Lake bathymetry from Indian Remote Sensing (P6-LISS III) satellite imagery using artificial neural network model

2013 ◽  
Vol 18 (2) ◽  
pp. 145-153 ◽  
Author(s):  
Sheela A. Moses ◽  
Letha Janaki ◽  
Sabu Joseph ◽  
Jairaj P. Gomathi ◽  
Justus Joseph
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Network Model ◽  
Satellite Imagery ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Indian Remote Sensing ◽  
Liss Iii ◽  
Artificial Neural

scholarly journals Development of a Spatial Hydrologic Soil Map Using Spectral Reflectance Band Recognition and a Multiple-Output Artificial Neural Network Model

2017 ◽  
Author(s):  
Khamis Naba Sayl ◽  
Haitham Abdulmohsin Afan ◽  
Nur Shazwani Muhammad ◽  
Ahmed ElShafie
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Spectral Reflectance ◽  
Artificial Neural Network Model ◽  
Performance Criteria ◽  
Artificial Neural ◽  
Soil Groups

Abstract. Soil type is important in any civil engineering project. Thorough and comprehensive information on soils in both the spatial and temporal domains can assist in sustainable hydrological, environmental and agricultural development. Conventional soil sampling and laboratory analysis are generally time-consuming, costly and limited in their ability to retrieve the temporal and spatial variability, especially in large areas. Remote sensing is able to provide meaningful data, including soil properties, on several spatial scales using spectral reflectance. In this study, a multiple-output artificial neural network model was integrated with geographic information system, remote sensing and survey data to determine the distributions of hydrologic soil groups in the Horan Valley in the Western Desert of Iraq. The model performance was evaluated using seven performance criteria along with the hydrologic soil groups developed by the United States Geological Survey (USGS). On the basis of the performance criteria, the model performed best for predicting the spatial distribution of clay soil, and the predicted soil types agreed well with the soil classifications of the USGS. Most of the samples were categorized as sandy loam, whereas one sample was categorized as loamy sand. The proposed method is reliable for predicting the hydrological soil groups in a study area.

scholarly journals Using Artificial Neural Networks to Predict Influences of Heterogeneity on Rock Strength at Different Strain Rates

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3042
Author(s):  
Sheng Jiang ◽  
Mansour Sharafisafa ◽  
Luming Shen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Strain Rate ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Rock Strength ◽  
Strain Rates ◽  
Filling Material ◽  
Artificial Neural

Pre-existing cracks and associated filling materials cause the significant heterogeneity of natural rocks and rock masses. The induced heterogeneity changes the rock properties. This paper targets the gap in the existing literature regarding the adopting of artificial neural network approaches to efficiently and accurately predict the influences of heterogeneity on the strength of 3D-printed rocks at different strain rates. Herein, rock heterogeneity is reflected by different pre-existing crack and filling material configurations, quantitatively defined by the crack number, initial crack orientation with loading axis, crack tip distance, and crack offset distance. The artificial neural network model can be trained, validated, and tested by finite 42 quasi-static and 42 dynamic Brazilian disc experimental tests to establish the relationship between the rock strength and heterogeneous parameters at different strain rates. The artificial neural network architecture, including the hidden layer number and transfer functions, is optimized by the corresponding parametric study. Once trained, the proposed artificial neural network model generates an excellent prediction accuracy for influences of high dimensional heterogeneous parameters and strain rate on rock strength. The sensitivity analysis indicates that strain rate is the most important physical quantity affecting the strength of heterogeneous rock.

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