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Related Concept Videos

Modeling and Similitude01:12

Modeling and Similitude

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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Typical Model Studies01:30

Typical Model Studies

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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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Levels of Use of a GIS01:29

Levels of Use of a GIS

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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: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

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Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
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Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

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In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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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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Watershed Planning within a Quantitative Scenario Analysis Framework
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Artificial Intelligence Modeling for Groundwater Environments across Spatial Scales.

Yiran Chen1, Hui Li1, Zi Zhan1

  • 1School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China.

Environmental Science & Technology
|September 30, 2025
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Artificial intelligence (AI) offers powerful tools for modeling groundwater systems, addressing risks like depletion and contamination across various scales. Further research is needed to enhance AI

Keywords:
cross-scale applicationsdata scarcitymodel explainabilitymodel uncertaintymodeling

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Area of Science:

  • Hydrogeology
  • Environmental Science
  • Artificial Intelligence

Background:

  • Groundwater resources face significant threats from climate change and human activities, leading to depletion and contamination.
  • Accurate modeling is crucial for understanding and predicting groundwater system responses to these changing conditions.
  • Artificial intelligence (AI) is increasingly recognized for its potential in various groundwater applications.

Purpose of the Study:

  • To review and evaluate the capabilities of AI in modeling groundwater flow and transport problems across diverse scales.
  • To assess AI's application in identifying contamination sources, optimizing remediation, and predicting groundwater levels and quality.
  • To explore AI's potential in cross-scale modeling and uncertainty quantification for enhanced reliability.

Main Methods:

  • Comprehensive literature review of AI applications in groundwater modeling.
  • Analysis of AI's use in site-specific (contamination, remediation) and regional/global scale predictions (levels, quality, risk assessment).
  • Examination of AI's role in upscaling/downscaling parameters and predictions, and uncertainty quantification techniques.

Main Results:

  • AI has shown significant promise in identifying contamination sources and optimizing remediation strategies at local scales.
  • AI effectively predicts groundwater levels and quality, and conducts risk assessments from regional to global scales.
  • AI demonstrates potential in cross-scale modeling, with initial success in parameter upscaling and prediction downscaling, and uncertainty quantification improves reliability.

Conclusions:

  • AI is a valuable tool for groundwater modeling, offering solutions for flow, transport, contamination, and risk assessment across scales.
  • Challenges remain in maintaining physics-consistent explainability at larger scales and addressing multisource uncertainties.
  • Future opportunities lie in developing robust evaluation systems for uncertainties, improving data accessibility, and integrating AI with physical constraints for enhanced model explainability.