Yapay Zeka Metotlarını Kullanarak Kişisel Su Tüketimi Tahminleme

Year 2023, Volume: 6 Issue: 2, 1434 - 1451, 05.07.2023

Derya Bırant , İrem Çalmaz , İrem Okur

Abstract

Su tüketiminin tahmini, küresel sürdürülebilirlik hedeflerine ulaşmada ve vatandaşların uzun vadeli su ihtiyaçlarını karşılamada çok önemli bir görevdir. Bireysel su ayak izlerini tahmin etmek için bazı çalışmalar yapılmış olsa da, bu alanda hala sınırlı miktarda araştırma bulunmaktadır. Bu sınırı gidermeye yönelik olarak, bu makale, su ayak izi göstergesi aracılığıyla dolaylı ve doğrudan su kullanımını dikkate alarak bireylerin su tüketim puanlarını tahmin etmek için WaterAI adlı yeni bir yapay zeka tabanlı model önermektedir. Su tüketimi tahmini için en iyi modeli belirlemek adına dört farklı makine öğrenme algoritmasını (doğrusal regresyon, LASSO regresyonu, gradyan artırma ve aşırı gradyan artırma) karşılaştırmaktadır. Veriler bir anket çalışması ile toplanmıştır. Deneysel sonuçlar, önerilen modelin, kişisel su tüketim skorunu etkili bir şekilde tahmin etmek için başarılı bir şekilde kullanılabileceğini göstermektedir.

Keywords

Yapay zeka, Makine öğrenmesi, Su tüketimi, Su ayak izi, Regresyon

Estimating Personal Water Consumption Using Artificial Intelligence Methods

Abstract

The estimation of water consumption is a crucial task in achieving global sustainability targets and addressing the long-term water needs of citizens. While some efforts have been done to estimate individual water footprints, there is still limited research in this area. To address this limitation, this article proposes a new artificial intelligence-based model, called WaterAI, to predict individuals’ water consumption scores by taking into account indirect and direct water use through the water footprint indicator. It compares four different machine learning algorithms (linear regression, LASSO regression, gradient boosting, and extreme gradient boosting) to determine the best one for water consumption estimation. The data were collected with a questionnaire survey. The experimental results show that the proposed model can be successfully used to predict personal water consumption scores in an effective way.

Keywords

Artificial intelligence, Machine learning, Water consumption, Water footprint, Regression

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