Spatial predictions of groundwater potential using automated machine learning (AutoML): a comparative study of feature selection and training sample size in Qinghai Province, China

Wang, Zitao; Wang, Jianping; Li, Mengling

doi:10.1007/s11356-023-31262-5

Environ Sci Pollut Res

2023

DOI: 10.1007/s11356-023-31262-5

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Spatial predictions of groundwater potential using automated machine learning (AutoML): a comparative study of feature selection and training sample size in Qinghai Province, China

Zitao Wang,

Jianping Wang,

Mengling Li

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2024

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Cited by 2 publications

References 57 publications

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An optimization framework with dimensionality reduction using Markov Chain Monte Carlo and genetic algorithms for groundwater potential assessment

Wang,

Yue,

Wang

2024

Applied Soft Computing

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An optimization framework with dimensionality reduction using Markov Chain Monte Carlo and genetic algorithms for groundwater potential assessment

Wang,

Yue,

Wang

2024

Applied Soft Computing

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Water level prediction model of the upper Yangtze River by cloud computing and AutoML

Li,

Chen,

Lei

et al. 2024

Journal of Hydroinformatics

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The change in water level in the upper reaches of the Yangtze River is of great significance to flood control and navigation. As the first water control station for the mainstream of the Yangtze River after the Minjiang, Hengjiang, Tuojiang River, and other important tributaries flow into the Yangtze River, it is imperative to forecast the water level of Zhutuo Station accurately. The present study utilizes Microsoft Azure's automated machine learning platform (AutoML) and recurrent neural network (RNN) model to predict water levels at Zhutuo Station. The AutoML approach demonstrates certain advantages over RNN methodologies in terms of operability, resource utilization, computational efficiency, and hardware configuration requirements when predicting the water level of Zhutuo. The results show that the future 1-h forecast performance is similar, the mean absolute error (MAE) and root mean square error (RMSE) of the AutoML platform are 0.0098 and 0.012, respectively, and the MAE and RMSE of the RNN model are 0.0088 and 0.011, respectively. The prediction performance of the RNN model is better in the next 8 h and the next 24 h. The current study's outcomes contribute valuable insights for the real-time monitoring and predictive analytics of water levels, thereby enabling waterway managers to obtain the balance between model complexity and modeling convenience.

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Spatial predictions of groundwater potential using automated machine learning (AutoML): a comparative study of feature selection and training sample size in Qinghai Province, China

Cited by 2 publications

References 57 publications

An optimization framework with dimensionality reduction using Markov Chain Monte Carlo and genetic algorithms for groundwater potential assessment

An optimization framework with dimensionality reduction using Markov Chain Monte Carlo and genetic algorithms for groundwater potential assessment

Water level prediction model of the upper Yangtze River by cloud computing and AutoML

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