scholarly journals Topographic Map Analysis of Laramie Range Bedrock-Walled Canyon Complex and the Goshen Hole Escarpment-Surrounded Basin, Albany and Platte Counties, Southeast Wyoming, USA

2018 ◽  
Vol 08 (01) ◽  
pp. 33-55
Author(s):  
Eric Clausen
Keyword(s):  
Topographic Map ◽  
Laramie Range ◽  
Map Analysis

scholarly journals Topographic Map Analysis of High Elevation Black Hills Through Valleys Linking Spearfish and Rapid Creek Headwaters Valleys, Lawrence County, South Dakota, USA

2017 ◽  
Vol 10 (1) ◽  
pp. 8
Author(s):  
Eric Clausen
Keyword(s):  
South Dakota ◽  
Drainage Basin ◽  
Water Source ◽  
High Elevation ◽  
Black Hills ◽  
Topographic Map ◽  
Drainage Basins ◽  
Converging Channel ◽  
Lawrence County ◽  
Map Analysis

The Spearfish-Rapid Creek drainage extends from elevations greater than 7130 feet (2173 meters) roughly in a north direction across the northern Black Hills upland to where it becomes the Spearfish-Whitewood Creek drainage divide at an elevation of approximately 6440 feet (1963 meters) and separates north-oriented Spearfish Creek headwaters from southeast- and east-oriented Rapid Creek headwaters. This study used detailed topographic maps to investigate through valleys (and wind gaps) now crossing the Spearfish-Rapid Creek drainage divide, which is one of the Black Hills’ highest drainage divides. Through valley (or wind gap) floor elevations were determined and ranged from approximately 6150 feet (1875 meters) to approximately 7050 feet (2149 meters) and through valley (and wind gap) depths were also calculated and ranged from approximately 30 feet (9 meters) to about 290 feet (88 meters). Map evidence suggesting these through valleys (and wind gaps) originated as components of diverging and converging complexes of bedrock-walled channels is described and suggests large and prolonged southeast-oriented floods once flowed from or across the Spearfish Creek drainage basin to the Rapid Creek drainage basin. Based on today’s topography there is no upland Black Hills region capable of generating the large and prolonged floods required to erode the observed through valleys (and wind gaps) and their associated diverging and converging channel complexes so the erosion is interpreted to have taken place while the Black Hills were just beginning to emerge as the topographic high they are today. A water source could not be determined from map evidence, but large and prolonged southeast-oriented floods across the region are consistent with a recently proposed hypothesis that massive southeast-oriented (continental ice sheet) ice-marginal melt water floods eroded what are today western South Dakota and North Dakota river drainage basins. 

Topographic map analysis for driver cognitive distraction

2014 ◽  
Author(s):  
Hossam Almahasneh ◽  
Nidal Kamel ◽  
Nicolas Walter ◽  
Aamir S. Malik ◽  
Weng-Tink Chooi
Keyword(s):  
Topographic Map ◽  
Cognitive Distraction ◽  
Map Analysis

scholarly journals Topographic map analysis to determine Arjuno-Welirang volcanostratigraphy and implication for geothermal exploration

2017 ◽  
Vol 103 ◽  
pp. 012018
Author(s):  
Lestari Apriani ◽  
Joshua Satriana ◽  
Citra Aulian Chalik ◽  
Reza Syahputra Mulyana ◽  
Muhammad Hafidz ◽  
...  
Keyword(s):  
Topographic Map ◽  
Geothermal Exploration ◽  
Map Analysis

How toxicity of nanomaterials towards different species could be simultaneously evaluated: Novel multi-nano-read-across approach

2017 ◽  
Author(s):  
Natalia Sizochenko ◽  
Alicja Mikolajczyk ◽  
Karolina Jagiello ◽  
Tomasz Puzyn ◽  
Jerzy Leszczynski ◽  
...  
Keyword(s):  
Missing Values ◽  
Metal Oxide Nanoparticles ◽  
Machine Learning Techniques ◽  
Self Organizing Map ◽  
Related Data ◽  
Mammalian Cell Lines ◽  
Learning Techniques ◽  
Combination Of Methods ◽  
Map Analysis ◽  
Unique Chemical

Application of predictive modeling approaches is able solve the problem of the missing data. There are a lot of studies that investigate the effects of missing values on qualitative or quantitative modeling, but only few publications have been<br>discussing it in case of applications to nanotechnology related data. Current project aimed at the development of multi-nano-read-across modeling technique that helps in predicting the toxicity of different species: bacteria, algae, protozoa, and mammalian cell lines. In this study, the experimental toxicity for 184 metal- and silica oxides (30 unique chemical types) nanoparticles from 15 experimental datasets was analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis was developed. At the first step, hidden patterns of toxicity among the nanoparticles were identified using a combination of methods. Then the developed model that based on categorization of metal oxide nanoparticles’ toxicity outcomes was evaluated by means of combination of supervised and unsupervised machine learning techniques to find underlying factors responsible for toxicity.

How toxicity of nanomaterials towards different species could be simultaneously evaluated: Novel multi-nano-read-across approach

2017 ◽  
Author(s):  
Natalia Sizochenko ◽  
Alicja Mikolajczyk ◽  
Karolina Jagiello ◽  
Tomasz Puzyn ◽  
Jerzy Leszczynski ◽  
...  
Keyword(s):  
Missing Values ◽  
Metal Oxide Nanoparticles ◽  
Machine Learning Techniques ◽  
Self Organizing Map ◽  
Related Data ◽  
Mammalian Cell Lines ◽  
Learning Techniques ◽  
Combination Of Methods ◽  
Map Analysis ◽  
Unique Chemical

Application of predictive modeling approaches is able solve the problem of the missing data. There are a lot of studies that investigate the effects of missing values on qualitative or quantitative modeling, but only few publications have been<br>discussing it in case of applications to nanotechnology related data. Current project aimed at the development of multi-nano-read-across modeling technique that helps in predicting the toxicity of different species: bacteria, algae, protozoa, and mammalian cell lines. In this study, the experimental toxicity for 184 metal- and silica oxides (30 unique chemical types) nanoparticles from 15 experimental datasets was analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis was developed. At the first step, hidden patterns of toxicity among the nanoparticles were identified using a combination of methods. Then the developed model that based on categorization of metal oxide nanoparticles’ toxicity outcomes was evaluated by means of combination of supervised and unsupervised machine learning techniques to find underlying factors responsible for toxicity.

DEVELOPMENT OF A LAND DEGRADATION ASSESSMENT MODEL BASED ON FIELD INDICATORS ASSESSMENT AND PREDICTION METHODS IN WAI SARI, SUB-WATERSHED KAIRATU DISTRICT, WESTERN SERAM REGENCY, MALUKU PROVINCE, INDONESIA

2019 ◽  
Vol 2 (1) ◽  
pp. 071-084
Author(s):  
Silwanus M. Talakua ◽  
Rafael M. Osok
Keyword(s):  
Land Degradation ◽  
Soil Loss ◽  
Assessment Model ◽  
Small Scale ◽  
Small Islands ◽  
Topographic Map ◽  
Field Assessment ◽  
Field Indicators ◽  
Degradation Measurement ◽  
Very High

The study was conducted in Wai Sari sub-watershed, Western Seram Regency Maluku to develop an accurate land degradation assessment model for tropical small islands. The Stocking’s field land degradation measurement and RUSLE methods were applied to estimate soil loss by erosion and the results of both methods were statistically tested in order to obtain a correction factor. Field indicators and prediction data were measured on 95 slope units derived from the topographic map. The rates of soil loss were calculated according to both methods, and the results were used to classify the degree of land degradation. The results show that the degree of land degradation based on the field assessment ranges from none-slight (4.04 - 17.565 t/ha/yr) to very high (235.44 - 404.00 t/ha/yr), while the RUSLE method ranges from none-slight (0.04-4.59 t/ha/yr) to very high 203.90 - 518.13 t/ha/yr.  However, the RUSLE method shows much higher in average soil loss (133.4 t/ha/yr) than the field assessment (33.9 t/ha/yr). The best regression equation of  logD/RP = - 0.594 + 1.0 logK + 1.0 logLS + 1.0 logC or D = 0.2547xRxKxLSx CxP was found to be a more suitable land degradation assessment  model for a small-scale catchment area in the tropical small islands.

Geobotanical mapping of vegetation in "Stolby" Reserve

2002 ◽  
pp. 32-43
Author(s):  
V. I. Vlasenko ◽  
M. G. Erunova ◽  
I. S. Scerbinina
Keyword(s):  
Forest Management ◽  
Forest Tree ◽  
Forest Steppe ◽  
Topographic Map ◽  
Forest Types ◽  
Mapping Unit ◽  
Vegetation Map ◽  
North West ◽  
Mountain Part ◽  
Mountain Taiga

The reserve “Stolby” is characteristic key plot of the mountain-taiga and subtaiga-forest steppe altitudinal belts in the East Sayan Mountains, where anthropogenic influence is the least pronounced. It was founded in 1925, in 15 km southward of Krasnoyarsk city, on north-west spurs of the Western Sayan Mountains which adjoin closely to right bank of the Yenisei River bordering upon the Middle Siberian Plateau. Reserve's physiography is characterized by low mountain and middle mountain erosion-accumulation relief with absolute heights of 200-800 m. Low mountain part (200-500 m) is composed of loose sedimentary rocks. In the middle mountain part of the reserve (500-800 m) there are outcrops of sienite rocks of various stages of destruction. Vegetation and soils of the reserve change in agreement with absolute heights and climate. In low mountains spread the subtaiga and forest-steppe leaved-light needle forests on mountain grey forest soils (8.1 % of reserve territory); the middle mountain part is occupied by the light needle and dark needle taiga forests on mountain podzol soils (91.9 % of the area). As the basement for vegetation map we took the map of forest environments of reserve by T. N. Butorina compiled according to materials of land forest management of 1977 year. As the result of forest management near 2000 biogeocoenoses were distinguished. The type of biogeocoenosis, according to V. N. Sukachev, is selected as mapping unit. Biogeocoenoses were united into 70 groups of forest types, representing 21 series of associations which are reflected in the map legend (Fig. 1). The main goal of map is to show the territorial distribution of groups and series of types of biogeocoenoses in the main structural units - altitudinal be't complexes (ВПК) which are equivalents of altitudinal vegetation belts. For designation of forest tree species various kinds of hatches were used. Formations of Siberian pine, larch, pine, fir, spruce, birch and aspen forests are shown on the map. Within the ВПК arabic numerals show the groups of types of biogeocoenoses (forest types), united into series according to similarity of dominants in ground layer. The mountain-taiga ВПК includes the following series and groups of types of biogeocoenoses: dwarf-shrub-moss (1-4); sedge-moss (5-9); bilberry-low herb-moss (10-14); tall herb-sedge (15-19); tall herb-wood sour-moss (20-26); tall herb-small reed (27-32). The subtaiga-forest steppe ВГ1К embraces: shrub steppificated (33-34); shrub-forb steppificated (35-38): sedge- bilberry (39-40); sedge-forb (41-43); bracken (44); small reed-forb (45); bilberrv-forb- sedge (46, 47); forb-tall herb (48-51); tall herb (52-55); wet tall herb-small reed (56-59); fern-tall herb (60). Intrazonal phytocoenoses: brook tall herb (61-63); brook shrub (64-68); lichen-moss (69); cowberry (70). In 1999-2000 on the base of topographic map in a scale 1 : 25 000, map of forest environments, transformed by us into vegetation map of the reserve, M. J . Erunova and I. S. Scerbinina worked out an electronic variant. For this project the instrumental facilities of GIS, GeoDraw and GeoGraph (CGI IG RAS, Moscow) and programs of Geophyt were used.

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