Stream power index for networks (SPIN) toolbox for decision support in urbanizing watersheds

2021 ◽  
pp. 105185
Author(s):  
K. Ghunowa ◽  
B.J. MacVicar ◽  
P. Ashmore
Keyword(s):  
Decision Support ◽  
Power Index ◽  
Stream Power ◽  
Stream Power Index

scholarly journals Landform classification using GIS techniques. The case of Kimi municipality area, Euboea Island, Greece.

2016 ◽  
Vol 47 (1) ◽  
pp. 264 ◽  
Author(s):  
I. Ilia ◽  
D. Rozos ◽  
I. Koumantakis
Keyword(s):  
Spatial Analysis ◽  
Power Index ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Wetness Index ◽  
Topographic Position Index ◽  
Landform Classification ◽  
Essential Parameter ◽  
Position Index ◽  
Stream Power Index

The main objective of this paper is to classify landforms in Kimi municipality area of Euboea Island, Greece using advanced spatial techniques. Landform categories were determined by conducting morphometric analysis through the use of advanced GIS functions. In particular, the process of classifying the landscape into landform categories was based on Topographic Position Index (TPI). The main topographic elements such as slope inclination, aspect, slope shape (curvature), topographic wetness index and stream power index were obtained from the DEM file of the study area. Landform classification was obtained using TPI grids and the classes were related with the geological pattern and the land cover by sophisticated spatial analysis function. The knowledge obtained from the present study could be useful in identifying areas prone to land degradation and instability problems in which landforms are identified as an essential parameter

scholarly journals Application of Fuzzy Best Worse Multi Criteria Decision Making Method for Flood Prioritization

2021 ◽  
Author(s):  
Sarita Gajbhiye Meshram ◽  
Ali reza Ildoromi ◽  
Mehdi Sepehri
Keyword(s):  
Decision Making ◽  
Water Conservation ◽  
Power Index ◽  
Hot Spot ◽  
Curve Number ◽  
Multi Criteria Decision Making ◽  
Stream Power ◽  
Stream Power Index ◽  
Flooded Areas

Abstract Flood is one of the major problems of the sad ekbatan watershed, northern of Hamadan province, Iran. This problem imposes high damages to the economic issue. Therefore, prioritization of the study area based on the flooding degree can be considered for identifying hot spot flooded areas for performing soil and water conservation practices. In this study, in order to prioritize sub-watersheds of the case study from viewpoint of flooding degree, five flood-related criteria i.e. entropy of drainage network (En), index of connectivity (IC), stream power index (SPI), curvature (C) and curve number (CN) were considered, then fuzzy based Best Worse Multi Criteria Decision Making (F-BWM) Method was used to assigning weights to used criteria and combination them to achieve flooding degree for each sub-watershed. The results of prioritization of sub-watersheds indicated that the sub-watersheds 14 and 21 are most and least susceptibility areas to flooding correspondingly.

scholarly journals Can Lumped Characteristics of a Contributing Area Provide Risk Definition of Sediment Flux?

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1787
Author(s):  
Barbora Jáchymová ◽  
Josef Krása ◽  
Tomáš Dostál ◽  
Miroslav Bauer
Keyword(s):  
Czech Republic ◽  
Large Scale ◽  
Power Index ◽  
Arable Land ◽  
Sediment Flux ◽  
Residential Areas ◽  
Stream Power ◽  
Watershed Area ◽  
Risk Definition ◽  
Stream Power Index

Accelerated soil erosion by water has many offsite impacts on the municipal infrastructure. This paper discusses how to easily detect potential risk points around municipalities by simple spatial analysis using GIS. In the Czech Republic, the WaTEM/SEDEM model is verified and used in large scale studies to assess sediment transports. Instead of computing actual sediment transports in river systems, WaTEM/SEDEM has been innovatively used in high spatial detail to define indices of sediment flux from small contributing areas. Such an approach has allowed for the modeling of sediment fluxes in contributing areas with above 127,484 risk points, covering the entire Czech Republic territory. Risk points are defined as outlets of contributing areas larger than 1 ha, wherein the surface runoff goes into residential areas or vulnerable bodies of water. Sediment flux indices were calibrated by conducting terrain surveys in 4 large watersheds and splitting the risk points into 5 groups defined by the intensity of sediment transport threat. The best sediment flux index resulted from the correlation between the modeled total sediment input in a 100 m buffer zone of the risk point and the field survey data (R2 from 0.57 to 0.91 for the calibration watersheds). Correlation analysis and principal component analysis (PCA) of the modeled indices and their relation to 11 lumped characteristics of the contributing areas were computed (average K-factor; average R-factor; average slope; area of arable land; area of forest; area of grassland; total watershed area; average planar curvature; average profile curvature; specific width; stream power index). The comparison showed that for risk definition the most important is a combination of morphometric characteristics (specific width and stream power index), followed by watershed area, proportion of grassland, soil erodibility, and rain erosivity (described by PC2).

scholarly journals Landslide susceptibility analysis using decision tree method, Phidim, Eastern Nepal

2013 ◽  
Vol 15 ◽  
pp. 69-76 ◽  
Author(s):  
Chandra Prakash Poudyal
Keyword(s):  
Decision Tree ◽  
Landslide Susceptibility ◽  
Power Index ◽  
Geographical Information ◽  
Susceptibility Map ◽  
Effective Parameters ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Landslide Susceptibility Map ◽  
Stream Power Index

The decision tree is one of the new methods used for the determination of landslide susceptibility in the study area. The Phidim area is selected for the application of this method. The total surface area is 168.07 sq. km, and is located at the eastern part of Nepal. There are total of 10 different data bases used for this study which are; geological formation, elevation, slope, curvature, aspect, stream power index, topographic wetness index, distance from drainage, lineaments, and slope length, and are considered as landslide conditioning factors. Geographical information system (GIS) is used as basic tools and ARC/View is used for the processing data analysis and final map preparation. For the decision tree analysis the PASW 18 (statistical tool) is used to generate values of each factor. According to the results of decision tree, two geological formations; stream power index and slope are found as the most effective parameters on the landslide occurrence in the study area. Using the predicted values, the landslide susceptibility map of the study area is produced. To assess the performance of the produced susceptibility map, the area under curve (AUC) is drawn. The AUC value of the produced landslide susceptibility map has been obtained as 95.9%. According to the results of the AUC evaluation, the produced map has showed a good performance. As to wrap up, the produced map is able to be used for medium scaled and regional planning purposes. DOI: http://dx.doi.org/10.3126/bdg.v15i0.7419 Bulletin of the Department of Geology, Vol. 15, 2012, pp. 69-76

scholarly journals Automated Classification of Agricultural Lands Using GIS and NES

2018 ◽  
Vol 7 (4.38) ◽  
pp. 1146
Author(s):  
V. K. Kalichkin ◽  
A. I. Pavlova ◽  
A. F. Petrov ◽  
V. A. Smolyakov
Keyword(s):  
Land Use ◽  
Information System ◽  
Geographic Information System ◽  
Interpolation Method ◽  
Ground Surface ◽  
Geographic Information ◽  
Automated Classification ◽  
Stream Power ◽  
Stream Power Index

The article proposes the methodology for the automated classification of uplands using Geographic Information System (GIS) and Neural Expert System (NES). Quantitative indicators of topography are used as the basis of the proposed classification. A database consisting of topographic, soil, and land use maps was created using ArcGIS 10 geographic information system. A topologically correct digital elevation model (DEM) was created by the ANUDEM interpolation method. The DEM contains the following maps: hypsometric, steepness and slopes exposure, plan, profile, common curvature of the ground surface, and cumulative runoff maps. The boundaries of elementary surfaces (ES), which are homogeneous morphological formations, are established. Parameters characterizing the Stream Power Index (SPI) are taken into account. The essence of the proposed classification consists in attributing of ES to a certain group of lands based on aggregate of features. To do this, partial scales were created, containing indicators of topography, soil cover, land drainage conditions, as well as the degree of erosion development. The authors formed knowledge base for traning the NES using GIS database and partial scales of estimates. Teaching of neural network was carried out. The classification and topology of land was carried out by means of the NES. The uplands are distributed in flat and slightly convex areas. They are characterized by the following indicators: the curvature of the ground surface: plan curvature (0 – 0.03), profile curvature (0 – 0.15), common curvature (0 – 0.22); slope angles (less than 1.5о); horizontal dissection in elevation (less than 0.5 km/km2), vertical dissection (less than 5 m); and SPI (from -13.80 to -6.47). Electronic map of uplands of LLC «Salair» land-use area was created in the ArcGIS 10 environment.  

scholarly journals Harris Hawks Optimization: A Novel Swarm Intelligence Technique for Spatial Assessment of Landslide Susceptibility

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3590 ◽  
Author(s):  
Bui ◽  
Moayedi ◽  
Kalantar ◽  
Osouli ◽  
Gör ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Characteristic Curve ◽  
Absolute Error ◽  
Slope Aspect ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Conditioning Factors ◽  
Performance Error ◽  
Stream Power Index ◽  
Artificial Neural Network Ann

In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors—elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall—is prepared to develop the ANN and HHO–ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO–ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO–ANN = 0.773) the landslide pattern.

Assessment of shallow seated landslide size and magnitude characteristics: An example from Northeastern Turkey

2020 ◽  
Author(s):  
Kübra Tezel ◽  
Aykut Akgün ◽  
Ehsan Alizadeh
Keyword(s):  
Image Interpretation ◽  
Google Earth ◽  
Slope Gradient ◽  
Stream Power ◽  
Field Surveys ◽  
Clastic Rocks ◽  
Multiple Image ◽  
Investigation Area ◽  
Prone Area ◽  
Stream Power Index

<p>Landslides occurred in the Northeastern part of Turkey are generally classified to be shallow seated landslides or earthflow type based on Varnes (1984) classification. These landslides are occasionally seen in the weathered  Eocene or Upper Crateceous aged volcanic and volcano-clastic rocks. Although there are considerable studies both directly on these landslides in point of mapping and hazard assessment, there is no any studies concerning size and magnitude characteristics of them. By considering this point, an assessment of size and magnitude characteristics of shallow seated type landslides at an area where is one of most landslide prone area of Turkey was carried out.</p><p>The investigation area is totaly covered by Eocene aged volcano-clastic lithology, and the weathering is widespread due to the climatical conditions in the area. The extend of the area is 140 square kilometers. In the area, 120 landslides were mapped by a multiple image interpretation that is from the years of 2000 to 2019. To do this, Google Earth images were used. In the area, the area (A<sub>L</sub>) of the landslide mapped differs from 53.28 m<sup>2</sup> to 902,809 m<sup>2</sup>. The length and width of these landslide were also determined and these characteristics were taken into account for an assessment of relationship between the size and topographical features such as slope gradient, curvature, topographical wetness index and stream power index. The approximate volumes of these landslides were calculated by considering direct depth observations in the field surveys, and then assessed by different relations proposed by different studies. Magnitude (M) of these landslides were also assessed by taken into account of the area (A<sub>L</sub>) and volume (V<sub>L</sub>) values.</p>

Effects of Glacial Isostatic Adjustment on Surface Topography, Flow Accumulation,  Stream Power & Sediment Transport Indexes in the Canadian Prairies

2021 ◽  
Author(s):  
Patrick Wu ◽  
Tanghua Li ◽  
Holger Steffen
Keyword(s):  
Sediment Transport ◽  
North America ◽  
Surface Topography ◽  
Water Flow ◽  
Stream Power ◽  
Land Uplift ◽  
Flow Accumulation ◽  
Isostatic Adjustment ◽  
Stream Power Index ◽  
Differential Uplift

<p>Glacial Isostatic Adjustment (GIA) induced by the melting of the Pleistocene Ice Sheets causes differential land uplift, relative sea level and geoid changes. Thus, GIA in North America may affect water flow-accumulation and the rate of sedimentation and erosion in the South Saskatchewan River Basin (SSRB), but so far this has not been well investigated.</p><p> </p><p>Our aim here is to use surface topography in the SSRB and simple models of surface water flow to compute flow-accumulation, wetness index, stream power index and sediment transport index - the latter two affect the rates of erosion and sedimentation. Since the river basin became virtually ice-free around 8 ka BP, we shall study the effects of GIA induced differential land uplift during the last 8 ka on these indexes.</p><p> </p><p>Using the present-day surface topography ETOPO1 model, we see that the stream power index and sediment transport index in the SSRB may not be high enough to alter the surface topography significantly today and probably during the last 8 ka except for places around the Rocky Mountains. The effect of using 1 and 3 arc minute grid resolution of the ETOPO1 model does not significantly alter the value of these indexes. However, we note that using 1 arc minute grid is much more computationally intensive, so only a smaller area of the SSRB can be included in the computation.</p><p> </p><p>Next, we assume that sedimentation and erosion did not occur in the SSRB during the last 8 ka BP, and the change in surface topography is only due to GIA induced differential uplift. We use land uplift predicted by a large number of GIA models to study the changes in stream power & sediment transport indexes in the last 8 ka BP. Our base GIA model is ICE6G_C(VM5a). Then we investigate the effects of using uplift predicted by other GIA models that can still fit the observed relative sea level (RSL), uplift rate and gravity-rate-of-change data in North America reasonably well. These alternate GIA models have lateral heterogeneity in the mantle and lithosphere included – in particular we test those that give the largest differential uplift in the SSRB. We found that the effect of these other GIA earth models is not large on the stream power & sediment transport indexes. Finally, we investigate the sensitivity of these indexes on the ice models that are consistent with GIA observations. The results of this study will be useful to our understanding of water flow accumulation, sedimentation and erosion in the past, present and future and for water resource management in North America.</p>

scholarly journals The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

2021 ◽  
Vol 13 (5) ◽  
pp. 2459
Author(s):  
Soyoung Park ◽  
Jinsoo Kim
Keyword(s):  
Groundwater Resources ◽  
Management Strategies ◽  
Management Plan ◽  
Groundwater Potential ◽  
Gradient Boosting ◽  
Stream Power ◽  
Area Index ◽  
Conditioning Factors ◽  
Extreme Gradient Boosting ◽  
Stream Power Index

Understanding the potential groundwater resource distribution is critical for sustainable groundwater development, conservation, and management strategies. This study analyzes and maps the groundwater potential in Busan Metropolitan City, South Korea, using random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB) methods. Fourteen groundwater conditioning factors were evaluated for their contribution to groundwater potential assessment using an elastic net. Curvature, the stream power index, the distance from drainage, lineament density, and fault density were excluded from the subsequent analysis, while nine other factors were used to create groundwater potential maps (GMPs) using the RF, GBM, and XGB models. The accuracy of the resultant GPMs was tested using receiver operating characteristic curves and the seed cell area index, and the results were compared. The analysis showed that the three models used in this study satisfactorily predicted the spatial distribution of groundwater in the study area. In particular, the XGB model showed the highest prediction accuracy (0.818), followed by the GBM (0.802) and the RF models (0.794). The XGB model, which is the most recently developed technique, was found to best contribute to improving the accuracy of the GPMs. These results contribute to the establishment of a sustainable management plan for groundwater resources in the study area.

An illustrated introduction to general geomorphometry

2017 ◽  
Vol 41 (6) ◽  
pp. 723-752 ◽  
Author(s):  
Igor V Florinsky
Keyword(s):  
Analytical Description ◽  
Shape Index ◽  
Shuttle Radar Topography Mission ◽  
Slope Aspect ◽  
Triaxial Ellipsoid ◽  
Stream Power ◽  
Mathematical Concepts ◽  
Non Local ◽  
Stream Power Index ◽  
Horizontal Curvature

Geomorphometry is widely used to solve various multiscale geoscientific problems. For the successful application of geomorphometric methods, a researcher should know the basic mathematical concepts of geomorphometry and be aware of the system of morphometric variables, as well as understand their physical, mathematical and geographical meanings. This paper reviews the basic mathematical concepts of general geomorphometry. First, we discuss the notion of the topographic surface and its limitations. Second, we present definitions, formulae and meanings for four main groups of morphometric variables, such as local, non-local, two-field specific and combined topographic attributes, and we review the following 29 fundamental morphometric variables: slope, aspect, northwardness, eastwardness, plan curvature, horizontal curvature, vertical curvature, difference curvature, horizontal excess curvature, vertical excess curvature, accumulation curvature, ring curvature, minimal curvature, maximal curvature, mean curvature, Gaussian curvature, unsphericity curvature, rotor, Laplacian, shape index, curvedness, horizontal curvature deflection, vertical curvature deflection, catchment area, dispersive area, reflectance, insolation, topographic index and stream power index. For illustrations, we use a digital elevation model (DEM) of Mount Ararat, extracted from the Shuttle Radar Topography Mission (SRTM) 1-arc-second DEM. The DEM was treated by a spectral analytical method. Finally, we briefly discuss the main paradox of general geomorphometry associated with the smoothness of the topographic surface and the non-smoothness of the real topography; application of morphometric variables; statistical aspects of geomorphometric modelling, including relationships between morphometric variables and roughness indices; and some pending problems of general geomorphometry (i.e. analysis of inner surfaces of caves, analytical description of non-local attributes and structural lines, as well as modelling on a triaxial ellipsoid). The paper can be used as a reference guide on general geomorphometry.

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