预测伊斯坦布尔淡水消耗的各种机器学习方法的评估

M. Hekimoğlu, Ayşe İrem Çeti̇n, B. Kaya
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引用次数: 0

摘要

规划、组织和管理水资源对城市地区和大都市至关重要。伊斯坦布尔是最大的特大城市之一,人口超过1500万。大量的水需求和日益稀缺的清洁水资源使得这个城市必须进行长期规划,因为持续的供水需要大规模的投资项目。成功的投资计划需要对淡水需求进行准确的预测和预测。本研究考虑了不同的机器学习方法来预测伊斯坦布尔的淡水需求。利用2009年以来市政府提供的月度消费数据,我们比较了ARIMA、Holt-Winters、人工神经网络、递归神经网络、长短期记忆和简单递归神经网络模型的预测精度。研究发现,利用多层感知机和季节ARIMA对伊斯坦布尔的月淡水需求预测效果最好。从预测建模的角度来看,这一结果再次表明了传统预测模型和新型机器学习技术的结合使用,以实现最高的预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of Various Machine Learning Methods to Predict Istanbul’s Freshwater Consumption
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.
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