Predicting soil aggregate stability using readily available soil properties and machine learning techniques

CATENA ◽  
2020 ◽  
Vol 187 ◽  
pp. 104408 ◽  
Author(s):  
Javier I. Rivera ◽  
Carlos A. Bonilla
Keyword(s):  
Machine Learning ◽  
Soil Properties ◽  
Aggregate Stability ◽  
Soil Aggregate ◽  
Machine Learning Techniques ◽  
Soil Aggregate Stability ◽  
Learning Techniques

How Tree Encroachment and Soil Properties Affect Soil Aggregate Stability in an Eroded Grassland in South Africa

2014 ◽  
Vol 78 (5) ◽  
pp. 1753-1764 ◽  
Author(s):  
Pascal Podwojewski ◽  
Séraphine Grellier ◽  
Sandile Mthimkhulu ◽  
Louis Titshall
Keyword(s):  
South Africa ◽  
Soil Properties ◽  
Aggregate Stability ◽  
Soil Aggregate ◽  
Soil Aggregate Stability ◽  
Tree Encroachment

scholarly journals Machine learning approaches for the prediction of soil aggregate stability

Heliyon ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. e06480
Author(s):  
Yassine Bouslihim ◽  
Aicha Rochdi ◽  
Namira El Amrani Paaza
Keyword(s):  
Machine Learning ◽  
Aggregate Stability ◽  
Soil Aggregate ◽  
Learning Approaches ◽  
Soil Aggregate Stability

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

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.

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