AI Assists In Water Resource Management

Scientists have tested two models of artificial intelligence for forecasting groundwater reserves, showing that the support vector regression method has good predictive power.

AsianScientist (Dec. 1, 2017) – In a study published in Water Resources Management, scientists in China have applied artificial intelligence to forecast the depth of groundwater in arid regions.

Groundwater is an important resource for arid and semi-arid regions, and is significantly linked to ecological environments. Groundwater depth (GWD) influences the distribution of vegetation to affect salinization and land desertification. Therefore, the prediction of the GWD is a critical task in water resources management.

In this study, a research group from the Key Laboratory of Eco-hydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources of the Chinese Academy of Sciences, together with their colleague from Australia, investigated the three different observation wells in the Ejina Basin located at inner Mongolia.

They applied two different machine learning models: the wavelet-artificial neural network (WA-ANN) and the wavelet-support vector regression (WA-SVR) to record and make predictions about GWD. First, the researchers employed a function known as discrete wavelet transformation to decompose the data on GWD and meteorological inputs into approximations. They then embedded within their computations predictive features for high frequency and low frequency waves.

Their results revealed that the WA-SVR performed better than WA-ANN model. The authors claim that this is the first time that a set of hybrid artificial intelligent models has been used to predict GWD in the Ejina basin. They hope that their research will contribute towards a better understanding of the factors influencing ecological water conveyance in the area.

The article can be found at: Yu et al. (2017) Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China.


Source: Chinese Academy of Sciences; Photo: Pixabay.
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