Groundwater Potential Mapping in Hubei Region of China Using Machine Learning, Ensemble Learning, Deep Learning and AutoML Methods

Bai, Zhigang; Liu, Qimeng; Liu, Yu

doi:10.1007/s11053-022-10100-4

Cited by 39 publications

(12 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…crop prediction [130][131][132], crop classification [133]), environmental science (e.g. environmental impact assessment [134], waterlogging risk estimation [135], water storage estimation [30], water potential mapping [136], meteorological forecasting [137], ocean behaviour prediction [138]), geology (e.g. oil well placement [139], soil roughness estimation [32], soil moisture estimation [31], landslide risk estimation [140,141]), transportation (e.g.…”

Section: Sdss With Automlmentioning

confidence: 99%

Spatial Decision Support Systems with Automated Machine Learning: A Review

Wen¹,

Li²

2022

Preprint

View full text Add to dashboard Cite

Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness were discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems were also discussed to address these challenges. This paper argues that integrating spatial decision support systems with automated machine learning can not only encourage user adoption, but also mutually benefit research in both fields — bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning.

show abstract

Section: Sdss With Automlmentioning

confidence: 99%

Spatial Decision Support Systems with Automated Machine Learning: A Review

Wen¹,

Li²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The presence and spread of groundwater are influenced by various anthropogenic and natural variables. Therefore, studying potential groundwater resources is essential for their wise development and usage (Bai et al, 2022). Studies on the effects of climate change indicate that there won't be much change in groundwater recharge in the foreseeable future (Crosbie et al, 2013;Taylor et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Groundwater potential zonation methodologies have come a long way with the use of physical methods (like borehole drilling and pumping tests), weighted overlay analysis (Pandey et al, 2014;Saraf & Choudhury, 1998;Sener et al, 2005;Srivastava & Bhattacharya, 2006) and machine learning methods (Bai et al, 2022;Corsini et al, 2009;Kordestani et al, 2019;Kumar et al, 2021;Naghibi et al, 2018;Rahmati et al, 2016;Rizeei et al, 2019;Zabihi et al, 2016). All procedures are based on the data collected by remote sensing, with the exception of physical methods, which are expensive and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Suitable sites for groundwater development: A capture map-based approach integrated with weighted overlay analysis

Bajpai

Kumar

Gaur

et al. 2023

Preprint

View full text Add to dashboard Cite

A novel methodology for suitable site selection for groundwater development based on river capture, pumping cost and groundwater potential has been proposed for better groundwater utilization. River capture and cost map have been generated from a calibrated groundwater model, simulated with forecasted hydrological time series data. The groundwater potential has been calculated with weighted overlay analysis. These three variables have been used to classify the model domain into five zones of groundwater development by K-Means clustering. The area with lower river capture, low cost of pumping and high groundwater potential is found to be the best location for groundwater extraction. The methodology has been applied to the lower Ain river basin, France.

show abstract

“…Machine learning algorithms have the capability to process large datasets and capture intricate relationships among multiple variables, enabling more accurate and efficient predictions (Namous et al, 2021;Garg et al, 2022;Hajaj et al, 2023;Jari et al, 2023). Among the machine learning techniques, ensemble learning, which combines the outputs of multiple models, has shown enhanced performance and improved prediction accuracy compared to individual models (Bai et al, 2022). In recent years, major advancements have been observed in the development, evaluation, and validation of innovative techniques pertaining to artificial intelligence (AI) that leverage machine learning (ML) and deep learning (DL) techniques focused on the domain of GWP mapping (Thanh et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Investigating machine learning and ensemble learning models in groundwater potential mapping in arid region: case study from Tan-Tan water-scarce region, Morocco

Jari,

Bachaoui,

Hajaj

et al. 2023

Front. Water

View full text Add to dashboard Cite

Groundwater resource management in arid regions has a critical importance for sustaining human activities and ecological systems. Accurate mapping of groundwater potential plays a vital role in effective water resource planning. This study investigates the effectiveness of machine learning models, including Random Forest (RF), Adaboost, K-Nearest Neighbors (KNN), and Gaussian Process in groundwater potential mapping (GWPM) in the Tan-Tan arid region, Morocco. Fourteen groundwater conditional factors were considered following multicollinearity test, including topographical, hydrological, climatic, and geological factors. Additionally, point data with 174 sites indicative of groundwater occurrences were incorporated. The groundwater inventory data underwent random partitioning into training and testing datasets at three different ratios: 55/45%, 65/35%, and 75/25%. Ultimately, a comprehensive ranking of the 13 models, encompassing both individual and ensemble models, was determined using the prioritization rank technique. The results revealed that ensemble learning (EL) models, particularly RF and Adaboost (RF-Adaboost), outperformed individual models in groundwater potential mapping. Based on accuracy assessment using the validation dataset, the RF-Adaboost EL results yielded an Area Under the Receiver Operating characteristic Curve (AUROC) and Overall Accuracy (OA) of 94.02 and 94%, respectively. Ensemble models have been effectively applied to integrate 14 factors, capturing their intricate interrelationships, and thereby enhancing the accuracy and robustness of groundwater prediction in the Tan-Tan water-scarce region. Among the natural factors, the current study identified lithology, structural elements (such as faults and tectonic lineaments), and land use as significant contributors to groundwater potential. However, the critical characteristics of the study area showing a coastal position as well as a low background in groundwater prospectivity (low borehole points) are challenging in GWPM. The findings highlight the importance of the significant factors in assessing and managing groundwater resources in arid regions. Moreover, this study makes a contribution to the management of groundwater resources by demonstrating the effectiveness of ensemble learning algorithms in the groundwater potential mapping (GWPM) in arid regions.

show abstract

Groundwater Potential Mapping in Hubei Region of China Using Machine Learning, Ensemble Learning, Deep Learning and AutoML Methods

Cited by 39 publications

References 69 publications

Spatial Decision Support Systems with Automated Machine Learning: A Review

Spatial Decision Support Systems with Automated Machine Learning: A Review

Suitable sites for groundwater development: A capture map-based approach integrated with weighted overlay analysis

Investigating machine learning and ensemble learning models in groundwater potential mapping in arid region: case study from Tan-Tan water-scarce region, Morocco

Contact Info

Product

Resources

About