scholarly journals Mapping soils in two watersheds using legacy data and extrapolation for similar surrounding areas

2016 ◽  
Vol 40 (5) ◽  
pp. 534-546 ◽  
Author(s):  
Marcelo Henrique Procópio Pelegrino ◽  
Sérgio Henrique Godinho Silva ◽  
Michele Duarte de Menezes ◽  
Elidiane da Silva ◽  
Phillip Ray Owens ◽  
...  
Keyword(s):  
Decision Trees ◽  
Soil Maps ◽  
Kappa Index ◽  
Digital Mapping ◽  
Legacy Data ◽  
Mapping Techniques ◽  
Surrounding Areas ◽  
Cart Algorithm ◽  
Source Of Information ◽  
Extract Information

ABSTRACT Existing soil maps (legacy data) associated with digital mapping techniques are alternatives to obtain information at lower costs, however, tests are required to do it more efficiently. This study had as objectives to compare different methods to extract information from detailed scale soil maps using decision trees for mapping soil classes at two watersheds in Minas Gerais, validate these maps in the field and use the best method to extrapolate information to larger areas, also validating these maps of larger areas. Detailed soil maps of Vista Bela creek (VBW) and Marcela creek (MCW) watersheds were used as source of information. Seven methods to extract information from maps were compared: the whole polygon, eliminating 20 and 40 m from the polygon boundaries, and with buffers around the sampled points with radii of 25 m, 50 m, 75 m, and 100 m. The Classification and Regression Trees (CART) algorithm was employed to create decision trees and enable creation of soil maps. Accuracy was assessed through overall accuracy and kappa index. The best method was used to extrapolate information to larger areas and maps were validated. The best methods for VCW and MCW were, respectively, eliminating 20 m from polygon edges and buffer of 25 m of radii from points. Maps for larger areas were obtained using these methods. Removing uncertainty areas from legacy soil maps contribute to better modeling and prediction of soil classes. Information generated in this work allowed for validated extrapolation of soil maps for regions surrounding the watersheds.

scholarly journals Innovative Mesosystems Algorithm for Sustainable Development Priority Areas Identification in Industry Based on Decision Trees Construction

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3055
Author(s):  
Aleksey I. Shinkevich ◽  
Irina G. Ershova ◽  
Farida F. Galimulina ◽  
Alla A. Yarlychenko
Keyword(s):  
Sustainable Development ◽  
Decision Trees ◽  
Training Sample ◽  
The Sustainable Development ◽  
Priority Areas ◽  
New Science ◽  
Development Methodology ◽  
Mathematical Processing ◽  
The Difference ◽  
Cart Algorithm

Globally, assessing sustainable development methodology is kept in sustainable society index (SSI) format, but at the level of meso- and microsystems it remains undeveloped. The aim of the study is to typologize innovative mesosystems in Russian industry in the context of sustainable development based on the CART algorithm and to develop an algorithm for identifying priority areas of sustainable development. The research methods applied included formalization, a systematic approach, and the CART algorithm (calculation of the Gini index, training sample segmentation, the use of a recursive function and regression assessment). As a result of the study, the algorithm for the differentiated identification of innovative mesosystems sustainable development priority directions in industry based on the unique author’s methodology (ISDI) is proposed. The predominance of mesosystems with weak level of sustainable development requiring state support in favor of such mesosystems restructure is revealed. The novelty of the research lies in the development of new science-based solutions to ensure an accelerated transition of industry to the path of sustainable development. The difference of the author’s approach from the provisions known in science is the inclusion of environmental innovations in the mechanism for managing the sustainable development of innovative mesosystems and subsequent accounting in the process of mathematical processing of an array of data, which determines the uniqueness of the constructed decision trees.

scholarly journals Analysis of Decision Tree Induction Algorithms

2019 ◽  
Vol 8 (11) ◽  
pp. e298111473
Author(s):  
Hugo Kenji Rodrigues Okada ◽  
Andre Ricardo Nascimento das Neves ◽  
Ricardo Shitsuka
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Decision Trees ◽  
Quantitative Study ◽  
Data Structures ◽  
Execution Time ◽  
Supervised Machine Learning ◽  
Decision Tree Induction ◽  
Classification And Regression ◽  
Cart Algorithm

Decision trees are data structures or computational methods that enable nonparametric supervised machine learning and are used in classification and regression tasks. The aim of this paper is to present a comparison between the decision tree induction algorithms C4.5 and CART. A quantitative study is performed in which the two methods are compared by analyzing the following aspects: operation and complexity. The experiments presented practically equal hit percentages in the execution time for tree induction, however, the CART algorithm was approximately 46.24% slower than C4.5 and was considered to be more effective.

Paleoecological Archives in Lake Deposits 2: Records from Important Groups

2003 ◽  
Author(s):  
Andrew S. Cohen
Keyword(s):  
Fossil Record ◽  
Late Quaternary ◽  
Isolated Cells ◽  
New Students ◽  
Surrounding Areas ◽  
General Aspects ◽  
Insect Fossils ◽  
Lake Deposits ◽  
Source Of Information ◽  
Lacustrine Communities

The lacustrine fossil record comprises a mixture of endogenic fossils, such as cladocerans, derived from lakes, and exogenic fossils, such as insects or pollen, which are carried into lakes, by wind and water from surrounding areas. Our primary emphasis here will be on the endogenic fossil record of lakes; we will only briefly consider general aspects of the taphonomy and paleoecological significance of exogenic fossils for terrestrial plant and insect fossils. Information about lake fossils varies greatly between groups. Some taxa, such as diatoms, are virtual workhorses of the field, with numerous investigators, and established methods of sampling, analysis, and interpretation. At the other extreme are organisms such as copepods, which, despite their importance in lacustrine ecosystems, are so poorly fossilized that they are unlikely to ever play a major role in paleolimnology. In between these extremes lie the majority of lacustrine organisms. Many relatively common groups have great potential for paleoecological interpretation, but, for reasons of inadequate study, a lack of researchers, or difficulties in taxonomy, have thus far been little used by paleolimnologists. Major opportunities await new students in the field who are willing to take up the challenges of studying these clades. Despite their importance in lacustrine communities, cyanobacteria remain a relatively unexploited source of information for paleolimnology. Isolated cells have poor preservation potential, and fossil cyanobacterial cells are preserved in Late Quaternary lake muds primarily by their more resistant reproductive spores (akinetes), or occasionally by filaments. Planktonic cyanobacteria are only rarely recorded in older sediments. In contrast, benthic cyanobacterial communities are well represented in ancient lake beds by their constructional deposits, lithified algal mats, stromatolites, and thrombolites. Although their body fossils have been used only rarely to solve paleolimnological problems, planktonic cyanobacteria have great potential for this purpose, given their obvious importance in many lacustrine communities. Relatively resistant akinetes might be very useful for understanding changes in plankton communities, especially in cases where better- studied siliceous microfossils (diatoms and chrysophytes) are not well preserved, for example, in very alkaline lakes. However, almost nothing is known of the taphonomic biases that control the planktonic cyanobacterial fossil record.

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