Identifying Sources of Soil Pollution in Industrial Areas Using IoT Data and Neural Networks

• Published in: 2024 Asian Conference on Intelligent Technologies (ACOIT) pp. 1 - 6
• Main Authors: Kovarasan, Rajesh Kambattan, Shibu, Dr N V, Sairam, A., Manivannan, K. K., Ishwarya, M.V, GaneshBabu, T. R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06.09.2024
• Subjects: Contamination; Environmental monitoring; Environmentally friendly manufacturing techniques; Industrial emissions; Internet of Things; Neural networks; Pollution; Pollution control; Pollution mitigation; Pollution source identification; Remediation strategies; Soil; Soil contamination; Soil pollution; Stakeholders; Technological innovation
• ISBN: 9798350374933
• DOI: 10.1109/ACOIT62457.2024.10939152

Quick and precise source identification is crucial for developing successful remediation solutions to address soil contamination in industrial locations, which presents serious dangers to both the environment and human health. To locate the origins of soil contamination, this research presents a new method that combines neural network models with data collected from the Internet of Things (IOT). To monitor soil type, pollution levels, and weather conditions in real-time, set up IoT devices. After that, advanced neural network algorithms are used to process and analyze the data to find connections and patterns that might indicate the origins of the pollution. Our findings show that this method is useful for identifying where soil pollution inside industrial zones is coming from. Achieve high accuracy in source identification by integrating IoT technology and machine learning approaches. This allows us to address environmental harm and protect public health via focused remediation activities. Policymakers, environmental scientists, and stakeholders engaged in sustainable development and pollution control may benefit greatly which highlight the potential of IoT data and neural networks in challenges difficult environmental concerns.