Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

By Ravinesh C. Deo, Mehmet Sahin. 2015

302 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/0f44df746ec0939b2f4df0f976f0738d490c1495

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In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey (2018, methodology): “…important to note that for both MLP-based predictive models, the Levenberg–Marquardt (LM) backpropagation learning algorithm considered as a fast and efficient tool compared to the other training algorithm (e.g., Adamowski et al. 2012; Deo and Şahin 2015b, 2016; Deo et al. 2017b) has been adopted.”
Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models (2016, methodology): “The hidden layer bias exactly learns from distinct observations; with most of the training accomplished in short time spans which are not easily achieved by using traditional neural network models (Acharya et al. 2013; Deo and Şahin 2015a, b).”
Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment (2018, methodology): “The modelling stages were operated according to the procedures in our previous studies, e.g., Deo and Samui (2017); Deo et al. (2016, 2017b); Deo and Şahin (2015, 2016).”
Meteorological drought prediction using heuristic approaches based on effective drought index: a case study in Uttarakhand (2020, methodology): “…studies conducted on meteorological drought forecasting using machine learning and stochastic models based on various drought indices such as (Mishra and Desai 2006; Belayneh et al. 2014; Deo and Şahin, 2015a, b; Deo et al. 2017a; Mouatadid et al. 2018; Ali et al. 2018; Abbasi et al. 2019).”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, methodology): “However, it must also be noted that without wavelet filtering of the predictor data the performance of the classical ELMmodel in this study was inferior to that of Deo and Sahin (2015b).”
Machine learning and artificial intelligence to aid climate change research and preparedness (2019, background): “Deo and Sahin (2015) Climate extremes Predictmeteorological and agricultural drought conditions from satellite data.”