scholarly journals A Python Algorithm for Shortest-Path River Network Distance Calculations Considering River Flow Direction

Data ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 8
Author(s):  
Nicolas Cadieux ◽  
Margaret Kalacska ◽  
Oliver T. Coomes ◽  
Mari Tanaka ◽  
Yoshito Takasaki
Keyword(s):  
Shortest Path ◽  
River Flow ◽  
Flow Direction ◽  
Distance Matrix ◽  
River Networks ◽  
Shortest Path Algorithm ◽  
Target Node ◽  
Network Distance ◽  
Network Algorithms ◽  
Shortest Path Analysis

Vector based shortest path analysis in geographic information system (GIS) is well established for road networks. Even though these network algorithms can be applied to river layers, they do not generally consider the direction of flow. This paper presents a Python 3.7 program (upstream_downstream_shortests_path_dijkstra.py) that was specifically developed for river networks. It implements multiple single-source (one to one) weighted Dijkstra shortest path calculations, on a list of provided source and target nodes, and returns the route geometry, the total distance between each source and target node, and the total upstream and downstream distances for each shortest path. The end result is similar to what would be obtained by an “all-pairs” weighted Dijkstra shortest path algorithm. Contrary to an “all-pairs” Dijkstra, the algorithm only operates on the source and target nodes that were specified by the user and not on all of the nodes contained within the graph. For efficiency, only the upper distance matrix is returned (e.g., distance from node A to node B), while the lower distance matrix (e.g., distance from nodes B to A) is not. The program is intended to be used in a multiprocessor environment and relies on Python’s multiprocessing package.

scholarly journals An all-pairs shortest path algorithm for bipartite graphs

2013 ◽  
Vol 3 (4) ◽  
Author(s):  
Svetlana Torgasin ◽  
Karl-Heinz Zimmermann
Keyword(s):  
Shortest Path ◽  
Time Complexity ◽  
Shortest Paths ◽  
Distance Matrix ◽  
Bipartite Graphs ◽  
Matrix Product ◽  
Complex Structures ◽  
Shortest Path Algorithm ◽  
Order Of Magnitude ◽  
All Pairs Shortest Path

AbstractBipartite graphs are widely used for modeling of complex structures in biology, engineering, and computer science. The search for shortest paths in such structures is a highly demanded procedure that requires optimization. This paper presents a variant of the all-pairs shortest path algorithm for bipartite graphs. The method is based on the distance matrix product and improves the general algorithm by exploiting the graph topology. The space complexity is reduced by a factor of at least four and the time complexity decreased by almost an order of magnitude when compared with the basic APSP algorithm.

scholarly journals A new vector-based global river network dataset accounting for variable drainage density

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Peirong Lin ◽  
Ming Pan ◽  
Eric F. Wood ◽  
Dai Yamazaki ◽  
George H. Allen
Keyword(s):  
Spatial Variability ◽  
Flow Direction ◽  
River System ◽  
Drainage Density ◽  
River Network ◽  
River Networks ◽  
Digital Elevation ◽  
Machine Learning Approach ◽  
Elevation Model ◽  
National Hydrography Dataset

AbstractSpatial variability of river network drainage density (Dd) is a key feature of river systems, yet few existing global hydrography datasets have properly accounted for it. Here, we present a new vector-based global hydrography that reasonably estimates the spatial variability of Dd worldwide. It is built by delineating channels from the latest 90-m Multi-Error-Removed Improved Terrain (MERIT) digital elevation model and flow direction/accumulation. A machine learning approach is developed to estimate Dd based on the global watershed-level climatic, topographic, hydrologic, and geologic conditions, where relationships between hydroclimate factors and Dd are trained using the high-quality National Hydrography Dataset Plus (NHDPlusV2) data. By benchmarking our dataset against HydroSHEDS and several regional hydrography datasets, we show the new river flowlines are in much better agreement with Landsat-derived centerlines, and improved Dd patterns of river networks (totaling ~75 million kilometers in length) are obtained. Basins and estimates of intermittent stream fraction are also delineated to support water resources management. This new dataset (MERIT Hydro–Vector) should enable full global modeling of river system processes at fine spatial resolutions.

Utilizing Restricted Direction Strategy and Binary Heap Technology to Optimize Dijkstra Algorithm in WebGIS

2009 ◽  
Vol 419-420 ◽  
pp. 557-560 ◽  
Author(s):  
Rui Li
Keyword(s):  
Shortest Path ◽  
Operating Efficiency ◽  
Dijkstra Algorithm ◽  
Shortest Path Algorithm ◽  
Research Focus ◽  
Network Information ◽  
The Core ◽  
Highway Network ◽  
Core Issue ◽  
Storage Structures

Shortest path is the core issue in application of WebGIS. Improving the efficiency of the algorithm is an urgent requirement to be resolved at present. By the lossy algorithm analyzing, which is the current research focus of the shortest path algorithm to optimize, utilizing adjacency table of storage structures, restricted direction strategy and binary heap technology to optimize the algorithm, thereby reduce the scale of algorithm to improve the operating efficiency of algorithm. This scheme has been applied in the simulation of the data downloaded from the Guangdong Provincial Highway Network Information System and satisfactory results have been obtained.

scholarly journals Shortest path algorithm for optimal sectioning of hydrocarbon transport pipeline

2016 ◽  
Vol 49 (12) ◽  
pp. 532-537
Author(s):  
A. Cano-Acosta ◽  
John Fontecha ◽  
Nubia Velasco ◽  
Felipe Muñoz-Giraldo
Keyword(s):  
Shortest Path ◽  
Shortest Path Algorithm ◽  
Hydrocarbon Transport
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