Chaos-generalized regression neural network prediction model of mine water inflow

Abstract Artificial neural network (ANN) provides a new way for mine water inflow prediction. However, the effectiveness of prediction using ANN model would not be guaranteed if the influencing factors of water inflow are difficult to quantify or there are only a few observation data. Chaos theory c...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jianlin Li, Luyang Wang, Xinyi Wang, Peiqiang Gao
Formato:	article
Lenguaje:	EN
Publicado:	Springer 2021
Materias:	Chaos theory Phase space reconstruction Generalized regression neural network Input layer neuron Long-term prediction of mine water Science Q Technology T
Acceso en línea:	https://doaj.org/article/159fec4a91714459b984b558553fd412
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:159fec4a91714459b984b558553fd412
record_format	dspace
spelling	oai:doaj.org-article:159fec4a91714459b984b558553fd4122021-11-14T12:15:08ZChaos-generalized regression neural network prediction model of mine water inflow10.1007/s42452-021-04846-42523-39632523-3971https://doaj.org/article/159fec4a91714459b984b558553fd4122021-11-01T00:00:00Zhttps://doi.org/10.1007/s42452-021-04846-4https://doaj.org/toc/2523-3963https://doaj.org/toc/2523-3971Abstract Artificial neural network (ANN) provides a new way for mine water inflow prediction. However, the effectiveness of prediction using ANN model would not be guaranteed if the influencing factors of water inflow are difficult to quantify or there are only a few observation data. Chaos theory can recover the rich dynamic information hidden in time series. By reconstructing water inflow time series in phase space, the multi-dimensional matrix could be obtained, with each column representing an influencing factor of water inflow and its value representing the change of the influencing factor with time. Therefore, a new prediction model of mine water inflow can be established by combining ANN with chaos theory when lacking data on the influencing factors of water inflow. In the present study, the No. 12 coal mine of Pingdingshan China was selected as the study site. The Chaos-GRNN model and Chaos- BPNN model of mine, water inflow were established by using the water inflow data from February 1976 to December 2013. The model was verified by using the water inflow values in the 24 months from 2014 to 2015. The number embedded dimension (M) of influencing factors of water inflow determined by phase space reconstruction was 7, meaning that there were 7 influencing factors of water inflow and 7 neurons in GRNN input layer, and the time delay was 13 months. The value of GRNN input layer neurons was determined accordingly. The maximum Lyapunov index was 0.0530, and the prediction time of GRNN was 19 months. The two models were evaluated by using four evaluation indices (R, RMSE, MAPE, NSE) and violin plot. It was found that both models can realize the long-term prediction of water inflow, and the prediction effectiveness of Chaos-GRNN model is better than that of Chaos-BPNN model.Jianlin LiLuyang WangXinyi WangPeiqiang GaoSpringerarticleChaos theoryPhase space reconstructionGeneralized regression neural networkInput layer neuronLong-term prediction of mine waterScienceQTechnologyTENSN Applied Sciences, Vol 3, Iss 12, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chaos theory Phase space reconstruction Generalized regression neural network Input layer neuron Long-term prediction of mine water Science Q Technology T
spellingShingle	Chaos theory Phase space reconstruction Generalized regression neural network Input layer neuron Long-term prediction of mine water Science Q Technology T Jianlin Li Luyang Wang Xinyi Wang Peiqiang Gao Chaos-generalized regression neural network prediction model of mine water inflow
description	Abstract Artificial neural network (ANN) provides a new way for mine water inflow prediction. However, the effectiveness of prediction using ANN model would not be guaranteed if the influencing factors of water inflow are difficult to quantify or there are only a few observation data. Chaos theory can recover the rich dynamic information hidden in time series. By reconstructing water inflow time series in phase space, the multi-dimensional matrix could be obtained, with each column representing an influencing factor of water inflow and its value representing the change of the influencing factor with time. Therefore, a new prediction model of mine water inflow can be established by combining ANN with chaos theory when lacking data on the influencing factors of water inflow. In the present study, the No. 12 coal mine of Pingdingshan China was selected as the study site. The Chaos-GRNN model and Chaos- BPNN model of mine, water inflow were established by using the water inflow data from February 1976 to December 2013. The model was verified by using the water inflow values in the 24 months from 2014 to 2015. The number embedded dimension (M) of influencing factors of water inflow determined by phase space reconstruction was 7, meaning that there were 7 influencing factors of water inflow and 7 neurons in GRNN input layer, and the time delay was 13 months. The value of GRNN input layer neurons was determined accordingly. The maximum Lyapunov index was 0.0530, and the prediction time of GRNN was 19 months. The two models were evaluated by using four evaluation indices (R, RMSE, MAPE, NSE) and violin plot. It was found that both models can realize the long-term prediction of water inflow, and the prediction effectiveness of Chaos-GRNN model is better than that of Chaos-BPNN model.
format	article
author	Jianlin Li Luyang Wang Xinyi Wang Peiqiang Gao
author_facet	Jianlin Li Luyang Wang Xinyi Wang Peiqiang Gao
author_sort	Jianlin Li
title	Chaos-generalized regression neural network prediction model of mine water inflow
title_short	Chaos-generalized regression neural network prediction model of mine water inflow
title_full	Chaos-generalized regression neural network prediction model of mine water inflow
title_fullStr	Chaos-generalized regression neural network prediction model of mine water inflow
title_full_unstemmed	Chaos-generalized regression neural network prediction model of mine water inflow
title_sort	chaos-generalized regression neural network prediction model of mine water inflow
publisher	Springer
publishDate	2021
url	https://doaj.org/article/159fec4a91714459b984b558553fd412
work_keys_str_mv	AT jianlinli chaosgeneralizedregressionneuralnetworkpredictionmodelofminewaterinflow AT luyangwang chaosgeneralizedregressionneuralnetworkpredictionmodelofminewaterinflow AT xinyiwang chaosgeneralizedregressionneuralnetworkpredictionmodelofminewaterinflow AT peiqianggao chaosgeneralizedregressionneuralnetworkpredictionmodelofminewaterinflow
_version_	1718429331179438080

Chaos-generalized regression neural network prediction model of mine water inflow

Ejemplares similares