Application of Artificial Intelligence for Predicting Erosion of Biochar Amended Soils

Ankit Garg, Insha Wani and Vinod Kushvaha
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Ankit Garg: Guangdong Engineering Center for Structure Safety and Health Monitoring, Shantou University, Shantou 515063, China
Insha Wani: Guangdong Engineering Center for Structure Safety and Health Monitoring, Shantou University, Shantou 515063, China
Vinod Kushvaha: Indian Institute of Technology Jammu, Jammu 181221, India

Sustainability, 2022, vol. 14, issue 2, 1-19

Abstract: Recently, incentives have been provided in developed countries by the government for commercial production of biochar for soil treatment, and other construction uses with an aim to reduce a significant amount of carbon emissions by 2030. Biochar is an important material for the development of circular economy. This study aims to develop a simple Artificial Neural Network (ANN) based model to predict erosion of biochar amended soils (BAS) under varying conditions (slope length, slope gradient, rainfall rate, degree of compaction (DoC), and percentage of biochar amendments). Accordingly, a model has been developed to estimate the total erosion rate and total water flow rate as a function of the above conditions. The model was developed based on available data from flume experiments. Based on ANN modelling results, it was observed that slope length was the most important factor in determining total erosion rate, followed by slope gradient, DoC, and percentage of biochar amendment. The percentage of biochar amendment was a leading factor in the total water flow rate determination as compared to other factors. It was also found that the reduction in erosion is relatively minimal during an increase in slope length up to 1.55 m, reducing sharply beyond that. At a slope length of 2 m, erosion is found to be reduced by 33% (i.e., 2.6 to 1.75), whereas the total flow rate decreases linearly from 1250 mL/m 2 /min to 790 mL/m 2 /min. The ANN model developed shows that soil biochar composite (SBC) with 5% biochar amendment gave the best results in reducing soil erosion. This study can be a helpful tool in providing preliminary guidelines for using biochar in erosion control.

Keywords: soil erosion; artificial neural network; biochar; soil management; circular economy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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