[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121377-en":3,"doc-seo-121377-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121377,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning-Based Fish Species Recommendation Using Water Quality Parameters","Machine learning enables data-driven fish species recommendations in aquaculture by leveraging measurable water quality conditions. Traditional fish farming often relies on manual monitoring and farmer intuition, causing inefficient species selection and unreliable outcomes that translate into economic losses. This paper introduces a software-based recommendation system that analyzes seven key parameters—pH, temperature, turbidity, TDS, dissolved oxygen, nitrate, and ammonia—using models such as Random Forest, XGBoost, and SVM, achieving over 90% accuracy. A GUI supports real-time user input and instant recommendations to improve efficiency and sustainability.","Machine Learning-Based Fish Species Recommendation Using  \nWater Quality Parameters  \nMuhammad Owais Khan1, Faheem Ul Haq1, Aasif Awan2 1Robotics Team Hadaf Group of Colleges Peshawar, Pakistan  \n2Lecturer Biotechnology Hadaf College of Allied Health Sciences, Peshawar, Pakistan  \n*[Correspondence](Correspondence:mowaiskhandev@gmail.com)[:](Correspondence:mowaiskhandev@gmail.com)[mowaiskhandev@gmail.com](Correspondence:mowaiskhandev@gmail.com),[engr.faheemulhaq@gmail.com](engr.faheemulhaq@gmail.com), [awan.aasif1@gmail.com](awan.aasif1@gmail.com)  \nCitation | Khan. M. O, Haq. F. U, Awan. A, “Machine Learning-Based Fish Species Recommendation Using Water Quality Parameters”, IJIST, Vol. 07 Special Issue. pp 110-126, May 2025  \nReceived| April 10, 2025 Revised| May 03, 2025 Accepted| May 07, 2025 Published| May 09, 2025.   \nThe integration of machine learning (ML) in aquaculture enables data-driven fish species  \nrecommendations based on water quality parameters. Traditional fish farming faces  \nchallenges like manual monitoring, inefficient species selection, and unpredictable water conditions, leading to economic losses. This paper presents a software-based fish recommendation system using ML models to analyze seven key water parameters: pH, Temperature, Turbidity, TDS, Dissolved Oxygen, Nitrate, and Ammonia. Various ML algorithms, including Random Forest, XGBoost, and SVM, were evaluated, with the optimized model achieving over 90% accuracy. A graphical user interface (GUI) allows users to input parameters and receive real-time recommendations, enhancing efficiency and sustainability in aquaculture.  \nKeywords: Fish Farming; Machine Learning; Water Quality Analysis; XGBoost; Smart Aquaculture.  \nIntroduction:  \nAquaculture has become an essential component of global food systems, contributing significantly to food security, nutrition, and economic development. As demand for fish continues to rise, modernizing aquaculture practices has become crucial. However, traditional fish farming remains largely dependent on manual water quality monitoring and farmer intuition, which often results in inefficient operations, inaccurate species selection, and vulnerability to environmental changes. These challenges can lead to poor yields, increased costs, and avoidable losses. Water quality parameters such as pH, temperature, total dissolved solids (TDS), turbidity, ammonia, dissolved oxygen, and nitrate directly influence fish health, growth, and survival. Monitoring these parameters manually is not only labor-intensive but also lacks the responsiveness required for real-time decision-making, especially in large-scale farming systems. To address these limitations, this study proposes a machine learning-based fish species recommendation system that predicts the most suitable species for a given aquatic environment. The objective is to support aquaculture decision-making by analyzing real-time water quality data using a variety of machine learning algorithms, including Random Forest, Decision Tree, XG-Boost, K- Nearest Neighbors, Support Vector Machine, and Logistic Regression. The system integrates preprocessing techniques such as feature scaling and dataset balancing to enhance prediction accuracy. In addition, a graphical user interface (GUI) was developed to allow farmers and aquaculture professionals to input water parameters and receive instant fish species recommendations. By automating and optimizing the species selection process, this system aims to improve the efficiency and sustainability of aquaculture operations. The novelty of this study lies in the use of additional water quality parameters not originally present in the dataset, such as TDS, dissolved oxygen, ammonia, and nitrate, generated through synthetic data. This, along with addressing class imbalance using SMOTE, improves the model's generalizability. By automating and optimizing the species selection process, this system aims to improve the efficiency and sustainabili","cbCaihgYiBcpRAqK","https://ap.wps.com/l/cbCaihgYiBcpRAqK","pdf",1203894,1,17,"English","en",105,"# Introduction\n## Problem of Traditional Aquaculture Monitoring\n## Proposed ML-Based Recommendation System\n# Literature Review\n## IoT-Based Water Quality Monitoring Systems\n## Intelligent Fish Species Selection Gaps","[{\"question\":\"What problem does the proposed system address in fish farming?\",\"answer\":\"It addresses the limitations of manual water quality monitoring and intuition-based species selection, which often lead to inefficient operations, inaccurate choices, and unpredictable results.\"},{\"question\":\"Which water quality parameters are used for fish species recommendation?\",\"answer\":\"The system uses seven parameters: pH, temperature, turbidity, TDS, dissolved oxygen, nitrate, and ammonia.\"},{\"question\":\"Which machine learning models are evaluated, and what accuracy is achieved?\",\"answer\":\"Random Forest, XGBoost, and SVM are evaluated, and the optimized model achieves over 90% accuracy.\"}]","Machine Learning-Based Fish Species Recommendation Using Water Quality Parameters | 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problem does the proposed system address in fish farming?","Question",{"text":75,"@type":76},"It addresses the limitations of manual water quality monitoring and intuition-based species selection, which often lead to inefficient operations, inaccurate choices, and unpredictable results.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which water quality parameters are used for fish species recommendation?",{"text":80,"@type":76},"The system uses seven parameters: pH, temperature, turbidity, TDS, dissolved oxygen, nitrate, and ammonia.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models are evaluated, and what accuracy is achieved?",{"text":84,"@type":76},"Random Forest, XGBoost, and SVM are evaluated, and the optimized model achieves over 90% 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