scholarly journals Development of pedotransfer functions by machine learning for prediction of soil electrical conductivity and organic carbon content

Geoderma ◽  
2020 ◽  
Vol 366 ◽  
pp. 114210
Author(s):  
K.K. Benke ◽  
S. Norng ◽  
N.J. Robinson ◽  
K. Chia ◽  
D.B. Rees ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electrical Conductivity ◽  
Organic Carbon ◽  
Carbon Content ◽  
Pedotransfer Functions ◽  
Organic Carbon Content ◽  
Soil Electrical Conductivity

scholarly journals Manipulation of Chemical Properties in Soil under Wetland Rice through Industrial Effluents

2019 ◽  
pp. 1-15
Author(s):  
Md. Rafiqul Islam ◽  
Golam Kibria Muhammad Mustafizur Rahman ◽  
Md. Abu Saleque
Keyword(s):  
Electrical Conductivity ◽  
Organic Carbon ◽  
Carbon Content ◽  
Rice Field ◽  
Soil Depth ◽  
Chemical Properties ◽  
Industrial Effluents ◽  
Rice Fields ◽  
Organic Carbon Content ◽  
Irrigated Rice

A laboratory experiment was conducted in Soil Science Division of Bangladesh Rice Research Institute (BRRI) during 2010-11 aimed to determine the effects of different industrial effluents on some soil chemical properties under long-term industrial wastewater irrigated rice field. Effluents irrigation created some differences in soil pH, electrical conductivity and organic carbon. The pH in all soil depth was higher with wastewater irrigated rice field. Irrigation with wastewater increased in all the effluents irrigated rice fields; the electrical conductivity (EC) was remarkable higher with  all soil depth than the control field. In all the rice fields soil (Control + effluents irrigated fields), the organic carbon content (%) started to decrease sharply with the increase in soil depth. Organic carbon content was slightly higher with wastewater irrigated rice soils. Exchangeable cations (Ca, Mg, K and Na), trace elements (Zn, Fe, Mn and Cu) and heavy metals (Pb, Cd, Cr and Ni) were increased through irrigation with wastewater in rice–rice cropping pattern.

scholarly journals Machine Learning-Based Production Prediction Model and Its Application in Duvernay Formation

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5509
Author(s):  
Zekun Guo ◽  
Hongjun Wang ◽  
Xiangwen Kong ◽  
Li Shen ◽  
Yuepeng Jia
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
Carbon Content ◽  
Gas Production ◽  
Fluid Volume ◽  
Total Organic Carbon Content ◽  
Organic Carbon Content ◽  
Gas Ratio

The production of a single gas well is influenced by many geological and completion factors. The aim of this paper is to build a production prediction model based on machine learning technique and identify the most important factor for production. Firstly, around 159 horizontal wells were collected, targeting the Duvernay Formation with detailed geological and completion records. Secondly, the key factors were selected using grey relation analysis and Pearson correlation. Then, three statistical models were built through multiple linear regression (MLR), support vector regression (SVR), gaussian process regression (GPR). The model inputs include fluid volume, proppant amount, cluster counts, stage counts, total horizontal lateral length, gas saturation, total organic carbon content, condensate-gas ratio. The model performance was assessed by root mean squared errors (RMSE) and R-squared value. Finally, sensitivity analysis was applied based on best performance model. The analysis shows following conclusions: (1) GPR model shows the best performance with the highest R-squared value and the lowest RMSE. In the testing set, the model shows a R-squared of 0.8 with a RMSE of 280.54 × 104 m3 in the prediction of cumulative gas production within 1st 6 producing months and gives a R-squared of 0.83 with a RMSE of 1884.3 t in the prediction of cumulative oil production within 1st 6 producing months (2) Sensitivity analysis based on GPR model indicates that condensate-gas ratio, fluid volume, and total organic carbon content are the most important features to cumulative oil production within 1st 6 producing months. Fluid volume, Stages, and total organic carbon content are the most significant factors to cumulative gas production within 1st 6 producing months. The analysis progress and results developed in this study will assist companies to build prediction models and figure out which factors control well performance.

Composted municipal waste effect on chosen properties of calcareous soil

2012 ◽  
Vol 26 (4) ◽  
pp. 365-374 ◽  
Author(s):  
M. Hamidpour ◽  
M. Afyuni ◽  
E. Khadivi ◽  
A. Zorpas ◽  
V. Inglezakis
Keyword(s):  
Electrical Conductivity ◽  
Hydraulic Conductivity ◽  
Organic Carbon ◽  
Carbon Content ◽  
Soil Ph ◽  
Calcareous Soil ◽  
Aggregate Stability ◽  
Municipal Waste ◽  
Organic Carbon Content ◽  
Compost Application

Abstract A 3-year field study was conducted to assess effects of composted municipal waste on some properties, distribution of Zn, Cu in a calcareous soil and uptake of these metals by wheat. The treatments were 0, 25, 50 and 100 Mg ha-1 of municipal solidwastewhichwas applied in three consecutive years. The application of composted municipal waste increased the saturated hydraulic conductivity, the aggregate stability,the organic carbon content and electrical conductivity, whereas it slightly decreased the soil pH and bulk density. A significant increase in the concentration of Zn and Cu were observed with increasing number and rate of compost application. The distribution of Zn and Cu between the different fractions in untreated and treated soils showed that the majority of Zn and Cu were in the residual form. Finally, the levels of Zn and Cu were higher in grains of wheat grown in composttreated plots compared to that grown in the control plots.

Combination of Fractional Order Differential and Machine Learning Algorithm for Spectral Estimation of Soil Organic Carbon Content

2020 ◽  
Vol 57 (15) ◽  
pp. 153001
Author(s):  
赵启东 Zhao Qidong ◽  
葛翔宇 Ge Xiangyu ◽  
丁建丽 Ding Jianli ◽  
王敬哲 Wang Jingzhe ◽  
张振华 Zhang Zhenhua ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Content ◽  
Fractional Order ◽  
Learning Algorithm ◽  
Spectral Estimation ◽  
Organic Carbon Content ◽  
Machine Learning Algorithm ◽  
Soil Organic Carbon Content

scholarly journals 3D mapping of soil organic carbon content and soil moisture with multiple geophysical sensors and machine learning

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Tobias Rentschler ◽  
Ulrike Werban ◽  
Mario Ahner ◽  
Thorsten Behrens ◽  
Philipp Gries ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Content ◽  
Organic Carbon Content ◽  
3D Mapping ◽  
Soil Organic Carbon Content
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