Investigating chaos in river stage and discharge time series

Khatibi, Rahman; Sivakumar, Bellie; Ghorbani, Mohammad Ali; Kisi, Özgür; Koçak, Kasım; Zadeh, Davod Farsadi

doi:10.1016/j.jhydrol.2011.10.026

Cited by 96 publications

(48 citation statements)

References 36 publications

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“…It examines, in dimension m, the nearest neighbor ) appended to the j th vector and to its nearest neighbor with embedding [15]. In this study, the false nearest neighbor method was implemented using the TISEAN package [26] and uses the second method above.…”

Section: False Nearest Neighbor Algorithmmentioning

confidence: 99%

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Implementation of Chaotic Analysis on River Discharge Time Series

Albostan¹,

Önöz²

2015

EPE

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The gauged river data play an important role in modeling, planning and management of the river basins. Among the hydrological data, the daily discharge data seem to be more significant for determining the amount of energy production and the control the risks of floods and drought. Hence, the data need correct measurement, analysis, and reliable estimates. The purpose of the paper is to investigate the question whether all the stations in a river basin exhibit chaotic behavior. For this purpose, the daily discharge data of four gauge stations are examined by using three nonlinear data analysis methods: 1) phase space reconstruction; 2) correlation dimension; and 3) local approximation where all those methods provide identification of chaotic behaviors. The results show that all stations exhibit chaotic character. Taking into account the proven chaotic characteristic of the stations, local approximation method is applied to observe the prediction accuracy. Considering the fact that global warming is a serious threat on natural resources, the prediction accuracy is becoming a key factor to ensure sustainability. Hence, this study is a good example on the implementation of chaotic analysis by means of the obtained results from the methods.

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Section: False Nearest Neighbor Algorithmmentioning

confidence: 99%

“…Even the primary objective of those studies were on investigating the existence of chaos in hydrological processes, other aspects such as prediction e.g. [6] [8] [9] [14] [15], noise level determination and noise reduction e.g. [8] [16] were also given due consideration.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Chaotic Analysis on River Discharge Time Series

Albostan¹,

Önöz²

2015

EPE

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“…River flow simulation is significant for planning and management of catchment area, evaluation of risk and control of droughts, floods, development of water resources, production of hydroelectric energy, navigation planning and allocation of water for agriculture (Khatibi et al 2012).…”

Section: Introductionmentioning

confidence: 99%

River flow simulation using a multilayer perceptron-firefly algorithm model

Darbandi

Pourhosseini

2018

Appl Water Sci

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River flow estimation using records of past time series is importance in water resources engineering and management and is required in hydrologic studies. In the past two decades, the approaches based on the artificial neural networks (ANN) were developed. River flow modeling is a non-linear process and highly affected by the inputs to the modeling. In this study, the best input combination of the models was identified using the Gamma test then MLP-ANN and hybrid multilayer perceptron (MLP-FFA) is used to forecast monthly river flow for a set of time intervals using observed data. The measurements from three gauge at Ajichay watershed, East Azerbaijani, were used to train and test the models approach for the period from January 2004 to July 2016. Calibration and validation were performed within the same period for MLP-ANN and MLP-FFA models after the preparation of the required data. Statistics, the root mean square error and determination coefficient, are used to verify outputs from MLP-ANN to MLP-FFA models. The results show that MLP-FFA model is satisfactory for monthly river flow simulation in study area.

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“…Some methods like autocorrelation function (ACF) or correlation integral (CI) methods could bring an educated guess about which lag time should be used in the development of such models [35][36][37]. However, we have used average mutual information (AMI), which does not require large data sets, unlike the ACF and CI methods; it has also been widely used in the field of hydro-informatics in recent years [38]. The following equation is the method for computing AMI for each one of the data sets designed as input variables of the GEP models.…”

Section: Average Mutual Informationmentioning

confidence: 99%

Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

Shabani

Candelieri

Archetti

et al. 2018

Water

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This article proposes a new general approach in short-term water demand forecasting based on a two-stage learning process that couples time-series clustering with gene expression programming (GEP). The approach was tested on the real life water demand data of the city of Milan, in Italy. Moreover, multi-scale modeling using a series of head-time was deployed to investigate the optimum temporal resolution under study. Multi-scale modeling was performed based on rearranging hourly based patterns of water demand into 3, 6, 12, and 24 h lead times. Results showed that GEP should receive more attention among the emerging nonlinear modelling techniques if coupled with unsupervised learning algorithms in detailed spherical k-means.

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Investigating chaos in river stage and discharge time series

Cited by 96 publications

References 36 publications

Implementation of Chaotic Analysis on River Discharge Time Series

Implementation of Chaotic Analysis on River Discharge Time Series

River flow simulation using a multilayer perceptron-firefly algorithm model

Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

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