Topographic data sets for Texas by river basin

1997 ◽  
Author(s):  
L.L. Tan
Keyword(s):  
River Basin ◽  
Data Sets ◽  
Topographic Data

Ontology-based integration of topographic data sets

2005 ◽  
Vol 7 (2) ◽  
pp. 97-106 ◽  
Author(s):  
Harry T. Uitermark ◽  
Peter J.M. van Oosterom ◽  
Nicolaas J.I. Mars ◽  
Martien Molenaar
Keyword(s):  
Data Sets ◽  
Topographic Data

scholarly journals Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1387 ◽  
Author(s):  
Le ◽  
Ho ◽  
Lee ◽  
Jung
Keyword(s):  
Neural Network ◽  
River Basin ◽  
Input Data ◽  
Short Term Memory ◽  
Flood Forecasting ◽  
Data Sets ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Hoa Binh

Flood forecasting is an essential requirement in integrated water resource management. This paper suggests a Long Short-Term Memory (LSTM) neural network model for flood forecasting, where the daily discharge and rainfall were used as input data. Moreover, characteristics of the data sets which may influence the model performance were also of interest. As a result, the Da River basin in Vietnam was chosen and two different combinations of input data sets from before 1985 (when the Hoa Binh dam was built) were used for one-day, two-day, and three-day flowrate forecasting ahead at Hoa Binh Station. The predictive ability of the model is quite impressive: The Nash–Sutcliffe efficiency (NSE) reached 99%, 95%, and 87% corresponding to three forecasting cases, respectively. The findings of this study suggest a viable option for flood forecasting on the Da River in Vietnam, where the river basin stretches between many countries and downstream flows (Vietnam) may fluctuate suddenly due to flood discharge from upstream hydroelectric reservoirs.

An appraisal of flood events using IMD, CRU, and CCSM4-derived meteorological data sets over the Vaigai river basin, Tamil Nadu (India)

2019 ◽  
Vol 5 (4) ◽  
pp. 1731-1744 ◽  
Author(s):  
Satish Nagalapalli ◽  
Arnab Kundu ◽  
R. K. Mall ◽  
D. Thattai ◽  
S. Rangarajan
Keyword(s):  
River Basin ◽  
Tamil Nadu ◽  
Meteorological Data ◽  
Data Sets ◽  
Flood Events

Rapid multi-scale analysis of near-surface geophysical anomaly maps: Application to an archaeo-geophysical data set

Geophysics ◽  
2020 ◽  
pp. 1-41 ◽  
Author(s):  
Jens Tronicke ◽  
Niklas Allroggen ◽  
Felix Biermann ◽  
Florian Fanselow ◽  
Julien Guillemoteau ◽  
...  
Keyword(s):  
Random Noise ◽  
Geophysical Data ◽  
Magnetic Data ◽  
Engineering Practice ◽  
Data Sets ◽  
Stripe Pattern ◽  
Data Set ◽  
Near Surface ◽  
Multi Scale ◽  
Topographic Data

In near-surface geophysics, ground-based mapping surveys are routinely employed in a variety of applications including those from archaeology, civil engineering, hydrology, and soil science. The resulting geophysical anomaly maps of, for example, magnetic or electrical parameters are usually interpreted to laterally delineate subsurface structures such as those related to the remains of past human activities, subsurface utilities and other installations, hydrological properties, or different soil types. To ease the interpretation of such data sets, we propose a multi-scale processing, analysis, and visualization strategy. Our approach relies on a discrete redundant wavelet transform (RWT) implemented using cubic-spline filters and the à trous algorithm, which allows to efficiently compute a multi-scale decomposition of 2D data using a series of 1D convolutions. The basic idea of the approach is presented using a synthetic test image, while our archaeo-geophysical case study from North-East Germany demonstrates its potential to analyze and process rather typical geophysical anomaly maps including magnetic and topographic data. Our vertical-gradient magnetic data show amplitude variations over several orders of magnitude, complex anomaly patterns at various spatial scales, and typical noise patterns, while our topographic data show a distinct hill structure superimposed by a microtopographic stripe pattern and random noise. Our results demonstrate that the RWT approach is capable to successfully separate these components and that selected wavelet planes can be scaled and combined so that the reconstructed images allow for a detailed, multi-scale structural interpretation also using integrated visualizations of magnetic and topographic data. Because our analysis approach is straightforward to implement without laborious parameter testing and tuning, computationally efficient, and easily adaptable to other geophysical data sets, we believe that it can help to rapidly analyze and interpret different geophysical mapping data collected to address a variety of near-surface applications from engineering practice and research.

scholarly journals Extraction of detailed level flood hazard zones using multi-temporal historical satellite data-sets – a case study of Kopili River Basin, Assam, India

2016 ◽  
Vol 8 (2) ◽  
pp. 792-802 ◽  
Author(s):  
Shivaprasad Sharma SV ◽  
Parth Sarathi Roy ◽  
Chakravarthi V ◽  
Srinivasarao G ◽  
Bhanumurthy V
Keyword(s):  
River Basin ◽  
Satellite Data ◽  
Flood Hazard ◽  
Data Sets ◽  
Multi Temporal ◽  
Detailed Level

scholarly journals Impact of the Accuracy of Land Cover Data sets on the Accuracy of Land Cover Change Scenarios in the Mono River Basin, Togo, West Africa

2019 ◽  
Vol 8 (1) ◽  
pp. 3073-3095 ◽  
Author(s):  
Djan’na H. Koubodana ◽  
◽  
Bernd Diekkrüger ◽  
Kristian Näschen ◽  
Julien Adounkpe ◽  
...  
Keyword(s):  
Land Cover ◽  
West Africa ◽  
Land Cover Change ◽  
River Basin ◽  
Data Sets ◽  
Land Cover Data

scholarly journals Changes in Extreme Precipitation across 30 Global River Basins

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1527
Author(s):  
Xin Feng ◽  
Zhaoli Wang ◽  
Xushu Wu ◽  
Jiabo Yin ◽  
Shuni Qian ◽  
...  
Keyword(s):  
River Basin ◽  
Extreme Precipitation ◽  
Yellow River ◽  
Recent Decade ◽  
Yellow River Basin ◽  
River Basins ◽  
Amur River Basin ◽  
Data Sets ◽  
Frequency Distributions ◽  
The Amur River

Extreme precipitation can cause disasters such as floods, landslides and crop destruction. A further study on extreme precipitation is essential for enabling reliable projections of future changes. In this study, the trends and frequency distribution changes in extreme precipitation across different major river basins around the world during 1960–2011 were examined based on two of the latest observational data sets respectively collected from 110,000 and 26,592 global meteorological stations. The results showed that approximately a quarter of basins have experienced statistically significant increase in maximum consecutive one-day, three-day and five-day precipitation (RX1day, RX3day and RX5day, respectively). In particular, dramatic increases were found in the recent decade for the Syr Darya River basin (SDR) and Amu Darya River basin (ADR) in the Middle East, while a decrease in RX3day and RX5day were seen over the Amur River basin in East Asia. One third of basins showed remarkable changes in frequency distributions of the three indices, and in most cases the distributions shifted toward larger amounts of extreme precipitation. Relative to the subperiod of 1960–1984, wider range of the three indices over SDR and ADR were detected for 1985–2011, indicating intensification along with larger fluctuations of extreme precipitation. However, some basins have frequency distributions shifting toward smaller amounts of RX3day and RX5day, such as the Columbia River basin and the Yellow River basin. The study has potential to provide the most up-to-date and comprehensive global picture of extreme precipitation, which help guide wiser public policies in future to mitigate the effects of these changes across global river basins.

Analysis of Precipitation Variability using Memory Based Artificial Neural Networks

2019 ◽  
Vol 10 (1) ◽  
pp. 29-42
Author(s):  
Shyama Debbarma ◽  
Parthasarathi Choudhury ◽  
Parthajit Roy ◽  
Ram Kumar
Keyword(s):  
River Basin ◽  
Daily Precipitation ◽  
Data Sets ◽  
Precipitation Series ◽  
Adaptive Memory ◽  
Mean Error ◽  
Daily Data ◽  
Barak River ◽  
Ann Models ◽  
Statistical Criteria

This article analyzes the variability in precipitation of the Barak river basin using memory-based ANN models called Gamma Memory Neural Network(GMNN) and genetically optimized GMNN called GMNN-GA for precipitation downscaling precipitation. GMNN having adaptive memory depth is capable techniques in modeling time varying inputs with unknown input characteristics, while an integration of the model with GA can further improve its performances. NCEP reanalysis and HadCM3A2 (a) scenario data are used for downscaling and forecasting precipitation series for Barak river basin. Model performances are analyzed by using statistical criteria, RMSE and mean error and are compared with the standard SDSM model. Results obtained by using 24 years of daily data sets show that GMNN-GA is efficient in downscaling daily precipitation series with maximum daily annual mean error of 6.78%. The outcomes of the study demonstrate that execution of the GMNN-GA model is superior to the GMNN and similar with that of the standard SDSM.

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