Multi-stage committee based extreme learning machine model incorporating the influence of climate parameters and seasonality on drought forecasting

Ali, Mumtaz and Deo, Ravinesh C. and Downs, Nathan J. and Maraseni, Tek (2018) Multi-stage committee based extreme learning machine model incorporating the influence of climate parameters and seasonality on drought forecasting. Computers and Electronics in Agriculture, 152. pp. 149-165. ISSN 0168-1699

Abstract

Drought forewarning is an important decisive task since drought is perceived a recurrent feature of climate variability and climate change leading to catastrophic consequences for agriculture, ecosystem sustainability, and food and water scarcity. This study designs and evaluates a soft-computing drought modelling framework in context of Pakistan, a drought-stricken nation, by means of a committee extreme learning machine (Comm-ELM) model in respect to a committee particle swarm optimization-adaptive neuro fuzzy inference system (Comm-PSO-ANFIS) and committee multiple linear regression (Comm-MLR) model applied to forecast monthly standardized precipitation index (SPI). The proposed Comm-ELM model incorporates historical monthly rainfall, temperature, humidity, Southern Oscillation Index (SOI) at monthly lag (t − 1) and the respective month (i.e., periodicity factor) as the explanatory variable for the drought’s behaviour defined by SPI. The model accuracy is assessed by root mean squared error, mean absolute error, correlation coefficient, Willmott’s index, Nash-Sutcliffe efficiency and Legates McCabe’s index in the independent test dataset. With the incorporation of periodicity as an input factor, the performance of the Comm-ELM model for Islamabad, Multan and Dera Ismail Khan (D. I. Khan) as the test stations, was remarkably improved in respect to the Comm-PSO-ANFIS and Comm-MLR model. Other than the superiority of Comm-ELM over the alternative models tested for monthly SPI forecasting, we also highlight the importance of the periodicity cycle as a pertinent predictor variable in a drought forecasting model. The results ascertain that the model accuracy scales with geographic factors, due to the complexity of drought phenomenon and its relationship with the different inputs and data attributes that can affect the overall evolution of a drought event. The findings of this study has important implications for agricultural decision-making where future knowledge of drought can be used to develop climate risk mitigation strategies for better crop management.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version, in accordance with the copyright policy of the publisher. This research was supported by USQ Postgraduate Scholarship to Mumtaz Ali, supervised by Dr Ravinesh Deo (Principal Supervisor) and Dr Nathan Downs and A/Prof Tek Maraseni (Associate).
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 22 Jul 2018 23:39
Last Modified: 21 Sep 2018 05:22
Uncontrolled Keywords: standardized precipitation index; drought forecasting; committee model; extreme learning machine; particle swarm optimization based adaptive; neuro fuzzy inference system; multi-linear regression
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
04 Earth Sciences > 0401 Atmospheric Sciences > 040105 Climatology (excl.Climate Change Processes)
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
Identification Number or DOI: 10.1016/j.compag.2018.07.013
URI: http://eprints.usq.edu.au/id/eprint/34596

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