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

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.