Application of Adaptive Neuro-Fuzzy Inference System (ANFIS) For Optimizing Nano-Biochar Application in Soil Remediation Projects in Chas

The use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) is investigated into this work for optimizing nano-biochar application in soil remediation projects in Chas, Bokaro, Jharkhand, India. The research addresses the critical need for effective soil remediation techniques in areas affected by industrial pollution and agricultural intensification. By leveraging ANFIS, an intelligent hybrid system that blends neural networks with fuzzy logic, we aim to enhance the precision and efficiency of nano-biochar application in soil remediation efforts. The study encompasses extensive field experiments, laboratory analyses, and computational modeling to develop a well ANFIS model for forecast optimal nano-biochar dosages based on various soil parameters and contaminant levels. Results demonstrate the superior performance of ANFIS in optimizing nano-biochar application compared to conventional methods, leading to improved soil quality indicators and reduced remediation time. This research contributes to the advancement of sustainable soil management practices and provides a valuable tool for environmental practitioners and policymakers in Chas and similar regions facing soil contamination challenges.

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Publication Details

Journal
Communications in Mathematics and Applications
Published
2024-12-31
DOI
https://doi.org/10.26713/cma.v15i5.2903
Primary Topic
Neural Networks and Applications
Type
article
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article

Application of Adaptive Neuro-Fuzzy Inference System (ANFIS) For Optimizing Nano-Biochar Application in Soil Remediation Projects in Chas

Ruchira Tiwari, Mira Sinha
2 links
Communications in Mathematics and Applications
Neural Networks and Applications
article

Application of Adaptive Neuro-Fuzzy Inference System (ANFIS) For Optimizing Nano-Biochar Application in Soil Remediation Projects in Chas

Ruchira Tiwari, Mira Sinha
article en

Abstract

The use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) is investigated into this work for optimizing nano-biochar application in soil remediation projects in Chas, Bokaro, Jharkhand, India. The research addresses the critical need for effective soil remediation techniques in areas affected by industrial pollution and agricultural intensification. By leveraging ANFIS, an intelligent hybrid system that blends neural networks with fuzzy logic, we aim to enhance the precision and efficiency of nano-biochar application in soil remediation efforts. The study encompasses extensive field experiments, laboratory analyses, and computational modeling to develop a well ANFIS model for forecast optimal nano-biochar dosages based on various soil parameters and contaminant levels. Results demonstrate the superior performance of ANFIS in optimizing nano-biochar application compared to conventional methods, leading to improved soil quality indicators and reduced remediation time. This research contributes to the advancement of sustainable soil management practices and provides a valuable tool for environmental practitioners and policymakers in Chas and similar regions facing soil contamination challenges.

Communications in Mathematics and ApplicationsVol. 15(5)
Indian Institute of Technology Dhanbad (IN), Jharkhand Rai University (IN)
Life in Land
Openalex Percentile: Top 72%
Neural Networks and Applications
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