Version 1
: Received: 23 January 2021 / Approved: 25 January 2021 / Online: 25 January 2021 (09:50:43 CET)
How to cite:
Moayedi, H.; Mosavi, A. Hybridizing Neural Network with Multi-Verse, Black Hole, and Shuffled Complex Evolution Optimizer Algorithms Predicting the Dissolved Oxygen. Preprints2021, 2021010464. https://doi.org/10.20944/preprints202101.0464.v1
Moayedi, H.; Mosavi, A. Hybridizing Neural Network with Multi-Verse, Black Hole, and Shuffled Complex Evolution Optimizer Algorithms Predicting the Dissolved Oxygen. Preprints 2021, 2021010464. https://doi.org/10.20944/preprints202101.0464.v1
Moayedi, H.; Mosavi, A. Hybridizing Neural Network with Multi-Verse, Black Hole, and Shuffled Complex Evolution Optimizer Algorithms Predicting the Dissolved Oxygen. Preprints2021, 2021010464. https://doi.org/10.20944/preprints202101.0464.v1
APA Style
Moayedi, H., & Mosavi, A. (2021). Hybridizing Neural Network with Multi-Verse, Black Hole, and Shuffled Complex Evolution Optimizer Algorithms Predicting the Dissolved Oxygen. Preprints. https://doi.org/10.20944/preprints202101.0464.v1
Chicago/Turabian Style
Moayedi, H. and Amir Mosavi. 2021 "Hybridizing Neural Network with Multi-Verse, Black Hole, and Shuffled Complex Evolution Optimizer Algorithms Predicting the Dissolved Oxygen" Preprints. https://doi.org/10.20944/preprints202101.0464.v1
Abstract
The great importance of estimating dissolved oxygen (DO) dictates utilizing proper evaluative models. In this work, a multi-layer perceptron (MLP) network is trained by three capable metaheuristic algorithms, namely multi-verse optimizer (MVO), black hole algorithm (BHA), and shuffled complex evolution (SCE) for predicting the DO using the data of the Klamath River Station, Oregon, US. The records (DO, water temperature, pH, and specific conductance) belonging to the water years 2015 - 2018 (USGS) are used for pattern analysis. The results of this process showed that all three hybrid models could properly infer the DO behavior. However, the BHA and SCE accomplished this task by simpler configurations. Next, the generalization ability of the developed patterns is tested using the data of the 2019 water year. Referring to the calculated mean absolute errors of 1.0161, 1.1997, and 1.0122, as well as Pearson correlation coefficients of 0.8741, 0.8453, and 0.8775, the MLPs trained by the MVO and SCE perform better than the BHA. Therefore, these two hybrids (i.e., the MLP-MVO and MLP-SCE) can be satisfactorily used for future applications.
Keywords
Water quality; dissolved oxygen; Neural network; Metaheuristic schemes
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.