Timely Decision Support for Watershed Management with WEPPcloud

2021 ◽  
Author(s):  
Erin Brooks ◽  
Mariana Dobre ◽  
Roger Lew ◽  
Chinmay Deval ◽  
Anurag Srivastava ◽  
...  
Keyword(s):  
Decision Support ◽  
Soil Erosion ◽  
Real Time ◽  
Watershed Management ◽  
Multiple Scales ◽  
Data Retrieval ◽  
Climatic Data ◽  
Watershed Model ◽  
Management Decisions ◽  
Erosion Models

<p>Since the development and availability of GIS-based software and satellite imagery, there has been a vision that watershed managers would have near-real-time, three-dimensional hydrologic and soil erosion models that could easily assess impacts of watershed management decisions at high spatial resolutions across multiple scales.  Our research team has made significant advances to address this challenging problem especially in the forest environment. The technology and data retrieval and access has dramatically improved to the point where it is possible to provide useful, near-real-time, geospatial decision support for watershed managers.  This talk describes an online watershed model called WEPPcloud, widely used by the Forest Service and one of the FSWEPP suite of watershed tools, which is based fundamentally on a process-based hydrologic, soil erosion model (WEPP, Water Erosion Prediction Project).  WEPPcloud is driven by discoverable, data-rich geospatial mapping products (e.g. soils, topography, satellite-based vegetation characteristics) and management libraries. It accesses daily grid-based historical and future projected climatic data to provide a comprehensive spatially and temporally explicit assessment of the impacts of management decisions on hydrologic response and sediment transport.  Currently, WEPPcloud can be applied throughout the continental US, and beta versions are available for Australia and Europe. We will demonstrate this tools’ development and application to guide pre-fire fuel management and post-fire mitigation, flood risk for communities where drinking water supplies and water resources are vulnerable to wildfire. We will discuss the ongoing limitations, challenges and opportunities towards more fully incorporating geospatial hydrologic and soil erosion models into watershed management decisions.</p>

778-P: The Impact of Self-Management Blood Glucose (SMBG) Real-Time Decision Support on Glycemic Control in Adult with T1D in China

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 778-P
Author(s):  
ZIYU LIU ◽  
CHAOFAN WANG ◽  
XUEYING ZHENG ◽  
SIHUI LUO ◽  
DAIZHI YANG ◽  
...  
Keyword(s):  
Decision Support ◽  
Blood Glucose ◽  
Glycemic Control ◽  
Real Time ◽  
Self Management ◽  
The Impact

scholarly journals A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Real Time ◽  
Forest Fires ◽  
Wildland Fire ◽  
Fire Spread ◽  
Low Earth Orbit ◽  
Practical Implementation ◽  
Spatiotemporal Resolution ◽  
Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

scholarly journals A Set of Integral Grid-Coding Algebraic Operations Based on GeoSOT-3D

2021 ◽  
Vol 10 (7) ◽  
pp. 489
Author(s):  
Kaihua Hou ◽  
Chengqi Cheng ◽  
Bo Chen ◽  
Chi Zhang ◽  
Liesong He ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Information ◽  
Data Retrieval ◽  
Geospatial Data ◽  
Algebraic Operation ◽  
Real Time Processing ◽  
C Language ◽  
Time Processing ◽  
Binary Operations ◽  
Set Operations

As the amount of collected spatial information (2D/3D) increases, the real-time processing of these massive data is among the urgent issues that need to be dealt with. Discretizing the physical earth into a digital gridded earth and assigning an integral computable code to each grid has become an effective way to accelerate real-time processing. Researchers have proposed optimization algorithms for spatial calculations in specific scenarios. However, a complete set of algorithms for real-time processing using grid coding is still lacking. To address this issue, a carefully designed, integral grid-coding algebraic operation framework for GeoSOT-3D (a multilayer latitude and longitude grid model) is proposed. By converting traditional floating-point calculations based on latitude and longitude into binary operations, the complexity of the algorithm is greatly reduced. We then present the detailed algorithms that were designed, including basic operations, vector operations, code conversion operations, spatial operations, metric operations, topological relation operations, and set operations. To verify the feasibility and efficiency of the above algorithms, we developed an experimental platform using C++ language (including major algorithms, and more algorithms may be expanded in the future). Then, we generated random data and conducted experiments. The experimental results show that the computing framework is feasible and can significantly improve the efficiency of spatial processing. The algebraic operation framework is expected to support large geospatial data retrieval and analysis, and experience a revival, on top of parallel and distributed computing, in an era of large geospatial data.

Autonomous Performance Monitoring of an On-Line Real-Time Decision Support System

2011 ◽  
Vol 44 (1) ◽  
pp. 13080-13085 ◽  
Author(s):  
Michael Lees ◽  
Rob Evans ◽  
Iven Mareels
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Performance Monitoring ◽  
On Line
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