Hybrid machine learning models for prediction of daily dissolved oxygen

水准点(测量) 人工神经网络 平均绝对百分比误差 平均绝对误差 灵敏度(控制系统) 计算机科学 人口 机器学习 人工智能 近似误差 均方预测误差 均方误差 统计 预测建模 数学 工程类 地质学 人口学 大地测量学 电子工程 社会学
作者
Aliasghar Azma,Yakun Liu,Masoumeh Azma,Mohsen Saadat,Di Zhang,Jinwoo Cho,Shahabaldin Rezania
出处
期刊:Journal of water process engineering [Elsevier]
卷期号:54: 103957-103957 被引量:8
标识
DOI:10.1016/j.jwpe.2023.103957
摘要

Measuring water quality parameters is a significant step in many hydrological assessments. Dissolved oxygen (DO) is one of these parameters that is an indicator of water quality. Hence, this study offers two novel intelligent models, i.e., the integration of biogeography-based optimization (BBO) and atom search optimization (ASO) with artificial neural network (ANN), to predict the daily DO. These methods are comparatively assessed and validated against several benchmark techniques. Five-year (2014–2019) water quality data of a USGS station called Rock Creek (Station number 01648010) is used for implementing the proposed model. In this sense, the models first learn the DO behavior using 80 % of the data and they then predict the DO for the fifth year. As per the performed sensitivity analysis, the water temperature was selected as the most effective parameter in the DO prediction. Trying different population sizes determined an optimal configuration of the employed models and assessing the accuracy of the results revealed that the proposed models can nicely perceive the DO pattern with around 4 % mean absolute percentage error (MAPE) and 97.5 % correlation. In the testing phase, the BBO-ANN and ASO-ANN models predicted the DO of the fifth year with MAPEs 2.3848 and 2.5170 %, and correlations of 0.99186 and 0.99135, respectively. Moreover, the suggested BBO-ANN and ASO-ANN outperformed some similar hybrids from the existing literature. Lastly, an explicit formula is derived from the BBO-ANN for convenient prediction of the DO.
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