Detrending Algorithm Research Articles

A detection method of Auricularia auricula mycelium storage period based on near-infrared spectral characteristics and deep learning model

The storage time of Auricularia auricula mycelium was an important factor influencing its growth activity. The rapid detection of storage time had important theoretical significance and practical value for evaluating, cultivating, and scientifically managing Auricularia auricula germplasm resources. Due to the problem of mapping the relationship between the complex near-infrared (NIR) spectral characteristics of Auricularia auricula mycelium and its storage period, it was difficult to characterize using mathematical models accurately. In this paper, a method for detecting the storage period of Auricularia auricula mycelium based on NIR spectral characteristics and deep learning was proposed. First, five different storage periods of Auricularia auricula mycelium were taken as the research object, and the de-trending (DT) algorithm was used to pre-process the spectral data. Then, the Competitive Adaptive Reweighted Sampling (CARS) algorithm was applied to extract the NIR spectral characteristics of the mycelium. The reduction rate of the spectral variables reached 94.64 %. Further, a one-dimensional convolutional neural network (T-CNN) was constructed, and the SGD-Adam algorithm was used to calculate the optimal network parameters. Finally, a method for detecting the storage period of Auricularia auricula mycelium was implemented, achieving a simulation accuracy of 98.33 %. The experimental results revealed that this method outperformed traditional machine learning models such as partial least squares-discriminant analysis (PLS-DA), back propagation (BP), support vector machine (SVM), and k-nearest neighbors (KNN) by an average of 19.56 % in detection accuracy. Additionally, the detection speed was only 0.07 s. This achievement enabled rapid and accurate detection of the storage time of Auricularia auricula mycelium, providing a dependable foundation for identifying the growth activity of Auricularia auricula mycelium. It provided technical support for the scientific management of the edible fungus mycelium, including quality monitoring, evaluation, and screening.

High-precision long distance measurement based on broad spectrum interferometry using a femtosecond laser

Abstract The method based on broad spectrum interference using femtosecond pulses is an important ranging method. In this paper, we experimentally evaluate the effect of interferometric spectral bandwidth used for calculation on the ranging results, and precision can be improved by choosing the optimal spectral bandwidth. In addition, in order to eliminate the influence of interferometric spectral fluctuations on the measurement results, we introduce the detrend algorithm into the processing algorithm and compare the measurement results before and after the algorithm improvement. Then, four kinds of nominal distance were measured and the contribution of the relevant factors to the measurement uncertainty is evaluated. Finally, the synthetic standard uncertainty of measurement results was 0.02, 0.10, 1.05 and 2.78 μm for nominal distance of ∼0, ∼1.5 ∼16.5 and ∼43.5 m respectively.

Early multi-parameter detection study of lithium-ion batteries based on the ratio of blue to infrared light and gas concentration

As the use of lithium-ion batteries in energy storage and transportation grows, the number of fires and explosions caused by battery thermal runaway increases, emphasizing the vital need for enhanced early detection technology. Existing research does not clarify the effect of airflow environment and detection location on battery smoke flow and detection effectiveness. The purpose of this study is to illuminate the environmental effects on battery smoke diffusion, allowing for the selection of more appropriate detection parameters while also improving detection sensitivity and robustness. Here, the investigation focuses on the smoke characteristics of 18650 LiFePO4 (LFP) batteries, using the ratio of blue to infrared light and concentrations of CO and H2. Additionally, a detrending algorithm was developed to interpret smoke signals, and the smoke characteristics of batteries in different states of diffusion after thermal runaway occurred at different states of charge (SOC) were further investigated. It is shown that the smoke signal values processed by the detrending algorithm are more suitable for smoke detection in thermal runaway batteries. Using smoke and fugitive gas growth rate parameters, the results show that airflow interference is important in promoting smoke diffusion. Notably, the ambient state and the SOC of the battery have a greater influence on the maximum concentration of CO compared to H2. The linear equations and the addition of the environmental influence parameter DR imply that H2 is a better choice as the primary gas parameter for identifying battery runaway. The airflow disruption reduced the smoke response time by around 40 % while having minimal influence on gas diffusion. This study establishes a theoretical foundation for optimizing battery operating environments and gives practical guidelines for the use of multi-parameter monitoring and early detection of lithium-ion batteries. Furthermore, this study improves the safe management and protection of lithium batteries in their application.

TransitFit: combined multi-instrument exoplanet transit fitting for JWST, HST, and ground-based transmission spectroscopy studies

ABSTRACT We present transitfit1, a package designed to fit exoplanetary transit light curves. transitfit offers multi-epoch, multi-wavelength fitting of multi-telescope transit data. transitfit allows per-telescope detrending to be performed simultaneously with transit parameter fitting, including custom detrending. Host limb darkening can be fitted using prior conditioning from stellar atmosphere models. We demonstrate transitfit in a number of contexts. We model multi-telescope broad-band optical data from the ground-based SPEARNET survey of the low-density hot-Neptune WASP-127b and compare results to a previously published higher spectral resolution GTC/OSIRIS transmission spectrum. Using transitfit, we fit 26 transit epochs by TESS to recover improved ephemeris of the hot-Jupiter WASP-91b and a transit depth determined to a precision of 111 ppm. We use transitfit to conduct an investigation into the contested presence of TTV signatures in WASP-126b using 180 transits observed by TESS, concluding that there is no statistically significant evidence for such signatures from observations spanning 27 TESS sectors. We fit HST observations of WASP-43 b, demonstrating how transitfit can use custom detrending algorithms to remove complex baseline systematics. Lastly, we present a transmission spectrum of the atmosphere of WASP-96b constructed from simultaneous fitting of JWST NIRISS Early Release Observations and archive HST WFC3 transit data. The transmission spectrum shows generally good correspondence between spectral features present in both data sets, despite very different detrending requirements.

Intelligent identification on cotton verticillium wilt based on spectral and image feature fusion

BackgroundVerticillium wilt is the major disease of cotton, which would cause serious yield reduction and economic losses, and the identification of cotton verticillium wilt is of great significance to cotton research. However, the traditional method is still manual, which is subjective, inefficient, and labor-intensive, and therefore, this study has proposed a novel method for cotton verticillium wilt identification based on spectral and image feature fusion. The cotton hyper-spectral images have been collected, while the regions of interest (ROI) have been extracted as samples including 499 healthy leaves and 498 diseased leaves, and the average spectral information and RGB image of each sample were obtained. In spectral feature processing, the preprocessing methods including Savitzky-Golay smoothing (SG), multiplicative scatter correction (MSC), de-trending (DT) and mean normalization (MN) algorithms have been adopted, while the feature band extraction methods have adopted principal component analysis (PCA) and successive projections algorithm (SPA). In RGB image feature processing, the EfficientNet was applied to build classification model and 16 image features have been extracted from the last convolutional layer. And then, the obtained spectral and image features were fused, while the classification model was established by support vector machine (SVM) and back propagation neural network (BPNN). Additionally, the spectral full bands and feature bands were used as comparison for SVM and BPNN classification respectively.ResultThe results showed that the average accuracy of EfficientNet for cotton verticillium wilt identification was 93.00%. By spectral full bands, SG-MSC-BPNN model obtained the better performance with classification accuracy of 93.78%. By feature bands, SG-MN-SPA-BPNN model obtained the better performance with classification accuracy of 93.78%. By spectral and image fused features, SG-MN-SPA-FF-BPNN model obtained the best performance with classification accuracy of 98.99%.ConclusionsThe study demonstrated that it was feasible and effective to use fused spectral and image features based on hyper-spectral imaging to improve identification accuracy of cotton verticillium wilt. The study provided theoretical basis and methods for non-destructive and accurate identification of cotton verticillium wilt.

QLP Data Release Notes 002: Improved Detrending Algorithm

Light curves feature many kinds of variability, including instrumental systematics, intrinsic stellar variability such as pulsations, and flux changes caused by transiting exoplanets or eclipsing binary stars. Detrending is a key pre-planet-search data processing step that aims to remove variability not due to transits. This data release note describes improvements to the Quick-Look Pipeline's detrending algorithm via the inclusion of quaternion data to remove short-timescale systematics. We describe updates to our procedure, intermediate data products outputted by the algorithm, and improvements to light curve precision.

Multi-Step Traffic Speed Prediction Based on Ensemble Learning on an Urban Road Network

Short-term traffic speed prediction plays an important role in the field of Intelligent Transportation Systems (ITS). Usually, traffic speed forecasting can be divided into single-step-ahead and multi-step-ahead. Compared with the single-step method, multi-step prediction can provide more future traffic condition to road traffic participants for guidance decision-making. This paper proposes a multi-step traffic speed forecasting by using ensemble learning model with traffic speed detrending algorithm. Firstly, the correlation analysis is conducted to determine the representative features by considering the spatial and temporal characteristics of traffic speed. Then, the traffic speed time series is split into a trend set and a residual set via a detrending algorithm. Thirdly, a multi-step residual prediction with direct strategy is formulated by the ensemble learning model of stacking integrating support vector machine (SVM), CATBOOST, and K-nearest neighbor (KNN). Finally, the forecasting traffic speed can be reached by adding predicted residual part to the trend one. In tests that used field data from Zhongshan, China, the experimental results indicate that the proposed model outperforms the benchmark ones like SVM, CATBOOST, KNN, and BAGGING.

First Detection of Hydroxyl Radical Emission from an Exoplanet Atmosphere: High-dispersion Characterization of WASP-33b Using Subaru/IRD

Abstract We report the first detection of a hydroxyl radical (OH) emission signature in the planetary atmosphere outside the solar system, in this case, in the dayside of WASP-33b. We analyze high-resolution near-infrared emission spectra of WASP-33b taken using the InfraRed Doppler spectrograph on the 8.2 m Subaru telescope. The telluric and stellar lines are removed using a detrending algorithm, SysRem. The residuals are then cross-correlated with OH and H2O planetary spectrum templates produced using several different line lists. We check and confirm the accuracy of OH line lists by cross-correlating with the spectrum of GJ 436. As a result, we detect the emission signature of OH at K p of km s−1 and v sys of −0.3 km s−1 with a signal-to-noise ratio (S/N) of 5.4 and a significance of 5.5σ. Additionally, we marginally detect H2O emission in the H-band with an S/N of 4.0 and a significance of 5.2σ using the POKAZATEL line list. However, no significant signal is detected using the HITEMP 2010, which might be due to differences in line positions and strengths, as well as the incompleteness of the line lists. Nonetheless, this marginal detection is consistent with the prediction that H2O is mostly thermally dissociated in the upper atmosphere of the ultra-hot Jupiters. Therefore, along with CO, OH is expected to be one of the most abundant O-bearing molecules in the dayside atmosphere of ultra-hot Jupiters and should be considered when studying their atmospheres.

Classification of Lingwu long jujube internal bruise over time based on visible near-infrared hyperspectral imaging combined with partial least squares-discriminant analysis

Early detection of internal bruise is one of the major challenges in postharvest quality sorting processes in Lingwu long jujube. In this study, the visible/near infrared (VIS/NIR) hyperspectral imaging system (400–1000 nm) was used to rapidly detect the intact and damaged jujube at five time points after mechanical damage (2 h, 4 h, 8 h, 12 h and 24 h). The region of interest of samples was selected by ENVI software, and the average spectrum was calculated for modelling. Different preprocessing methods were used to transform and enhance the spectral signal. Partial least squares-discriminant analysis (PLS-DA) classification models of the original and preprocessed spectra were established. The de-trending-PLS-DA model had the best effect, the accuracy of the calibration set and prediction set were 85.56% and 92.22%, respectively. The spectra were pre-processed by the de-trending algorithm, and the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), interval variable iterative space shrinkage approach (iVISSA), cluster analysis (CA), two-dimensional correlation spectra (2D-COS), UVE-SPA, CARS-SPA, iVISSA-SPA, CA-SPA and 2D-COS-SPA method were used to select characteristic variables. The PLS-DA model based on feature variables was established. The de-trending-CARS-PLS-DA model with 63 variables was found to be the optimal model. In the de-trending-CARS-PLS-DA model, the accuracy of calibration set and prediction set were 86.67% and 91.11%, respectively. It was found that the model accurately detected bruising in jujube at 8 h after bruising, and the accuracy of calibration set and prediction set were 100%. The results showed that VIS/NIR hyperspectral imaging technology combined with PLS-DA could discriminate different stages of bruising in Lingwu long jujube. This study may help develop an online detection system of bruising in Lingwu long jujube.

Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves

Spectroscopic sensing provides physical and chemical information in a non-destructive and rapid manner. To develop non-destructive estimation methods of tea quality-related metabolites in fresh leaves, we estimated the contents of free amino acids, catechins, and caffeine in fresh tea leaves using visible to short-wave infrared hyperspectral reflectance data and machine learning algorithms. We acquired these data from approximately 200 new leaves with various status and then constructed the regression model in the combination of six spectral patterns with pre-processing and five algorithms. In most phenotypes, the combination of de-trending pre-processing and Cubist algorithms was robustly selected as the best combination in each round over 100 repetitions that were evaluated based on the ratio of performance to deviation (RPD) values. The mean RPD values were ranged from 1.1 to 2.7 and most of them were above the acceptable or accurate threshold (RPD = 1.4 or 2.0, respectively). Data-based sensitivity analysis identified the important hyperspectral regions around 1500 and 2000 nm. Present spectroscopic approaches indicate that most tea quality-related metabolites can be estimated non-destructively, and pre-processing techniques help to improve its accuracy.

Detection of Fe i Emission in the Dayside Spectrum of WASP-33b*

Abstract We analyze the high-resolution emission spectrum of WASP-33b taken using the High Dispersion Spectrograph (R ≈ 165,000) on the 8.2 m Subaru telescope. The data cover λ ≈ 6170–8817 Å, divided over 30 spectral orders. The telluric and stellar lines are removed using a de-trending algorithm, SysRem, before cross-correlating with planetary spectral templates. We calculate the templates assuming a 1D plane-parallel hydrostatic atmosphere including continuum opacity of bound–free H− and Rayleigh scattering by H2 with a range of constant abundances of Fe i. Using a likelihood-mapping analysis, we detect an Fe i emission signature at 6.4σ located at of 226.0 km s−1 and v sys of −3.2 km s−1—consistent with the planet’s expected velocity in the literature. We also confirm the existence of a thermal inversion in the dayside of the planet, which is very likely to be caused by the presence of Fe i and previously detected TiO in the atmosphere. This makes WASP-33b one of the prime targets to study the relative contributions of both species to the energy budget of an ultra-hot Jupiter.

On forcings of length of day changes: From 9-day to 18.6-year oscillations

We analyze fluctuations in Earth's rotation velocity (therefore also the equivalent length of day), using >50 years of IERS observations and the powerful method of Singular Spectral Analysis. The first 16 eigenvalues uncovered by SSA correspond to 10 components, all with physical sense. The first component is the trend, the second is the lunar node tide (18.6 yr, amplitude 1.3 ms). Next are variations with a period that implies forcing related to solar activity (11 years, 0.46 ms amplitude). Then, zonal oscillations linked to the solar (1 year, 0.81 ms; 0.5 year, 0.76 ms;) and lunar (27.54 days, 0.39 ms; 13.66 days, 0.73 ms; 13.63 days, 0.27 ms; 9.13 days, 0.14 ms) tidal potentials. The QBO at 2.36 years (0.08 ms) is interpreted as a Sun-related oscillation. The components at 13.63 and 13.66 days could contain a solar contribution. SSA is an efficient detrending algorithm and way to identify irregular (quasi-periodical) oscillatory components: its application to l.o.d. data yields refined observations, in good agreement with recent models but with some new results. There is no extracted component that could not be attributed to an existing periodic or quasi-periodic physical phenomenon. Progress in computers and signal processing has allowed us to resume and extend the analysis of l.o.d., and in particular to show evidence of a solar signature in the series. Uncovering the mechanisms through which solar activity acts on Earth's rotation is an exciting project for geophysicists that deserve renewed attention.

No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI

As a consequence of recent technological advances in the field of functional magnetic resonance imaging (fMRI), results can now be made available in real-time. This allows for novel applications such as online quality assurance of the acquisition, intra-operative fMRI, brain-computer-interfaces, and neurofeedback. To that aim, signal processing algorithms for real-time fMRI must reliably correct signal contaminations due to physiological noise, head motion, and scanner drift. The aim of this study was to compare performance of the commonly used online detrending algorithms exponential moving average (EMA), incremental general linear model (iGLM) and sliding window iGLM (iGLMwindow). For comparison, we also included offline detrending algorithms (i.e., MATLAB's and SPM8's native detrending functions). Additionally, we optimized the EMA control parameter, by assessing the algorithm's performance on a simulated data set with an exhaustive set of realistic experimental design parameters. First, we optimized the free parameters of the online and offline detrending algorithms. Next, using simulated data, we systematically compared the performance of the algorithms with respect to varying levels of Gaussian and colored noise, linear and non-linear drifts, spikes, and step function artifacts. Additionally, using in vivo data from an actual rt-fMRI experiment, we validated our results in a post hoc offline comparison of the different detrending algorithms. Quantitative measures show that all algorithms perform well, even though they are differently affected by the different artifact types. The iGLM approach outperforms the other online algorithms and achieves online detrending performance that is as good as that of offline procedures. These results may guide developers and users of real-time fMRI analyses tools to best account for the problem of signal drifts in real-time fMRI.

An incipient fault detection approach via detrending and denoising

An incipient fault tends to be buried by either the process trend or the measurement noise. Fault–trend ratio (FTR) and fault–noise ratio (FNR) are two main factors that impact the detection performance. An incipient fault detection approach is proposed in this paper based on the detrending and denoising techniques. There are three main phases in this approach. First, to increase FTR, a detrending algorithm is implemented. The fault detection rate can be significantly enhanced, when the normal trend is eliminated from the testing residual. Second, to increase FNR, a denoising algorithm is realized. The residual obtained from this algorithm can avoid the incipient fault being buried by the widely oscillating noise. Therefore the fault detection performance can be further improved. Third, the new detection statistic is composed based on the two algorithms. The approach is applied to a simulated process, a satellite attitude control system process, and the Tennessee Eastman process. The comparison results show that the proposed method outperforms the traditional Hotelling method in detecting incipient faults.

A modified CoRoT detrend algorithm and the discovery of a new planetary companion

We present MCDA, a modification of the CoRoT detrend algorithm (CDA) suitable to detrend chromatic light curves. By means of robust statistics and better handling of short term variability, the implementation decreases the systematic light curve variations and improves the detection of exoplanets when compared with the original algorithm. All CoRoT chromatic light curves (a total of 65,655) were analysed with our algorithm. Dozens of new transit candidates and all previously known CoRoT exoplanets were rediscovered in those light curves using a box-fitting algorithm. For three of the new cases spectroscopic measurements of the candidates' host stars were retrieved from the ESO Science Archive Facility and used to calculate stellar parameters and, in the best cases, radial velocities. In addition to our improved detrend technique we announce the discovery of a planet that orbits a $0.79_{-0.09}^{+0.08}\,R_\odot$ star with a period of $6.71837\pm0.00001$ days and has $0.57_{-0.05}^{+0.06}\,R_{\rm J}$ and $0.15\pm0.10\,M_{\rm J}$. We also present the analysis of two cases in which parameters found suggest the existence of possible planetary companions.

Nandb-number and brightness in R with a novel automatic detrending algorithm.

SummaryAn R package for performing number and brightness image analysis, with the implementation of a novel automatic detrending algorithm.Availability and implementationAvailable at https://github.com/rorynolan/nandb for all platforms.Contact rnolan@well.ox.ac.uk or spadilla@well.ox.ac.ukSupplementary information Supplementary data are available at Bioinformatics online.

TMS-evoked long-lasting artefacts: A new adaptive algorithm for EEG signal correction.

During EEG the discharge of TMS generates a long-lasting decay artefact (DA) that makes the analysis of TMS-evoked potentials (TEPs) difficult. Our aim was twofold: (1) to describe how the DA affects the recorded EEG and (2) to develop a new adaptive detrend algorithm (ADA) able to correct the DA. We performed two experiments testing 50 healthy volunteers. In experiment 1, we tested the efficacy of ADA by comparing it with two commonly-used independent component analysis (ICA) algorithms. In experiment 2, we further investigated the efficiency of ADA and the impact of the DA evoked from TMS over frontal, motor and parietal areas. Our results demonstrated that (1) the DA affected the EEG signal in the spatiotemporal domain; (2) ADA was able to completely remove the DA without affecting the TEP waveforms; (3). ICA corrections produced significant changes in peak-to-peak TEP amplitude. ADA is a reliable solution for the DA correction, especially considering that (1) it does not affect physiological responses; (2) it is completely data-driven and (3) its effectiveness does not depend on the characteristics of the artefact and on the number of recording electrodes. We proposed a new reliable algorithm of correction for long-lasting TMS-EEG artifacts.

Alleviation of numerical instability in geophone-based measurement of track vibrations by utilizing a priori information

Deflections of structural elements like railway tracks and bridge spans, at which a reference point is not available for deflection measurement, have posed a dire predicament in the industry. It is because a reference point for measurement of deflections may not be available at most of structures due to the vibration of a whole structure. Even a modern technology like a high speed camera does not provide a sufficient quality to measure deflections due to the same reason. One solution to this predicament is proposed in this paper, a solution based on measurement of velocity with geophones and new algorithm of velocity integration utilizing a priori information. A priori information is employed to minimize numerical instability induced by missing integration constants. In addition, a detrending algorithm is incorporated to get rid of undesirable a low-frequency numerical artifact. Validity and reliability of the proposed method was verified by laboratory tests and finite element analysis. The laboratory tests were based on Linear Variable Differential Transformer (LVDT) measurements of geophones shaken by an electro-magnetic vibrator and on potentiometer measurements for a concrete sleeper under cyclic loading by a hydraulic actuator. For validation of feasibility and practicality, the developed algorithm was applied to evaluate performance of concrete sleepers on ballast track.

Real-time determination of sarcomere length of a single cardiomyocyte during contraction

Sarcomere length of a cardiomyocyte is an important control parameter for physiology studies on a single cell level; for instance, its accurate determination in real time is essential for performing single cardiomyocyte contraction experiments. The aim of this work is to develop an efficient and accurate method for estimating a mean sarcomere length of a contracting cardiomyocyte using microscopy images as an input. The novelty in developed method lies in 1) using unbiased measure of similarities to eliminate systematic errors from conventional autocorrelation function (ACF)-based methods when applied to region of interest of an image, 2) using a semianalytical, seminumerical approach for evaluating the similarity measure to take into account spatial dependence of neighboring image pixels, and 3) using a detrend algorithm to extract the sarcomere striation pattern content from the microscopy images. The developed sarcomere length estimation procedure has superior computational efficiency and estimation accuracy compared with the conventional ACF and spectral analysis-based methods using fast Fourier transform. As shown by analyzing synthetic images with the known periodicity, the estimates obtained by the developed method are more accurate at the subpixel level than ones obtained using ACF analysis. When applied in practice on rat cardiomyocytes, our method was found to be robust to the choice of the region of interest that may 1) include projections of carbon fibers and nucleus, 2) have uneven background, and 3) be slightly disoriented with respect to average direction of sarcomere striation pattern. The developed method is implemented in open-source software.

Detrending methods for fluctuation analysis in hydrology: amendments and comparisons of methodologies

Detrending is a key step in the study of the scaling behaviors using Detrended Fluctuation Analysis (DFA) to explore the long-range correlation of hydrological series. However, the irregular periodicity and various trends within hydrological series as a result of integrated influences of human activities such as construction of water reservoirs and human withdrawal of freshwater and climate changes such as alterations of precipitation changes in both space and time make difficult the selection of detrending methods. In this study, we attempt to address the detrending problem due to the important theoretical and practical merits of detrending in DFA-based scaling analysis. In this case, with focus on the irregularity of the periodic trends, a modified DFA, varying parameter DFA (VPDFA), and its combination with adaptive detrending algorithm (ADA) are employed to eliminate the influences of irregular cycles on DFA-based scaling results. The results indicate that, for streamflow series with no more than 20 cycles, VPDFA is recommended; otherwise, the combined method has to be employed. Comparison study indicates that the scaling behavior of the detrended observed streamflow series by average removed method, when compared to those by DFA, VPDFA, and ADA, is the one of the periodic residues around the averaged annual cycle for the entire series rather than that excluding all annual cycles. However, although the result by VPDFA for short observed streamflow record can well correspond to that for numerically simulated series, the scaling behavior obtained by combined method analyzing long record looks strange and is different from that by numerical analysis. We attribute this difference to the complicated hydrological structure and the possible hydrological alternation due to the increasing integrated impacts of human activities and human activities with the extending record. How to include the most of the important factors into the detrending procedure is still a challenging task for further study in the analysis of the scaling behavior of hydrological processes. Copyright © 2012 John Wiley & Sons, Ltd.