A MODEL INCORPORATING DISTURBANCE AND RECOVERY PROCESSES IN BENTHIC INVERTEBRATE HABITAT-FLOW TIME SERIES

2013 ◽  
Vol 30 (4) ◽  
pp. 413-426 ◽  
Author(s):  
D. A. Olsen ◽  
J. W. Hayes ◽  
D. J. Booker ◽  
P. J. Barter
Keyword(s):  
Time Series ◽  
Benthic Invertebrate ◽  
Flow Time ◽  
Recovery Processes ◽  
Disturbance And Recovery

Test of a Benthic Macroinvertebrate Habitat-Flow Time Series Model Incorporating Disturbance and Recovery Processes

2014 ◽  
Vol 31 (7) ◽  
pp. 785-797 ◽  
Author(s):  
J. W. Hayes ◽  
K. A. Shearer ◽  
E. O. Goodwin ◽  
J. Hay ◽  
C. Allen ◽  
...  
Keyword(s):  
Time Series ◽  
Flow Time ◽  
Time Series Model ◽  
Benthic Macroinvertebrate ◽  
Recovery Processes ◽  
Disturbance And Recovery

scholarly journals Burn severity and regeneration in large forest fires: an analysis from Landsat time series

2017 ◽  
pp. 17 ◽  
Author(s):  
S. Martínez ◽  
E. Chuvieco ◽  
I. Aguado ◽  
J. Salas
Keyword(s):  
Time Series ◽  
Forest Fires ◽  
Sustainable Forest Management ◽  
The Other ◽  
Burn Severity ◽  
Effective Strategies ◽  
Recovery Processes ◽  
Fire Condition ◽  
Spectral Profiles ◽  
Disturbance And Recovery

<p>The main objective of this study is to take a close look at post-fire recovery patterns in forestry areas under different burn severity conditions. We also investigate the time that forestry ecosystems take to recover their pre-fire condition. In this context, this study analyses both the level of severity in Uncastillo forest wildfire (7.664ha), one of the greatest occurred in Spain in 1994, and the pattern of natural recovery in the following decades (until 2014) using annual Landsat time series (sensors TM&amp;ETM+). Burn severity has been estimated by means of PROSPECT and GeoSAIL radiative transfer models following methodologies described in De Santis and Chuvieco (2009). On the other hand, recovery processes have been assessed from spectral profiles using the LandTrendr model (Landsat-based Detection of Trends in Disturbance and Recovery) (Kennedy et al., 2010). Results contribute to a further understanding of the post-fire evolution in forestry areas and to develop effective strategies for sustainable forest management.</p>

Application of Support Vector Regression for Modeling Low Flow Time Series

2018 ◽  
Vol 23 (2) ◽  
pp. 923-934 ◽  
Author(s):  
Bibhuti Bhusan Sahoo ◽  
Ramakar Jha ◽  
Anshuman Singh ◽  
Deepak Kumar
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Flow Time ◽  
Low Flow ◽  
Support Vector

scholarly journals An improved framework to predict river flow time series data

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7183 ◽  
Author(s):  
Hafiza Mamona Nazir ◽  
Ijaz Hussain ◽  
Ishfaq Ahmad ◽  
Muhammad Faisal ◽  
Ibrahim M. Almanjahie
Keyword(s):  
Time Series ◽  
Water Resource Management ◽  
River Flow ◽  
Time Series Data ◽  
Flow Time ◽  
Indus Basin ◽  
Series Data ◽  
Percentage Error ◽  
Empirical Bayesian ◽  
Noise Characteristics

Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using empirical Bayesian threshold to integrate the noises and sparsities of IMFs. Secondly, the denoised IMF’s and noise free IMF’s are further used as inputs in data-driven and simple stochastic models respectively to predict the river flow time series data. Finally, the predicted IMF’s are aggregated to get the final prediction. The proposed framework is illustrated by using four rivers of the Indus Basin System. The prediction performance is compared with Mean Square Error, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Our proposed method, CEEMDAN-EBT-MM, produced the smallest MAPE for all four case studies as compared with other methods. This suggests that our proposed hybrid model can be used as an efficient tool for providing the reliable prediction of non-stationary and noisy time series data to policymakers such as for planning power generation and water resource management.

Wavelet neural network model for river flow time series

2012 ◽  
Vol 165 (8) ◽  
pp. 425-439 ◽  
Author(s):  
Budu Krishna ◽  
Yellamelli Ramji Satyaji Rao ◽  
Purna Chandra Nayak
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
River Flow ◽  
Flow Time ◽  
Wavelet Neural Network
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