Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model

By Ravinesh C. Deo, Mukesh Tiwari, Jan Adamowski, John M. Quilty. Stochastic environmental research and risk assessment (Print), 2017

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Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey (2018, methodology): “The optimal number of neurons in the hidden layer were identified using a trial and error procedure to screen the most accurate model with the lowest root mean square error, following earlier studies (Deo and Şahin 2015a, b; Deo et al. 2017c; Raheli et al. 2017).”
Meteorological drought prediction using heuristic approaches based on effective drought index: a case study in Uttarakhand (2020, methodology): “The relationship between ACF and PACF for kth lags is written as (Landeras et al. 2009; Deo et al. 2017b; Malik et al. 2019c) PACFk;k ¼ ACF−∑k−1j¼1PACFk−1; j ACFk−1 1−∑k−1j¼1PACFk−1; j ACFk−1 ð9Þ The calculated PACF values for kth lags were tested by drawing upper and lower critical limits (CLs) at…”