Morphometrical analysis of torrential flows-prone catchments in tropical and mountainous terrain of the Colombian Andes by machine learning techniques

2020 ◽  
Vol 105 (1) ◽  
pp. 983-1012
Author(s):  
María Isabel Arango ◽  
Edier Aristizábal ◽  
Federico Gómez
2021 ◽  
Author(s):  
Emanuel Castillo Cardona ◽  
Edier Aristizábal

<p>Debris flow fans are commonly occupied by urban and rural settlements in mountainous regions such as in the northern Colombian Andes. Those fans are originated by violent surges of high sediment concentration that are then mobilized downstream by strong currents during torrential events highly destructive. Then, characterization and understanding of the dynamics that give rise to fans in tropical and mountainous regions such as Andean zone is a fundamental tool for land use planning. This research focuses on cartography of fans and catchments using digital elevation models in the central and western mountain range of the northern part of the Andean mountain belt. The methodology considered: morphometric measurements of the catchments and fans, lithological aspects of the catchments, type of catchments (torrential or no torrential). Then the correlation between morphometric parameters of fans and catchments is carried out, including relationships with qualitative variables by multivariate statistical analysis and machine learning techniques to find patterns between quantitative and qualitative variables. The results indicate that slope of the fans has a high correlation with Melton index of the catchments and with the slope of the main stream of the catchments. About the qualitative classification of the catchments in torrential and no torrential, it is observed that there are good discriminations for slope of the fan, volume of the deposits(fans), the relationship between the relief of the catchments and other variables. On the other hand, the lithology of the catchments does not have strong influences on the morphometry of the fans.</p>


2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.


Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  

Author(s):  
Anantvir Singh Romana

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.


Author(s):  
Padmavathi .S ◽  
M. Chidambaram

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.


Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.


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