ijegeo

International Journal of Environment and Geoinformatics

2148-9173

Cem GAZİOĞLU

10.30897/ijegeo.1270228

Environmental Sciences

Çevre Bilimleri

Evaluation of Various Machine Learning Methods to Predict Istanbul’s Freshwater Consumption

https://orcid.org/0000-0001-9446-0582

Hekimoğlu

Mustafa

KADIR HAS UNIVERSITY, FACULTY OF ENGINEERING AND NATURAL SCIENCES, DEPARTMENT OF INDUSTRIAL ENGINEERING

https://orcid.org/0000-0002-9298-0565

Çetin

Ayşe İrem

KADIR HAS UNIVERSITY, FACULTY OF ENGINEERING AND NATURAL SCIENCES, DEPARTMENT OF INDUSTRIAL ENGINEERING

https://orcid.org/0000-0002-9110-0765

Kaya

Burak Erkan

KADIR HAS UNIVERSITY, FACULTY OF ENGINEERING AND NATURAL SCIENCES, DEPARTMENT OF INDUSTRIAL ENGINEERING

06 15 2023

10 2 1 11 03 24 2023 03 27 2023

2014

International Journal of Environment and Geoinformatics

Planning, organizing, and managing water resources is crucial for urban areas and metropolitans. Istanbul is one of the largest megacities, with a population of over 15 million. The large volume of water demand and increasing scarcity of clean water resources make long-term planning necessary for this city, as sustained water supply requires large-scale investment projects. Successful investment plans require accurate projections and forecasting for freshwater demand. This study considers different machine learning methods for freshwater demand forecasting for Istanbul. Using monthly consumption data provided by the municipality since 2009, we compare forecasting accuracies of ARIMA, Holt-Winters, Artificial Neural Networks, Recursive Neural Networks, Long-Short Term Memory, and Simple Recurrent Neural Network models. We find that the monthly freshwater demand of Istanbul is best predicted by Multi-Layer Perceptron and Seasonal ARIMA. From the predictive modeling perspective, this result is another indication of the combined usage of conventional forecasting models and novel machine learning techniques to achieve the highest forecasting accuracy.

Water Management Machine Learning Neural Networks Autoregressive Models

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