time significantly.

Water Level Fluctuation in a Northeastern Ontario Peatland

1974 ◽  
Vol 4 (1) ◽  
pp. 76-81 ◽  
Author(s):  
T. S. Dai ◽  
V. F. Haavisto ◽  
J. H. Sparling
Keyword(s):  
Water Level ◽  
Narrow Range ◽  
Black Spruce ◽  
Heavy Rain ◽  
High Water ◽  
Water Levels ◽  
Level Fluctuation ◽  
Local Climate ◽  
Poor Fen ◽  
Run Off

Depths to water level and changes due to local climate were dissimilar in five peatland conditions in northeastern Ontario. The deepest water level and the greatest fluctuations occurred in an ombrotrophic black spruce bog site. The sedge-dominated poor fen site was submerged following every heavy rain. Waterlogged conditions remained within 6 cm of the surface at all times because of the influence by the water level of Dai Lake. The water level of Dai Lake varied within a narrow range because the loss of water was primarily dependent on slow seepage and evaporation. The lagg site was affected by continuous inflow, high water levels, and fast run-off, therefore, a larger fluctuation of water level prevailed at this site.

scholarly journals Wave model verification based on measurements in the Wadden Sea

2016 ◽  
Author(s):  
Cordula Berkenbrink ◽  
Luise Hentze ◽  
Andreas Wurpts
Keyword(s):  
Water Level ◽  
Wadden Sea ◽  
Wave Attenuation ◽  
Calculated Data ◽  
Barrier Islands ◽  
Wave Model ◽  
Water Levels ◽  
Model Verification ◽  
Coastal Protection ◽  
Lower Saxony

Abstract. The design height of coastal protection structures in Lower Saxony / Germany is determined by the design water level and the corresponding wave run up. For the calculation of these parameters several mathematical models are used which need to be verified for the conditions at the East Frisian Wadden Sea area. For this issue a wave measuring programme is operationally run, which includes various measurement locations and devices around the islands Norderney and Juist. The measurements are continuously extended and adapted in order to improve models and measurements. This paper shows a comparison between measured and calculated data for the storm surge of the 10.–11.01.2015 incorporating to new wave and water level gauges operated within COSYNA as well as a second research project dealing with wave attenuation behind barrier islands. Water levels within the investigation area were calculated by hydrodynamic models driven with a wind field originating from weather forecast and compared to water level measurements. The corresponding wave energy field was calculated by means of a third generation wave model and results compared to measurements of several devices located around the barrier Islands. The aim of the study shown here is to give a brief overview of possible error sources for model-data as well as data-data comparisons.

Improving Methodology for High-Resolution Reconstruction of Sea-Level Rise and Neotectonics by Paleoecological Analysis and AMS 14C Dating of Basal Peats

1998 ◽  
Vol 49 (1) ◽  
pp. 72-85 ◽  
Author(s):  
Torbjörn E. Törnqvist ◽  
Mark H.M. van Ree ◽  
Ron van 't Veer ◽  
Bas van Geel
Keyword(s):  
High Resolution ◽  
Ground Water ◽  
Sea Level Rise ◽  
Water Level ◽  
Sea Level ◽  
Small Sample ◽  
Water Levels ◽  
Ground Water Level ◽  
Specific Level ◽  
High Resolution Reconstruction

Sea-level research in several submerging coastal regions has traditionally been based on 14C dating of basal peats that overlie a compaction-free substratum and can be related to paleo-(ground)water levels. Provided that an unequivocal relationship between (ground)water level and sea level can be assumed, this approach contains two sources of uncertainty: (1) the paleoenvironmental interpretation of samples is usually based on inherently inaccurate macroscopic descriptions in the field, and (2) 14C ages of bulk peat samples may be erroneous as a result of contamination. Due to the uncertainties in both the altitude and the age—the two crucial sources of evidence necessary to arrive at accurate sea-level curves—sea-level index points are therefore represented by considerable, but typically not quantified, error boxes. Accelerator mass spectrometry (AMS) opens new perspectives for this type of sea-level research, as illustrated by a paleoecological and AMS 14C study of basal peats from a small study area in the Rhine–Meuse Delta (The Netherlands), where previous (conventional) work revealed highly problematic results. A detailed macrofossil analysis has two purposes: (1) an inferred paleoecological succession indicates a relatively accurate level of paludification of the site, and hence rise of the (ground)water level; (2) suitable macrofossils from that specific level are then selected for AMS 14C dating. In spite of very small sample sizes, our results are consistent and indicate that this approach can constitute a step forward in high-resolution reconstruction of sea-level rise. The new results further enable a revision of Holocene (ground)water gradient lines for the Rhine–Meuse Delta. A knickpoint in these gradient lines can be related to the effect of faulting. This approach therefore also has considerable potential to unravel and quantify neotectonic activity in submerging coastal settings.

scholarly journals Daily Water Level Prediction of Zrebar Lake (Iran): A Comparison between M5P, Random Forest, Random Tree and Reduced Error Pruning Trees Algorithms

2020 ◽  
Vol 9 (8) ◽  
pp. 479
Author(s):  
Viet-Ha Nhu ◽  
Himan Shahabi ◽  
Ebrahim Nohani ◽  
Ataollah Shirzadi ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Random Forest ◽  
Root Mean Square Error ◽  
Water Level ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Lag Time ◽  
Random Tree ◽  
Mean Square ◽  
Daily Water ◽  
Zrebar Lake

Zrebar Lake is one of the largest freshwater lakes in Iran and it plays an important role in the ecosystem of the environment, while its desiccation has a negative impact on the surrounded ecosystem. Despite this, this lake provides an interesting recreation setting in terms of ecotourism. The prediction and forecasting of the water level of the lake through simple but practical methods can provide a reliable tool for future lake water resource management. In the present study, we predict the daily water level of Zrebar Lake in Iran through well-known decision tree-based algorithms, including the M5 pruned (M5P), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). We used five different water input combinations to find the most effective one. For our modeling, we chose 70% of the dataset for training (from 2011 to 2015) and 30% for model evaluation (from 2015 to 2017). We evaluated the models’ performances using different quantitative (root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), percent bias (PBIAS) and ratio of the root mean square error to the standard deviation of measured data (RSR)) and visual frameworks (Taylor diagram and box plot). Our results showed that water level with a one-day lag time had the highest effect on the result and, by increasing the lag time, its effect on the result was decreased. This result indicated that all the developed models had a good prediction capability, but the M5P model outperformed the others, followed by RF and RT equally and then REPT. Our results showed that these algorithms can predict water level accurately only with a one-day lag time in water level as an input and they are cost-effective tools for future predictions.

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