The evolution of smart water grids leads to new Big Data challenges boosting the development and application of Machine Learning techniques to support efficient and sustainable drinking water management. These powerful techniques rely on hyperparameters making the models’ tuning a tricky and crucial task. We hence propose an insightful analysis of the tuning of Artificial Neural Networks for drinking water demand forecasting. This study focuses on layers and nodes’ hyperparameters fitting of different Neural Network architectures through a grid search method by varying dataset, prediction horizon and set of inputs. In particular, the architectures involved are the Feed Forward Neural Network, the Long Short Term Memory, the Simple Recurrent Neural Network and the Gated Recurrent Unit, while the prediction interval ranges from 1 h to 1 week. To avoid the problem of the Neural Networks tuning stochasticity, we propose the selection of the median model among several repetitions for each hyperparameter’s configurations. The proposed iterative tuning procedure highlights the change of the required number of layers and nodes depending on Neural Network architectures, prediction horizon and dataset. Significant trends and considerations are pointed out to support Neural Network application in drinking water prediction.

中文翻译：

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调整ANN超参数以预测饮用水需求

智能水网格的发展带来了新的大数据挑战，推动了机器学习技术的发展和应用，以支持高效和可持续的饮用水管理。这些强大的技术依赖于超参数，使模型的调整成为一项棘手且至关重要的任务。因此，我们提出了针对饮用水需求预测的人工神经网络调整的有见地的分析。通过改变数据集，预测范围和输入集，通过网格搜索方法，本研究着重于不同神经网络体系结构的层和节点的超参数拟合。特别地，所涉及的体系结构是前馈神经网络，长期短期记忆，简单递归神经网络和门控递归单元，而预测间隔为1小时至1周。为了避免神经网络调整随机性的问题，我们建议为每个超参数的配置在几个重复中选择中值模型。拟议的迭代调整程序根据神经网络体系结构，预测范围和数据集突出显示所需的层数和节点数的变化。指出了重要的趋势和考虑因素，以支持神经网络在饮用水预测中的应用。