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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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使用机器学习模型进行地下水位预测:一篇综述

Kenneth Beng Wee Boo1, Ahmed El-Shafie2, Faridah Othman1

  • 1Department of Civil Engineering, Faculty of Engineering, Universiti Malaya (UM), 50603 Kuala Lumpur, Malaysia.

Water research
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概括

准确地预测地下水位 (GWL) 对于管理枯竭的淡水资源至关重要. 本综述分析了机器学习 (ML) 模型,突出了有效预测GWL的最佳实践.

关键词:
人工智能的人工智能是人工智能.地下水位建模的地下水位建模水文学的水文学机器学习 机器学习水资源工程 水资源工程水平面预测预测

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科学领域:

  • 水文学的水文学
  • 环境科学 环境科学
  • 数据科学数据科学数据科学

背景情况:

  • 地下水是主要的淡水来源,但在全球范围内面临枯竭.
  • 准确地预测地下水位 (GWL) 对于可持续的水资源管理至关重要.
  • 机器学习 (ML) 显示了改善GWL预测准确性的前景.

研究的目的:

  • 在GWL预测中全面审查ML应用.
  • 确定最近研究 (2017-2023) 中使用的关键ML模型和建模概念.
  • 为最佳GWL预测提供见解和最佳实践.

主要方法:

  • 来自Web of Science (2017-2023) 的142篇文章的系统文献综述.
  • 专注于著名的ML模型:ANN,ANFIS,SVR,EC,DL,EN和HM. 这些模型是:
  • 对关键建模因素的分析:数据集大小,数据分割,输入选择,时间步骤,性能指标,研究区和含水层.

主要成果:

  • 机器学习模型显示了准确GWL预测的巨大潜力.
  • 经常使用人工神经网络 (ANN) 和深度学习 (DL) 等特定模型.
  • 数据处理和模型选择的最佳实践对于最佳性能至关重要.

结论:

  • 本综述综合了GWL预测中的当前ML趋势.
  • 它为研究人员和水资源管理机构提供建议.
  • 有效的ML实施是解决地下水枯竭挑战的关键.