Tuning ANN Hyperparameters for Forecasting Drinking Water Demand
| Autor: | Ariele Zanfei, Andrea Menapace, Maurizio Righetti |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Boosting (machine learning) Computer science QH301-705.5 QC1-999 0208 environmental biotechnology Big data 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre tuning analysis 01 natural sciences water smart grids short-term forecasting General Materials Science Biology (General) Instrumentation QD1-999 0105 earth and related environmental sciences Fluid Flow and Transfer Processes Hyperparameter Artificial neural network business.industry Process Chemistry and Technology Physics General Engineering Prediction interval Engineering (General). Civil engineering (General) 020801 environmental engineering Computer Science Applications Chemistry Recurrent neural network machine learning Hyperparameter optimization grid search hyperparameters Feedforward neural network water supply systems Artificial intelligence TA1-2040 business computer artificial neural networks drinking water consumption |
| Zdroj: | Applied Sciences, Vol 11, Iss 4290, p 4290 (2021) Applied Sciences Volume 11 Issue 9 |
| ISSN: | 2076-3417 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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