A new quality control procedure based on non-linear autoregressive neural network for validating raw river stage data

Lineros, Miriam López; Estévez, Javier; Giráldez, Juan Vicente; Madueño, A.

doi:10.1016/j.jhydrol.2013.12.026

Cited by 15 publications

(10 citation statements)

References 25 publications

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“…The use of perceptrons is a reasonable way to reduce the risk of incorporation spurious data [3]. A conventional multilayer perceptron (MLP) [53] has three layers: an input layer, one or more hidden layers and an output layer.…”

Section: Multilayer Perceptronsmentioning

confidence: 99%

“…A non-linear autoregressive neural network with external input (NARNN) [58] has been used with a seed that increases gradually during the validation process [3], whose performance has been contrasted by comparing it with the standard methods applied in validating the data of a float sensor to which errors of known magnitude have been added.…”

Section: Using a Narnn With Dynamic Seedmentioning

confidence: 99%

“…An adequate water resources management process is crucial to minimize the impact of extreme events [2]. One of the main problems in the analysis of time series is the absence of data, with gaps of different widths, number of missing data and frequency, which makes the model identification harder and prevents the adoption of common validation procedures, usually applied to complete data sets [3][4][5][6]. These deficiencies in hydrologic time series are usually due to the malfunctioning of monitoring equipment, the occurrence of anomalous natural phenomena and data transmission storage and retrieval process issues [7].…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Best Gap-Filling Technique for River Stage Data Suitable for Low Capacity Processors and Real-Time Application Using IoT

Luna

Lineros

Estévez

et al. 2020

Sensors

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Hydrometeorological data sets are usually incomplete due to different reasons (malfunctioning sensors, collected data storage problems, etc.). Missing data do not only affect the resulting decision-making process, but also the choice of a particular analysis method. Given the increase of extreme events due to climate change, it is necessary to improve the management of water resources. Due to the solution of this problem requires the development of accurate estimations and its application in real time, this work present two contributions. Firstly, different gap-filling techniques have been evaluated in order to select the most adequate one for river stage series: (i) cubic splines (CS), (ii) radial basis function (RBF) and (iii) multilayer perceptron (MLP) suitable for small processors like Arduino or Raspberry Pi. The results obtained confirmed that splines and monolayer perceptrons had the best performances. Secondly, a pre-validating Internet of Things (IoT) device was developed using a dynamic seed non-linear autoregressive neural network (NARNN). This automatic pre-validation in real time was tested satisfactorily, sending the data to the catchment basin process center (CPC) by using remote communication based on 4G technology.

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Section: Multilayer Perceptronsmentioning

confidence: 99%

Section: Using a Narnn With Dynamic Seedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing the Best Gap-Filling Technique for River Stage Data Suitable for Low Capacity Processors and Real-Time Application Using IoT

Luna

Lineros

Estévez

et al. 2020

Sensors

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show abstract

“…In time series prediction with neural networks, the main problems have usually been the selection of the length of the input vectors and the actual structure of the network [23]. These problems are similar in all neural architectures.…”

Section: Nar Neural Networkmentioning

confidence: 99%

A New Error Prediction Method for Machining Process Based on a Combined Model

Zhou

Zhu

Wang

et al. 2018

Mathematical Problems in Engineering

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Machining process is characterized by randomness, nonlinearity, and uncertainty, leading to the dynamic changes of machine tool machining errors. In this paper, a novel model combining the data processing merits of metabolic grey model (MGM) with that of nonlinear autoregressive (NAR) neural network is proposed for machining error prediction. The advantages and disadvantages of MGM and NAR neural network are introduced in detail, respectively. The combined model first utilizes MGM to predict the original error data and then uses NAR neural network to forecast the residual series of MGM. An experiment on the spindle machining is carried out, and a series of experimental data is used to validate the prediction performance of the combined model. The comparison of the experiment results indicates that combined model performs better than the individual model. The two-stage prediction of the combined model is characterized by high accuracy, fast speed, and robustness and can be applied to other complex machining error predictions.

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“…Since most of the meteorological measurement processes are nonlinear, the adequacy of these linear methods would be affected. A nonlinear autoregressive neural network is presented for consistency analysis of meteorological data and the approach has learning capacity of nonlinear dependencies from a large volume of potentially noisy data [10]. Nevertheless, because of the black-box of the neural network, the understandable heuristic knowledge could not be provided [11].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Partial Least Squares for Consistency Analysis of Meteorological Data

Meng¹,

Zhang²,

Yang³

et al. 2015

Mathematical Problems in Engineering

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Considering the different types of error and the nonlinearity of the meteorological measurement, this paper proposes a nonlinear partial least squares method for consistency analysis of meteorological data. For a meteorological element from one automated weather station, the proposed method builds the prediction model based on the corresponding meteorological elements of other surrounding automated weather stations to determine the abnormality of the measured values. For the proposed method, the latent variables of the independent variables and the dependent variables are extracted by the partial least squares (PLS), and then they are, respectively, used as the inputs and outputs of neural network to build the nonlinear internal model of PLS. The proposed method can deal with the limitation of traditional nonlinear PLS whose inner model is the fixed quadratic function or the spline function. Two typical neural networks are used in the proposed method, and they are the back propagation neural network and the adaptive neuro-fuzzy inference system (ANFIS). Moreover, the experiments are performed on the real data from the atmospheric observation equipment operation monitoring system of Shaanxi Province of China. The experimental results verify that the nonlinear PLS with the internal model of ANFIS has higher effectiveness and could realize the consistency analysis of meteorological data correctly.

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A new quality control procedure based on non-linear autoregressive neural network for validating raw river stage data

Cited by 15 publications

References 25 publications

Assessing the Best Gap-Filling Technique for River Stage Data Suitable for Low Capacity Processors and Real-Time Application Using IoT

Assessing the Best Gap-Filling Technique for River Stage Data Suitable for Low Capacity Processors and Real-Time Application Using IoT

A New Error Prediction Method for Machining Process Based on a Combined Model

Nonlinear Partial Least Squares for Consistency Analysis of Meteorological Data

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