Robust regularized extreme learning machine with asymmetric Huber loss function

摘要

Sediment transport is one of the major challenging fields in hydrology. The tropical atmosphere, complex topography and occasional extreme precipitation are the fundamental explanations behind this challenge. Thus, the rivers in this situation contain a huge quantity of sediment, which may affect the river hydraulics. Hence, it is required to collect various parameters such as discharge, velocity, rainfall and sediment concentration to analyze the impact of sediment for river engineering practices and management. Therefore, the dataset which is collected from the river may contain outliers and noises. For improving the prediction accuracy of sediment load, we present robust regularized extreme learning machine frameworks to reduce the effect of noise by using the asymmetric Huber loss function named as AHELM and epsilon-insensitive Huber loss function named as epsilon-AHELM. Further, the problems are rewritten in the form of strongly convex minimization problems whose solutions are acquired by simple function iterative schemes. To ensure the effectiveness of the proposed approach, we have considered the real-world datasets with two types of noises. Furthermore, the proposed schemes are applied on real sediment load datasets (SLDs) which are collected from the Tawang Chu river of Arunachal Pradesh, India. The results reveal that proposed AHELM and epsilon-AHELM with multiquadric activation function are performed better for real-world datasets, whereas AHELM and epsilon-AHELM with sigmoid activation function perform efficiently and effectively for the sediment load prediction. In overall, the experimental results clearly exhibit the applicability as well as the usability of the proposed extreme learning machine with asymmetric Huber loss functions.