scholarly journals Combining Artificial Neural Network and Ordinary Kriging to Predict Wetland Soil Organic Carbon Concentration in China’s Liao River Basin

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7005
Author(s):  
Yingdong Kang ◽  
Xiaoyan Li ◽  
Dehua Mao ◽  
Zongming Wang ◽  
Mingxuan Liang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
River Basin ◽  
Carbon Concentration ◽  
Ordinary Kriging ◽  
Wetland Soil ◽  
Organic Carbon Concentration ◽  
Artificial Neural

Accurate prediction of wetland soil organic carbon concentration and an understanding of its controlling factors are important for studying regional climate change and wetland carbon cycles; with that knowledge mechanisms can be put in place that are conducive to sustainable ecosystem management for environmental health. In this study, a hybrid approach combining an artificial neural network and ordinary kriging and 103 soil samples at three soil depth ranges (0–30, 30–60, and 60–100 cm) were used to predict wetland soil organic carbon concentration in China’s Liao River Basin. The model evaluation indicated that a combination of artificial neural network and ordinary kriging and limited soil samples achieved good performance in predicting wetland soil organic carbon concentration. Wetland soil organic carbon concentration in the Liao River Basin has apparent spatial and vertical heterogeneities with values decreasing from southeast to northwest and concentrates present mainly in the topsoil (0–30 cm). Mean wetland soil organic carbon concentration values at the three soil depths were 10.43 ± 0.38, 7.93 ± 0.25, and 7.61 ± 0.22 g/kg, respectively, which are smaller than those over other wetland regions in Northeast China. Terrain aspect contributed the most in predicting wetland soil organic carbon concentration at each of the three soil depths, followed by normalized difference vegetation index at 0–30 cm and mean annual precipitation at 30–60 and 60–100 cm. This study provides a framework method and baseline to quantify the soil organic carbon concentration dynamics in response to climatic and anthropogenic drivers.

scholarly journals Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

2014 ◽  
Vol 38 (6) ◽  
pp. 1794-1804 ◽  
Author(s):  
Yücel Tekin ◽  
Zeynal Tümsavas ◽  
Abdul Mounem Mouazen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Moisture Content ◽  
Least Squares ◽  
Near Infrared ◽  
Soil Samples ◽  
Dry Soil ◽  
Artificial Neural

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes

2010 ◽  
Vol 90 (1) ◽  
pp. 75-87 ◽  
Author(s):  
Z. Zhao ◽  
Q. Yang ◽  
G. Benoy ◽  
T L Chow ◽  
Z. Xing ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Resolution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Correlation Coefficient ◽  
Slope Position ◽  
Soil Maps ◽  
Coarse Resolution ◽  
Artificial Neural

Soil organic carbon (SOC) content is an important soil quality indicator that plays an important role in regulating physical, chemical and biological properties of soil. Field assessment of SOC is time consuming and expensive. It is difficult to obtain high-resolution SOC distribution maps that are needed for landscape analysis of large areas. An artificial neural network (ANN) model was developed to predict SOC based on parameters derived from digital elevation model (DEM) together with soil properties extracted from widely available coarse resolution soil maps (1:1 000 000 scale). Field estimated SOC content data extracted from high-resolution soil maps (1:10 000 scale) in Black Brook Watershed in northwestern New Brunswick, Canada, were used to calibrate and validate the model. We found that vertical slope position (VSP) was the most important variable that determines distributions of SOC across the landscape. Other variables such as slope steepness, and potential solar radiation (PSR) also had significant influence on SOC distributions. The prediction of the selected two-input-node SOC model (VSP and coarse resolution soil map recorded SOC as inputs) had a correlation coefficient of 0.92 with measured values, and model predicted SOC values had 47.9% of the total points within ±0.5% of the measured values and 70.6% within ±1% of the measured values. The prediction od the selected four-input-node model (VSP, slope steepness, PSR and coarse resolution SOC values as inputs) had a correlation coefficient of 0.98 with measured values and model predicted SOC values had 75% of the total points within ±0.5% of the measured values and 87% within ±1% of the measured values. The prediction of the five-input-nodes model with soil drainage as additional input had a correlation coefficient of 0.99 with measured values, and model predicted SOC values had 87% of the total points within ±0.5% of the measured values and 98% of the total points within ±1% of the measured values. The calibrated SOC prediction model was used to produce a high-resolution SOC map for the Black Brook Watershed and the resulting SOC distribution map is considered to be realistic. Results indicated that DEM-derived hydrological parameters together with widely available coarse resolution soil map data could be used to produce high-resolution SOC maps with the ANN method.Key words: Soil organic carbon, artificial neural network model, high-resolution soil maps, digital elevation model, vertical slope position

Long-term (47 yr) effects of tillage and frequency of summerfallow on soil organic carbon in a Dark Brown Chernozem soil in western Canada

2016 ◽  
Vol 96 (4) ◽  
pp. 347-350 ◽  
Author(s):  
Elwin G. Smith ◽  
H. Henry Janzen ◽  
Lauren Scherloski ◽  
Francis J. Larney ◽  
Benjamin H. Ellert
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Concentration ◽  
Western Canada ◽  
Conservation Value ◽  
Chernozem Soil ◽  
No Till ◽  
Organic Carbon Concentration ◽  
Annual Changes

After 47 yr of no-till and reduced summerfallow at Lethbridge, Alberta, soil organic carbon concentration and stocks increased 2.14 g kg−1 and 2.22 Mg ha−1, respectively, in the surface 7.5 cm layer. These findings confirmed the conservation value of reducing tillage and summerfallow. The annual changes were relatively small.

scholarly journals Artificial Neural Network Model for Estimation of Suspended Sediment Load in Krishna River Basin, India

2020 ◽  
Vol 9 (3) ◽  
pp. 3701-3705
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Suspended Sediment ◽  
River Basin ◽  
Flood Control ◽  
Sediment Load ◽  
Water Discharge ◽  
Suspended Sediment Load ◽  
Artificial Neural

The correct assessment of amount of sediment during design, management and operation of water resources projects is very important. Efficiency of dam has been reduced due to sedimentation which is built for flood control, irrigation, power generation etc. There are traditional methods for the estimation of sediment are available but these cannot provide the accurate results because of involvement of very complex variables and processes. One of the best suitable artificial intelligence technique for modeling this phenomenon is artificial neural network (ANN). In the current study ANN techniques used for simulation monthly suspended sediment load at Vijayawada gauging station in Krishna river basin, Andhra Pradesh, India. Trial & error method were used during the optimization of parameters that are involved in this model. Estimation of suspended sediment load (SSL) is done using water discharge and water level data as inputs. The water discharge, water level and sediment load is collected from January 1966 to December 2005. This approach is used for modelled the SSL. By considering the results, ANN has the satisfactory performance and more accurate results in the simulation of monthly SSL for the study location.

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