Intrinsic mode functions and a strategy for forecasting Indian monsoon rainfall

Iyengar, R. N.; Kanth, S. T. G. Raghu

doi:10.1007/s00703-004-0089-4

Cited by 66 publications

(36 citation statements)

References 17 publications

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“…With the increase in the distance between each extrema, the extrapolation errors will spread into the signal and misrepresent the higher IMFs at the end points. Iyengar and Raghukanth (2005) have modeled the linear and non-linear parts separately to circumvent this end problem of IMFs.…”

Section: Fig 12mentioning

confidence: 99%

Regional level forecasting of seismic energy release

Kavitha

Raghukanth

2015

Acta Geod Geophys

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This article explores a new strategy for forecasting of earthquake energy release in the seismogenic zones of the world. A total of 41 active seismogenic zones are identified with the help of past seismicity data. The magnitudes of individual events occurred in each zone are converted into seismic energy using an empirical relation. The annual earthquake energy time series is constructed by adding the energy releases of all the events in a particular year. The technique of principal component analysis is employed for the regionalization of these seismogenic zones using seismic energy time series. The annual energy time series of seismogenic zones are decomposed into finite number of intrinsic mode functions (IMFs) using ensemble empirical mode decomposition technique. The periodicities of the IMFs and their contribution to the total variance of the earthquake energy release are examined. The artificial neural network technique is used for modeling and forecasting the energy-time series of seismogenic zones. The model is verified with an independent subset of data and validated using statistical parameters. The forecast of the annual earthquake energy release in each seismogenic zone is provided for the year 2015.

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Section: Fig 12mentioning

confidence: 99%

Regional level forecasting of seismic energy release

Kavitha

Raghukanth

2015

Acta Geod Geophys

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“…One advantage of using these techniques is in dealing with long-term natural processes, which are commonly observed nonlinear and nonstationary. The EMD and HHT are widely used in various fields of science and engineering: meteorology and atmospheric physics [91][92][93][94][95][96], earthquake engineering, structural health monitoring (SHM), and control for civil structures [97][98][99][100][101][102].…”

Section: The Empirical Mode Decomposition and The Hilberthuang Transfmentioning

confidence: 99%

Nonparametric Monitoring for Geotechnical Structures Subject to Long-Term Environmental Change

Yun

Reddi

2011

Advances in Civil Engineering

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A nonparametric, data-driven methodology of monitoring for geotechnical structures subject to long-term environmental change is discussed. Avoiding physical assumptions or excessive simplification of the monitored structures, the nonparametric monitoring methodology presented in this paper provides reliable performance-related information particularly when the collection of sensor data is limited. For the validation of the nonparametric methodology, a field case study was performed using a full-scale retaining wall, which had been monitored for three years using three tilt gauges. Using the very limited sensor data, it is demonstrated that important performance-related information, such as drainage performance and sensor damage, could be disentangled from significant daily, seasonal and multiyear environmental variations. Extensive literature review on recent developments of parametric and nonparametric data processing techniques for geotechnical applications is also presented.

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“…Iyengar and Raghukanth developed rainfall-forecasting model using intrinsic mode function. They used Artificial Neural Network and regression for model development [3]. The rainfall prediction efficiency is 75% to 80% of the interannual variability.…”

Section: Introductionmentioning

confidence: 99%

A Fast Learning Algorithm for Rainfall Prediction

Mahajan¹,

Mazumdar²

2014

IJCA

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A PC based application is developed using 51 years of Indian rainfall data for long range prediction of average rain fall. This learning algorithm iteratively estimates 96 coefficients of a 5th order polynomial in few minutes. Proposed prediction model is based on modelling of time series rainfall data using 5th order non-linear predicting code. Steepest descent algorithm is used for extraction of appropriate coefficient of rainfall time series data. These coefficients are reinforced in model learning process. The rainfall data of 1960 to 2010 is used for the development of the model. Model has been tested on rainfall data for different training sets. The proposed model is capable of forecasting yearly rainfall 1 year in advance. Rainfall estimation accuracy is above 85%.

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Intrinsic mode functions and a strategy for forecasting Indian monsoon rainfall

Cited by 66 publications

References 17 publications

Regional level forecasting of seismic energy release

Regional level forecasting of seismic energy release

Nonparametric Monitoring for Geotechnical Structures Subject to Long-Term Environmental Change

A Fast Learning Algorithm for Rainfall Prediction

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