Abstract
Predicting the level of dissolved oxygen (DO) is an important issue ensuring the sustainability of the inhabitants of a river. A prediction model can predict the DO level using a historical dataset with regard to water temperature, pH, and specific conductance for a given river. The model can be built using sophisticated computational procedures such as multi-layer perceptron-based artificial neural networks. Different types of networks can be constructed for this purpose. In this study, the authors constructed three networks, namely, multi-verse optimizer (MVO), black hole algorithm (BHA), and shuffled complex evolution (SCE). The networks were trained using the datasets collected from the Klamath River Station, Oregon, USA, for the period 2015–2018. We found that the trained networks could predict the DO level of 2019. We also found that both BHA- and SCE-based networks could predict the level of DO using a relatively simple configuration compared to that of MVO. From the viewpoints of absolute errors and Pearson’s correlation coefficient, MVO- and SCE-based networks performed better than BHA-based networks. In synopsis, the authors recommend MVO- and MLP-based artificial neural networks for predicting the DO level of a river.
Highlights
After providing the appropriate dataset, the MLP is submitted to multi-verse optimizer (MVO), black hole algorithm (BHA), and shuffled complex evolution (SCE) algorithms to adjust its parameters through metaheuristic schemes
The proposed hybrid models are designed in the way that MVO, BHA, and SCE algorithms are responsible for adjusting the weights and biases of the MLP
The overall formulation of a neuron can be expressed as follows: RN = f (IN × W + b) where f (x) is the activation function used by the neurons in a layer; RN and IN
Summary
From a general point of view, the objective of many recent developments lies in facilitating complex analysis [7,8,9] These efforts have resulted in finding promising approaches for various analyses, such as water treatment [10,11], energy [12,13], construction and waste reduction [14,15,16], optics-related simulations [17,18], environmental modeling [19,20,21], and remote sensing [22]. Different intelligent methods have been devised by experts to give a reliable simulation of engineering problems [23,24,25]
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