Stream Power Determination in GIS: An Index to Evaluate the Most ’Sensitive’Points of a River

Rosa, Pierluigi De; Fredduzzi, Andrea; Cencetti, Corrado

doi:10.3390/w11061145

Cited by 23 publications

(10 citation statements)

References 20 publications

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“…The stream power is the main power indicator that drives the shape (erosion and deposition processes) of a fluvial channel. Its determination is a really important task [15]. It is reasoned, therefore, that there exists a range of intermediate discharges that do most of the work of shaping a river channel and that some summary value of these intermediate flows represents the formative discharge of the river [16].…”

Section: The Bankfull Definitionmentioning

confidence: 99%

A GIS-Based Tool for Automatic Bankfull Detection from Airborne High Resolution Dem

Rosa

Fredduzzi

Cencetti

2019

IJGI

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The ability to remotely sense bankfull elevations was of particular interest in this study because bankfull mapping depends on topographic indicators. The method proposed here and integrated in a GIS environment combines the hydraulic depth and the flow height for each cross section. The local maxima values indicate a sudden increase in flow width where water spills across the floodplain. Such an approach has been implemented as a GIS tool in the QGIS software, and provides a resulting polygonal map of the bankfull limits. The algorithm was applied on several fluvial reaches in Umbria (central Italy). The source code is available as open source. Preliminary results are presented in Section 4, comparing remotely sensed bankfull limits to those obtained from fields surveys and, more recently, by operator-expert interpretation of aerial orthophotos.

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Section: The Bankfull Definitionmentioning

confidence: 99%

A GIS-Based Tool for Automatic Bankfull Detection from Airborne High Resolution Dem

Rosa

Fredduzzi

Cencetti

2019

IJGI

Self Cite

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“…This index, achieved by DEM processing in SAGA GIS 2.1.0, was classified according to the literature: (−99)–(−3), (−2.9)–(−2), (−1.9)–(−1), (−0.9)–0, 0–99 ( Figure 2 e). Stream Power Index (SPI) is another morphometric factor that is generated in SAGA GIS 2.1.0 based on the values of upslope region that drains into a pixel and the tangent applied to the slope angle [ 64 ]. This predictor, which shows the capacity of the river for sediment transport, was mapped using the following classes values: <50, 50–500, 501–2000, 2001–5000, >5000 ( Figure 2 f).…”

Section: Datamentioning

confidence: 99%

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

Costache

Arabameri

Blaschke

et al. 2021

Sensors

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There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network–Frequency Ratio (DLNN-FR), Deep Learning Neural Network–Weights of Evidence (DLNN-WOE), Alternating Decision Trees–Frequency Ratio (ADT-FR) and Alternating Decision Trees–Weights of Evidence (ADT-WOE). The model’s performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.

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“…STI considers the influence of topography on erosion and clarifies the sediment transport potential to characterize erosion and sedimentation processes [95]. SPI is used to quantify the erosive capacity of rivers, where the flow is a determinant of channel erosion and flood damage [96]. TRI is one of the main factors affecting river potential energy, surface water storage capacity, runoff velocity, and river path at the watershed scale, characterizing elevation differences between adjacent raster [97].…”

Section: Topographical Factorsmentioning

confidence: 99%

Dam Siting: A Review

Wang

Tian

Cao

2021

Water

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Dams can effectively regulate the spatial and temporal distribution of water resources, where the rationality of dam siting determines whether the role of dams can be effectively performed. This paper reviews the research literature on dam siting in the past 20 years, discusses the methods used for dam siting, focuses on the factors influencing dam siting, and assesses the impact of different dam functions on siting factors. The results show the following: (1) Existing siting methods can be categorized into three types—namely, GIS/RS-based siting, MCDM- and MCDM-GIS-based siting, and machine learning-based siting. GIS/RS emphasizes the ability to capture and analyze data, MCDM has the advantage of weighing the importance of the relationship between multiple factors, and machine learning methods have a strong ability to learn and process complex data. (2) Site selection factors vary greatly, depending on the function of the dam. For dams with irrigation and water supply as the main purpose, the site selection is more focused on the evaluation of water quality. For dams with power generation as the main purpose, the hydrological factors characterizing the power generation potential are the most important. For dams with flood control as the main purpose, the topography and geological conditions are more important. (3) The integration of different siting methods and the siting of new functional dams in the existing research is not sufficient. Future research should focus on the integration of different methods and disciplines, in order to explore the siting of new types of dams.

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Stream Power Determination in GIS: An Index to Evaluate the Most ’Sensitive’Points of a River

Cited by 23 publications

References 20 publications

A GIS-Based Tool for Automatic Bankfull Detection from Airborne High Resolution Dem

A GIS-Based Tool for Automatic Bankfull Detection from Airborne High Resolution Dem

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

Dam Siting: A Review

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