用于预测突尼斯中部切比卡区水质参数的各种机器学习模型的性能比较

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Mohamed Abdelhedi, Hakim Gabtni
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引用次数: 0

摘要

这项开创性的研究率先应用最先进的机器学习算法来预测关键的水参数,特别是 pH 值、水深和盐度。研究结果明确显示了 MLP Regressor 和 XGB Regressor 的卓越性能,它们在预测 pH 值方面始终优于其他模型,R² 值显著,误差极小。MLP 回归模型在水深预测方面表现出色,而 XGB 回归模型则在准确预测盐度方面遥遥领先。这项研究的一个显著特点在于它采用了创新方法,将地理定位数据(经度、纬度和海拔高度)作为所有模型的专用输入。这种战略性的整合展示了算法前所未有的能力,即仅根据地理坐标就能预测水参数,凸显了机器学习在彻底改变水资源管理方面的变革潜力。这项研究为应用机器学习算法预测关键水参数提供了宝贵的见解,使自己站在了科学贡献的最前沿,为水资源的可持续利用树立了新的卓越标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Performance comparison of various machine learning models for predicting water quality parameters in the Chebika Zone of Central Tunisia

Performance comparison of various machine learning models for predicting water quality parameters in the Chebika Zone of Central Tunisia

This groundbreaking study pioneers the application of state-of-the-art machine learning algorithms to predict pivotal water parameters, specifically pH, water depth, and salinity. Rigorously evaluating four leading algorithms (Random Forest Regressor, MLP Regressor, Support Vector Machine, and XGB Regressor) leveraging a substantial dataset and employing comprehensive metrics, including R², MSE, MAE, and cross-validation scores.

Results unequivocally demonstrate the exceptional performance of MLP Regressor and XGB Regressor, consistently outclassing other models in predicting pH, with remarkable R² values and minimal errors. MLP Regressor excels as the preeminent model for water depth prediction, while XGB Regressor leads in accurately predicting salinity. The study underscores the paramount importance of cross-validation in meticulously assessing model robustness and generalization capabilities.

A distinctive feature of this research lies in its innovative approach, incorporating geographic localization data (longitude, latitude, and altitude) as exclusive inputs for all models. This strategic integration showcases the algorithms' unprecedented ability to predict water parameters based solely on geographical coordinates, underscoring the transformative potential of machine learning in revolutionizing water resource management.

The implications extend far beyond its immediate focus, encompassing critical areas such as geophysical exploration, environmental monitoring, water quality management, and ecological conservation. By providing invaluable insights into the application of machine learning algorithms for predicting key water parameters, this study positions itself at the forefront of scientific contributions, setting a new standard for excellence in sustainable water resource utilization.

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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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