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Citing#

We kindly request that any scientific publications utilizing WATex for analysis, research, or case studies cite the following reference. A prepared BibTeX entry for your convenience is provided below:

@article{Kouadio_Liu_Liu_2023,
    title={watex: machine learning research in water exploration},
    DOI={https://doi.org/10.1016/j.softx.2023.101367},
    journal={SoftwareX},
    author={Kouadio, Kouao Laurent and Liu, Jianxin and Liu, Rong},
    year={2023},
    volume={101367},
    pages={1-7}
}

Additional case studies and applications involving WATex can enrich your understanding and application of machine learning in water exploration and more. These real-world applications highlight the versatility and effectiveness of WATex in field conditions. We invite you to explore these resources for a deeper dive into the practical use of WATex in geophysical exploration, environmental tasks, and more.

Flow rate predictions#

Moreover, watex has been utilized in impactful real-world case study papers. For instance, it facilitated flow rate prediction in the Bagoue region by applying support vector machines (SVMs), offering comprehensive explanations about DC-resistivity parameter definitions (watex.datasets.load_bagoue()). Another significant application involved ensemble learning paradigms to enhance flow rate prediction accuracy in areas facing severe water shortages. These studies underscore the practical benefits and effectiveness of WATex in addressing geophysical and water exploration challenges. Below are the ready-made BibTeX entries for these published papers:

@article{Kouadio2022,
    author = {Kouadio, Kouao Laurent and Loukou, Nicolas Kouame and Coulibaly, Drissa and Mi, Binbin and Kouamelan, Serge Kouamelan and Gnoleba, Serge Pac{\^{o}}me D{\'{e}}guine and Zhang, Hongyu and XIA, Jianghai},
    doi = {10.1029/2021WR031623},
    journal = {Water Resources Research},
    pages = {1--33},
    title = {{Groundwater Flow Rate Prediction from Geo‐Electrical Features using Support Vector Machines}},
    year = {2022}
}

@article{Kouadio_Liu_Kouamelan_Liu_2023,
    author = {Kouadio, Kouao Laurent and Liu, Jianxin and Kouamelan, Serge Kouamelan and Liu, Rong},
    title = {Ensemble Learning Paradigms for Flow Rate Prediction Boosting},
    journal = {Water Resources Management},
    volume = {37},
    number = {11},
    pages = {4413--4431},
    year = {2023},
    doi = {10.1007/s11269-023-03562-5},
    abstract = {In response to the issue of water scarcity in recent years, international organizations, in collaboration with many governments, have initiated several drinking water supply projects carried out by geophysical and drilling companies. Unfortunately, despite the reliability of electrical resistivity profiling (ERP) and vertical electrical sounding (VES) methods, the substantial financial losses incurred due to numerous unsuccessful drillings are owing to the difficulty to emphasize the drilling location properly. Therefore, we proposed the ensemble machine learning (EML) paradigms to predict the flow rate (FR) with an optimal score before any drilling operations. The approach was experimented in a region with severe water shortages. Thus, geo-electrical features from the ERP and VES were defined and coupled with borehole data to create the binary dataset for unproductive and productive boreholes respectively. Then, the dataset is state-of-art transformed before feeding to the EML algorithms. The model performance and generalization capability were evaluated using the Matthews correlation, the accuracy, the confusion matrix, the binary predictor error, the precision-recall, and the cumulative gain plot. As a result, the benchmark, pasting, extreme gradient boosting, and stacking paradigms have built a powerful range of FR prediction scores between 90 ~ 96%. Henceforth, the robust EML paradigms can be used to identify the best location for drilling operations, lowering the repercussion of unsuccessful drillings.},
    URL = {https://doi.org/10.1007/s11269-023-03562-5}
}

Land subsidence simulation and risk prevention#

In addition to facilitating groundwater exploration, WATex has proven its versatility across a variety of geophysical and environmental challenges. A recent case study demonstrates WATex’s application in the realm of urban planning and infrastructure management, focusing on the simulation of land subsidence using advanced machine learning techniques. This research leverages the power of eXtreme Gradient Boosting Regressor (XGBR) and Long Short-Term Memory (LSTM) models, highlighting the critical roles of groundwater level and building concentration in land subsidence scenarios. By accurately predicting subsidence, this study expands the potential uses of WATex into environmental risk assessment and impact modeling. Furthermore, it emphasizes the software’s capacity to contribute valuable insights for policy making, aimed at fostering sustainable urban development strategies.

The following references highlight the impactful real-world applications of WATex in addressing complex geophysical and environmental issues:

@article{LIU2024120078,
    title = {Machine learning-based techniques for land subsidence simulation in an urban area},
    journal = {Journal of Environmental Management},
    volume = {352},
    pages = {120078},
    year = {2024},
    issn = {0301-4797},
    doi = {https://doi.org/10.1016/j.jenvman.2024.120078},
    url = {https://www.sciencedirect.com/science/article/pii/S0301479724000641},
    author = {Jianxin Liu and Wenxiang Liu and Fabrice Blanchard Allechy and Zhiwen Zheng and Rong Liu and Kouao Laurent Kouadio},
    keywords = {Land subsidence, Machine learning, Environmental risk assessment, Groundwater impact modeling}
}

Moreover, watex are used in real-world case study papers. For instance, it was used for the flow rate prediction in Bagoue region applying the support vector-machines (SVMs) and provides complete details explanations about the DC-resistivity parameter definitions (watex.datasets.load_bagoue()). Here is a ready-made BibTeX entry of the published papers:

Acknowledgment#

It is important to recognize the synergistic relationship between WATex and foundational machine learning libraries that enhance its capabilities. Specifically, the extensive use of scikit-learn for various machine learning tasks within WATex merits acknowledgment. Therefore, in addition to citing WATex in your scholarly work, it is also highly recommended to cite scikit-learn to appreciate the comprehensive ecosystem of tools that contribute to the effectiveness of WATex in geophysical exploration and environmental studies.

For convenient referencing, here is the BibTeX entry for scikit-learn, as recommended in their citation guidelines:

@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}

Here is the link to the scikit-learn citation guidelines for more details: scikit-learn.

This acknowledgment not only enriches the scholarly rigor of your citations but also highlights the collaborative nature of open-source software development in advancing scientific research and practical applications in the field of machine learning and geophysics.

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