Machine learning-based anomaly detection of groundwater microdynamics: case study of Chengdu, China

Shi, Haoxin; Guo, Jian; Deng, Yuandong; Qin, Zixuan

doi:10.1038/s41598-023-38447-5

Cited by 5 publications

(1 citation statement)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Collectively, the findings suggest that three variables, namely, LST at daytime from February to May (X13), NDVI from June to September (X20), and precipitation from February to May (X4), consistently show strong importance across different models. Previous research also highlights temperature [56], precipitation [56][57][58], and NDVI [59,60] as primary factors influencing groundwater levels. Some inconsistencies are observed between the analysis and the importance values from the machine learning models.…”

Section: Important Variables Analysismentioning

confidence: 99%

Groundwater Level Prediction with Machine Learning to Support Sustainable Irrigation in Water Scarcity Regions

Li,

Finsa,

Laskey

et al. 2023

Water

View full text Add to dashboard Cite

Predicting groundwater levels is challenging, especially in regions of water scarcity where data availability is often limited. However, these regions have substantial water needs and require cost-effective groundwater utilization strategies. This study uses artificial intelligence to predict groundwater levels to provide guidance for drilling shallow boreholes for subsistence irrigation. The Bilate watershed, located 80 km north of Arba Minch in southern Ethiopia and covering just over 5250 km2, was selected as the study area. Bilate is typical of areas in Africa with high demand for water and limited availability of well data. Using a non-time series database of 75 boreholes, machine learning models, including multiple linear regression, multivariate adaptive regression splines, artificial neural networks, random forest regression, and gradient boosting regression (GBR), were constructed to predict the depth to the water table. The study considered 20 independent variables, including elevation, soil type, and seasonal data (spanning three seasons) for precipitation, specific humidity, wind speed, land surface temperature during day and night, and Normalized Difference Vegetation Index (NDVI). GBR performed the best of the approaches, with an average 0.77 R-squared value and a 19 m median absolute error on testing data. Finally, a map of predicted water levels in the Bilate watershed was created based on the best model, with water levels ranging from 1.6 to 245.9 m. With the limited set of borehole data, the results show a clear signal that can provide guidance for borehole drilling decisions for sustainable irrigation with additional implications for drinking water.

show abstract

Section: Important Variables Analysismentioning

confidence: 99%

Groundwater Level Prediction with Machine Learning to Support Sustainable Irrigation in Water Scarcity Regions

Li,

Finsa,

Laskey

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

Transformative Potential of AI and Remote Sensing in Sustainable Groundwater Management

Singh,

Sharma

2024

Innovations in Sustainable Technologies and Computing

View full text Add to dashboard Cite

Uncertainty in Environmental Micropollutant Modeling

Ahkola,

Kotamäki,

Siivola

et al. 2024

Environmental Management

View full text Add to dashboard Cite

Water pollution policies have been enacted across the globe to minimize the environmental risks posed by micropollutants (MPs). For regulative institutions to be able to ensure the realization of environmental objectives, they need information on the environmental fate of MPs. Furthermore, there is an urgent need to further improve environmental decision-making, which heavily relies on scientific data. Use of mathematical and computational modeling in environmental permit processes for water construction activities has increased. Uncertainty of input data considers several steps from sampling and analysis to physico-chemical characteristics of MP. Machine learning (ML) methods are an emerging technique in this field. ML techniques might become more crucial for MP modeling as the amount of data is constantly increasing and the emerging new ML approaches and applications are developed. It seems that both modeling strategies, traditional and ML, use quite similar methods to obtain uncertainties. Process based models cannot consider all known and relevant processes, making the comprehensive estimation of uncertainty challenging. Problems in a comprehensive uncertainty analysis within ML approach are even greater. For both approaches generic and common method seems to be more useful in a practice than those emerging from ab initio. The implementation of the modeling results, including uncertainty and the precautionary principle, should be researched more deeply to achieve a reliable estimation of the effect of an action on the chemical and ecological status of an environment without underestimating or overestimating the risk. The prevailing uncertainties need to be identified and acknowledged and if possible, reduced. This paper provides an overview of different aspects that concern the topic of uncertainty in MP modeling.

show abstract

Machine learning-based anomaly detection of groundwater microdynamics: case study of Chengdu, China

Cited by 5 publications

References 54 publications

Groundwater Level Prediction with Machine Learning to Support Sustainable Irrigation in Water Scarcity Regions

Groundwater Level Prediction with Machine Learning to Support Sustainable Irrigation in Water Scarcity Regions

Transformative Potential of AI and Remote Sensing in Sustainable Groundwater Management

Uncertainty in Environmental Micropollutant Modeling

Contact Info

Product

Resources

About