Enhancement of chaotic hydrological time series prediction with real-time noise reduction using Extended Kalman Filter

Karunasingha, Dulakshi S. K.; Liong, Shie‐Yui

doi:10.1016/j.jhydrol.2018.08.044

Cited by 20 publications

(7 citation statements)

References 36 publications

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“…Other researchers have sought to enhance chaotic time series prediction rather than introducing new prediction techniques. Karunasingha et al [51] proposed the enhancement of prediction through the use of non-linear noise-reduction techniques to improve data quality. It was proposed by [4] that the reduction of noise in the chaotic time series improved the prediction accuracy.…”

Section: Future State Predictionmentioning

confidence: 99%

Characterization of normality of chaotic systems including prediction and detection of anomalies

Engler¹

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Accurate prediction and control pervades domains such as engineering, physics, chemistry, and biology. Often, it is discovered that the systems under consideration cannot be well represented by linear, periodic nor random data. It has been shown that these systems exhibit deterministic chaos behavior. Deterministic chaos describes systems which are governed by deterministic rules but whose data appear to be random or quasi-periodic distributions.Deterministically chaotic systems characteristically exhibit sensitive dependence upon initial conditions manifested through rapid divergence of states initially close to one another. Due to this characterization, it has been deemed impossible to accurately predict future states of these systems for longer time scales. Fortunately, the deterministic nature of these systems allows for accurate short term predictions, given the dynamics of the system are well understood. This fact has been exploited in the research community and has resulted in various algorithms for short term predictions.Detection of normality in deterministically chaotic systems is critical in understanding the system sufficiently to able to predict future states. Due to the sensitivity to initial conditions, the detection of normal operational states for a deterministically chaotic system can be challenging. The addition of small perturbations to the system, which may result in bifurcation of the normal states, further complicates the problem. The detection of anomalies and prediction of future states of the chaotic system allows for greater understanding of these systems.The goal of this research is to produce methodologies for determining states of normality for deterministically chaotic systems, detection of anomalous behavior, and the Copyright by JOSEPH JOHN ENGLER 2011 All Rights Reserved Graduate College

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Section: Future State Predictionmentioning

confidence: 99%

Characterization of normality of chaotic systems including prediction and detection of anomalies

Engler¹

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“…Noise can be propagated into the prediction model and make real problems in many of chaos real-life applications. Noise on chaotic time series prediction has been barely considered [19] to improve the performance prediction in the presence of noise. In this work, we focus on how to make a change in bias as disturbance data among the original data to examine the prediction of ANFIS based on chaotic noisy observations.…”

Section: Asymmetrical Double Strange Attractormentioning

confidence: 99%

Application of particle swarm optimization with ANFIS model for double scroll chaotic system

Wali

2021

IJECE

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The predictions for the original chaos patterns can be used to correct the distorted chaos pattern which has changed due to any changes whether from undesired disturbance or additional information which can hide under chaos pattern. This information can be recovered when the original chaos pattern is predicted. But unpredictability is most features of chaos, and time series prediction can be used based on the collection of past observations of a variable and analysis it to obtain the underlying relationships and then extrapolate future time series. The additional information often prunes away by several techniques. This paper shows how the chaotic time series prediction is difficult and distort even if Neuro-Fuzzy such as Adaptive Neural Fuzzy Inference System (ANFIS) is used under any disturbance. The paper combined particle swarm (PSO) and (ANFIS) to exam the prediction model and predict the original chaos patterns which comes from the double scroll circuit. Changes in the bias of the nonlinear resistor were used as a disturbance. The predicted chaotic data is compared with data from the chaotic circuit.

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“…In the reconstructed m-dimensional space, the regular trajectory of the motive system can be restored [8,10]. The key to reconstruct phase space is to determine the time delay τ and the embedding dimension m. In the paper, the embedding dimension m was determined by the Cao method [2], the time delay τ was determined by the autocorrelation function method [7,38].Further details about chaos theory can be found in Karunasingha et al [13] and Huang et al [12].…”

Section: Reconstructed Phase Spacementioning

confidence: 99%

“…Fortunately, chaos theory considers that there are a lot of information about related factors in the time series of mine water inflow, and the number and value of related factors can be obtained through the reconstructed phase space [19,29]. Chaos theory is an effective tool for studying complex systems [13], which can restore the rich dynamic information hidden in a single variable of the system [27].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2021

SN Appl. Sci.

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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.

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Enhancement of chaotic hydrological time series prediction with real-time noise reduction using Extended Kalman Filter

Cited by 20 publications

References 36 publications

Characterization of normality of chaotic systems including prediction and detection of anomalies

Characterization of normality of chaotic systems including prediction and detection of anomalies

Application of particle swarm optimization with ANFIS model for double scroll chaotic system

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

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