Su Tüketimi Tahmininde Yapay Zekâ: Sistematik İnceleme

Öz

Su tüketiminin tahmin edilmesi, su kaynaklarının sürdürülebilir bir şekilde yönetilmesi ve dünya genelindeki su sorunlarının çözülmesi açısından hayati öneme sahiptir. Su, tüm canlıların yaşamı için vazgeçilmez olduğundan, uzun süredir birçok çalışmanın odağında yer almıştır. Bu çalışmalarda yapay zekâ, makine öğrenmesi ve geleneksel istatistiksel yöntemler kullanılmıştır. Bu makale, su tüketimi tahmininde yapay zekâ uygulamalarına dair mevcut araştırmaları kapsamlı bir şekilde incelemektedir. Çalışma, 2019 ile 2024 yılları arasında yayımlanmış ve SpringerLink, IEEE Xplore ve Scopus gibi akademik veri tabanlarından elde edilmiş makaleler üzerinden yürütülmüştür. İncelenen literatür, su tüketimini tahmin etmek için kullanılan algoritmalara göre sınıflandırılmıştır. Ayrıca, su tüketimi tahmin çalışmalarında kullanılan yapay zekâ yöntemlerinin avantajları, dezavantajları ve karşılaşılan zorluklar da değerlendirilmiştir. Elde edilen sonuçlar, Uzun Kısa Süreli Bellek (LSTM) modellerinin performansının diğer yöntemlere kıyasla daha iyi olduğunu ortaya koymaktadır. Bununla birlikte, veri kalitesi ve erişilebilirliği sınırlayıcı faktörler arasında yer almaktadır. Bu çalışma, yapay zekâ tabanlı yöntemlerle su tüketimi tahminine yönelik son gelişmeleri incelemekte ve bu alandaki gelecekteki araştırmalar için potansiyel yönleri vurgulamaktadır.

Anahtar Kelimeler

• Makine öğrenmesi
• su tüketimi tahmini
• su talebi yönetimi
• yapay zeka

Artificial Intelligence in Water Consumption Forecasting: A Systematic Review

Öz

Predicting water consumption is crucial for the sustainable management of water resources and for solving the world's water problems. Water is the subject of numerous studies as it is essential for the survival of all living beings. Artificial intelligence, machine learning and conventional statistical methods have been used in these studies. This article provides a comprehensive overview of the research about AI applications for water consumption predictions. The study was conducted using articles published between 2019 and 2024, retrieved from academic databases such as SpringerLink, IEEE Xplore and Scopus. The analyzed literature was categorized based on water studies in relation to the algorithms used to predict water consumption. The study also investigated the advantages, disadvantages and difficulties of artificial intelligence methods used in water consumption estimation studies. The results show that the performance of Long Short-Term Memory models is better than other methods. Nevertheless, data quality and availability are limiting factors. This study examines recent advances in predicting water consumption using AI-based methods and identifies potential areas for further research in this field.

Anahtar Kelimeler

• Artificial intelligence
• machine learning
• water demand management
• water forecasting

Kaynakça

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