Spatial-Temporal Analysis of Environmental Data of North Beijing District Using Hilbert-Huang Transform

Abstract

Temperature, solar radiation and water are major important variables in ecosystem models which are measurable via wireless sensor networks (WSN). Effective data analysis is necessary to extract significant spatial and temporal information. In this work, information regarding the long term variation of seasonal field environment conditions is explored using Hilbert-Huang transform (HHT) based analysis on the wireless sensor network data collection. The data collection network, consisting of 36 wireless nodes, covers an area of 100 square kilometres in Yanqing, the northwest of Beijing CBD, in China and data collection involves environmental parameter observations taken over a period of three months in 2011. The analysis used the empirical mode decomposition (EMD/EEMD) to break a time sequence of data down to a finite set of intrinsic mode functions (IMFs). Both spatial and temporal properties of data explored by HHT analysis are demonstrated. Our research shows potential for better understanding the spatial-temporal relationships among environmental parameters using WSN and HHT.

Multiscale Temporal Variations of Atmospheric Mercury Distinguished by the Hilbert–Huang Transform Analysis Reveals Multiple El Niño–Southern Oscillation Links

“…The Hilbert–Huang transform (HHT) is a tool for calculating the instantaneous amplitude and frequency of data ,, and consists of two parts: a decomposition (EMD or EEMD) and a time-frequency signal analysis (HSA). , Both EMD and EEMD have been extensively applied to geophysical signals and are widely used for extracting physical information from nonlinear time series data. ,− , EMD decomposes the intrinsic nature of the raw signal adaptively into single modes, known as intrinsic mode functions (IMFs) that retain the physical properties of the signal, and where higher IMFs are of progressively lower oscillation with each successive EMD. Each IMF is independent of the others and should be interpreted accordingly. ,, After decomposition, HSA is then used to gather the local energy and instantaneous frequency information for each IMF and produce the HHT. , Thus, the HHT partly resolves the flaws in existing traditional spectral analytical methods, which rely on additive expansions and a linear assumption. − …”

“…Traditional methods for analyzing multiscale temporal data (e.g., Fourier and wavelet transforms) rely on additive expansions that generate constant amplitudes and frequencies, thus only having physical meaning for linear stationary data. − In fact, a foundational study for this work used an empirical mode decomposition (EMD) to analyze the GEM trends at Mauna Loa but was limited to a time-domain analysis of a data set of 7 years. To overcome the challenges of characterizing complex temporal variations of random, nonlinear environmental data, recent studies have employed a Hilbert–Huang transform (HHT) method which efficiently extracts information from both time and frequency domains directly without any assumptions. ,− In the HHT framework, the raw data signal is first decomposed into different intrinsic mode functions (IMFs) by ensemble EMD (EEMD), and then, the IMFs are processed by Hilbert spectral analysis (HSA), enabling the user to examine complicated intra- and intermode modulations explicitly and quantitatively. , Moreover, under the HHT platform, we can estimate the phase synchronization and time lag between two oscillations through Hilbert-based time delay analysis. , With those advantages, the HHT method has been successfully applied to examine overlapping low- and high-frequency patterns within geophysical and environmental data sets, ,,,, but as of yet, the HHT has not been employed to disentangle the linkages between ENSO and Hg data.…”

Dynamic Changes and Temporal Association with Ambient Temperatures: Nonlinear Analyses of Stroke Events from a National Health Insurance Database

“…Of note, the correlations were stronger in the Taipei Area (subtropical area) than in Kaohsiung City (tropical area). A similar method was applied to study the spatial–temporal association of environmental factors, including temperature, soil moisture, and photosynthetically active radiation [ 38 ].…”

Assessing Nonlinear Dynamics and Trends in Precipitation by Ensemble Empirical Mode Decomposition (EEMD) and Fractal Approach in Benin Republic (West Africa)

“…Since each IMF represents different intrinsic modes of oscillation, it is possible to calculate the period for each of them. e average periods are calculated in this study by the time intervals between consecutive zero-crossings on successive waves as in [50].…”