A LSTM-based Generative Adversarial Network for End-use Water Modelling

Abstract

Research pertaining to end-use water analysis plays a pivotal role in enabling local communities to enhance their management of pipelines, water resources, and associated policies. Nowadays, various end-use models have been developed based on diverse databases and measurements. Nonetheless, a predominant drawback prevalent in most of these models is their limited spatial scope and sluggish computational speed. This thesis endeavors to address these challenges through the proposition of a generative adversarial network (GAN) based stochastic end-use demand model. The SIMDEUM model, a stochastic end-use model, was first published in 2010. Since its inception, it has garnered substantial recognition and validation from numerous researchers. Within this thesis, the GAN model utilizes SIMDEUM as the training set and undergoes validation utilizing a comprehensive measurement dataset, encompassing over 1000 households from the Netherlands and the United States. Remarkably, the GAN model attains an error rate of 12% for end uses, coupled with an R2 value exceeding 0.8 for the overall model. In contrast to SIMDEUM, the GAN model significantly enhances computational speed by more than 500%. Furthermore, the GAN model can be tailored to specific requirements and seamlessly processes raw data.It is concluded that the GAN-based stochastic water use model presented in this thesis adeptly simulates end-use water demand.