A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Backpropagation to predict drinking water consumption [ Backpropagation para predecir el consumo de agua potable]
The present research article is based on the use of artificial neural networks as a tool for the prediction applied to the consumption of drinking water, where the artificial learning of a multilayer backpropagation network with the historical data consumed in m3 is used. In a computer with a regular feature, the backpropagation network was implemented in the Python 3.7.0 language, taking as a study object a case published on its SUNEDU website regarding drinking water consumption in 2017 and checking the results in January of 2018, taking these data for learning and the respective tests. It has been achieved as a result to predict the amount of water consumption of the institution. The test performed resulted in an excess of 23 m3, which represents 2.7% in excess given that in January 2018 a consumption of 833m3 was recorded. Bear in mind that it was done for the training process with a maximum error for the training of 0.000099 and with a maximum number of iterations of 100000. After training with the historical data of drinking water consumption, it was possible to predict the consumption of SUNEDU to January 2018.
Backpropagation to predict drinking water consumption [ Backpropagation para predecir el consumo de agua potable]
The present research article is based on the use of artificial neural networks as a tool for the prediction applied to the consumption of drinking water, where the artificial learning of a multilayer backpropagation network with the historical data consumed in m3 is used. In a computer with a regular feature, the backpropagation network was implemented in the Python 3.7.0 language, taking as a study object a case published on its SUNEDU website regarding drinking water consumption in 2017 and checking the results in January of 2018, taking these data for learning and the respective tests. It has been achieved as a result to predict the amount of water consumption of the institution. The test performed resulted in an excess of 23 m3, which represents 2.7% in excess given that in January 2018 a consumption of 833m3 was recorded. Bear in mind that it was done for the training process with a maximum error for the training of 0.000099 and with a maximum number of iterations of 100000. After training with the historical data of drinking water consumption, it was possible to predict the consumption of SUNEDU to January 2018.
Backpropagation to predict drinking water consumption [ Backpropagation para predecir el consumo de agua potable]
Huarote Zegarra, Raul (author) / Romero Valencia, Monica (author)
2018-12-30
doi:10.32829/sej.v2i2.64
Journal of Sciences and Engineering; Vol 2, No 2 (2018): July - December; 31-39 ; 2523-9503 ; 10.32829/sej.v2i2
Article (Journal)
Electronic Resource
English
DDC:
690
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