• Veerakumar K
• Kathirvel A
• Salman J
• Santhosh A
• A P Roger Rozario

Shrimp ponds, also known as temporary water bodies formed by shrimp during periods of high flow, have received limited attention in water quality assessment studies. In this study, we propose a classification framework that leverages machine learning algorithms to analyze water quality data collected from shrimp ponds. The framework includes feature selection, data preprocessing, and model training steps to effectively classify water quality based on key indicators such as pH, dissolved oxygen, turbidity, and nutrient concentrations. We demonstrate the effectiveness of our approach through experiments conducted on real-world water quality datasets collected from various shrimp ponds. Our results show promising performance in accurately classifying water quality levels, thus providing valuable insights for environmental monitoring and management efforts. The classification results demonstrate the effectiveness of the proposed approach in accurately predicting water quality levels in shrimp ponds. By integrating advanced data analytics techniques with domain knowledge in hydrology and ecology, this research contributes to the development of efficient strategies for monitoring and managing water resources in shrimp pond ecosystems

"Classification of water quality using shrimp pond by utilizing machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.h63-h67, March-2024, Available :http://www.jetir.org/papers/JETIR2403712.pdf

"Classification of water quality using shrimp pond by utilizing machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. pph63-h67, March-2024, Available at : http://www.jetir.org/papers/JETIR2403712.pdf