Comparison of artificial neural networks, geographically weighted regression and Cokriging methods for predicting the spatial distribution of soil macronutrients (N, P, and K)

2017 ◽  
Vol 27 (5) ◽  
pp. 747-759 ◽  
Author(s):  
Samad Emamgholizadeh ◽  
Shahin Shahsavani ◽  
Mohamad Amin Eslami
Keyword(s):  
Neural Networks ◽  
Spatial Distribution ◽  
Artificial Neural Networks ◽  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Artificial Neural

scholarly journals LITHOLOGICAL INTERPRETATION ACCORDING TO WELL-LOGGING DATA FOR THE BAZHENOV FORMATION WITH USAGE OF ARTIFICIAL NEURAL NETWORKS

2021 ◽  
Vol 2 (3) ◽  
pp. 3-9
Author(s):  
Elena U. Temnikova ◽  
Serafim I. Grubas ◽  
Arsenii A. Fedoseev
Keyword(s):  
Neural Networks ◽  
Spatial Distribution ◽  
Artificial Neural Networks ◽  
Well Logging ◽  
Data Models ◽  
Relative Content ◽  
Material Composition ◽  
Bazhenov Formation ◽  
Modern Classification ◽  
Artificial Neural

Using artificial neural networks for lithological interpretation according to well logging data, models of the relative content of rock-forming components of the Bazhenov Formation were constructed and its main types of rocks were identified in accordance with a modern classification. Results of lithological interpretation were used for building correlation schemes, which made it possible to trace the spatial distribution of the material composition and main types of rocks of the Bazhenov Formation for the Salym field.

scholarly journals A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content

2019 ◽  
Vol 8 (4) ◽  
pp. 174 ◽  
Author(s):  
Lin Chen ◽  
Chunying Ren ◽  
Lin Li ◽  
Yeqiao Wang ◽  
Bai Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Organic Carbon ◽  
Geographically Weighted Regression ◽  
Ordinary Kriging ◽  
Linear Models ◽  
Weighted Regression ◽  
Hybrid Approaches ◽  
Better Than

Accurate digital soil mapping (DSM) of soil organic carbon (SOC) is still a challenging subject because of its spatial variability and dependency. This study is aimed at comparing six typical methods in three types of DSM techniques for SOC mapping in an area surrounding Changchun in Northeast China. The methods include ordinary kriging (OK) and geographically weighted regression (GWR) from geostatistics, support vector machines for regression (SVR) and artificial neural networks (ANN) from machine learning, and geographically weighted regression kriging (GWRK) and artificial neural networks kriging (ANNK) from hybrid approaches. The hybrid approaches, in particular, integrated the GWR from geostatistics and ANN from machine learning with the estimation of residuals by ordinary kriging, respectively. Environmental variables, including soil properties, climatic, topographic, and remote sensing data, were used for modeling. The mapping results of SOC content from different models were validated by independent testing data based on values of the mean error, root mean squared error and coefficient of determination. The prediction maps depicted spatial variation and patterns of SOC content of the study area. The results showed the accuracy ranking of the compared methods in decreasing order was ANNK, SVR, ANN, GWRK, OK, and GWR. Two-step hybrid approaches performed better than the corresponding individual models, and non-linear models performed better than the linear models. When considering the uncertainty and efficiency, ML and two-step approach are more suitable than geostatistics in regional landscapes with the high heterogeneity. The study concludes that ANNK is a promising approach for mapping SOC content at a local scale.

Determination of S-N curves with the application of artificial neural networks

1999 ◽  
Vol 22 (8) ◽  
pp. 723-728 ◽  
Author(s):  
Artymiak ◽  
Bukowski ◽  
Feliks ◽  
Narberhaus ◽  
Zenner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural
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