WATER QUALITY AND POTABİLİTY PREDICTION WITH MACHINE LEARNING ALGORITHMS

Year 2023, Volume: 6 Issue: 2, 65 - 80, 30.12.2023

Tülay Turan

Abstract

Drinking water is one of the basic needs of people that is vital for their survival. It is important to understand the quality and potability of this requirement, which directly affects human health. Water quality can be estimated through conventional laboratory and statistical analysis. However, this solution is generally expensive and time consuming. Water availability can be analyzed quickly and efficiently with machine learning methods, which have developed rapidly in recent years and benefit many areas of our lives. In this context, models were developed with 15 different machine learning algorithms to predict water quality and drinkability and their results were compared. In model evaluations, it was seen that LGBMClassifier and SVC algorithms provided the best prediction performance. Hyperparameter optimization was performed using the GridSearchCv object for these two models that showed the best prediction performance. After the optimization process, the LGBMClassifier model achieved the most successful prediction result with an accuracy value of 0.92%. The study will guide future studies with its analysis and visualization of the factors affecting water quality and drinkability and its high prediction performance.

Keywords

Artificial Intelligence, LGBMClassifier, Machine Learning, Water Quality and Potability

MAKİNE ÖĞRENMESİ ALGORİTMALARI İLE SU KALİTESİ VE İÇİLEBİLİRLİK TAHMİNİ

Abstract

İçme suyu insanların yaşamlarını sürdürebilmeleri için hayati önem taşıyan temel ihtiyaçlarının başında gelmektedir. İnsan sağlığını doğrudan etkileyen bu ihtiyacın kalitesini ve içilebilirliğini anlamak önemlidir. Su kalitesi geleneksel laboratuvar ve istatistiksel analizler yoluyla tahmin edilebilir. Ancak bu çözüm genel olarak pahalı ve zaman alıcıdır. Son yıllarda hızla gelişen, hayatımızın bir çok alanına fayda sağlayan makine öğrenmesi yöntemleri ile su kullanılabilirliği hızlı ve verimli bir şekilde analiz edilebilir. Bu bağlamda gerçekleştirilen çalışmada, su kalitesinin ve içilebilirliğinin tahmini için 15 farklı makine öğrenmesi algoritması ile modeller geliştirilmiş ve elde ettikleri sonuçlar karşılaştırılmıştır. Model değerlendirmelerinde en iyi tahmin performansını LGBMClassifier ve SVC algoritmalarının sağladığı görülmüştür. En iyi tahmin performansını gösteren bu iki model için GridSearchCv nesnesi kullanılarak hiper parametre optimizasyonu gerçekleştirilmiştir. Optimizasyon işleminden sonra LGBMClassifier modeli %0,92 accuracy değeri ile en başarılı tahmin sonucunu elde etmiştir. Çalışma su kalitesi ve içilebilirliğini etkileyen faktörleri analiz etmesi, görselleştirmesi ve yüksek tahmin performansı ile gelecek çalışmalara yön verecektir.

Keywords

LGBMClassifier, Makine Öğrenmesi, Su Kalitesi ve İçilebilirliği, Yapay Zeka

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