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Related Concept Videos

Typical Model Studies01:30

Typical Model Studies

350
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.
350
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

41
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...
41
Rapidly Varying Flow01:24

Rapidly Varying Flow

56
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
56
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

148
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.
148
Modeling and Similitude01:12

Modeling and Similitude

257
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...
257
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

195
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
195
  1. Home
  3. Research Domains
  5. Environmental Sciences
  7. Pollution And Contamination
  9. Groundwater Quality Processes And Contaminated Land Assessment
  11. Machine Learning Based Water Quality Evolution And Pollution Identification In Reservoir Type Rivers.
  1. Home
  3. Research Domains
  5. Environmental Sciences
  7. Pollution And Contamination
  9. Groundwater Quality Processes And Contaminated Land Assessment
  11. Machine Learning Based Water Quality Evolution And Pollution Identification In Reservoir Type Rivers.

Related Experiment Video

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

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Machine learning based water quality evolution and pollution identification in reservoir type rivers.

Rui Deng1, Tianci Zhu2, Weilin Zhou3

  • 1School of Smart City, Chongqing Jiaotong University, No. 66 Xuefu Road, Nan'an District, Chongqing, 400074, China; Technology Innovation Center for Spatio-temporal Information and Equipment of Intelligent City, Ministry of Natural Resources, No. 6 Qingzhu East Road, Yubei District, Chongqing, 400021, China.

Environmental Pollution (Barking, Essex : 1987)
|June 14, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Reservoir regulation significantly impacts river water quality, increasing chlorophyll-a while moderately affecting total nitrogen. Machine learning, particularly XGBoost, accurately quantifies these pollutant dynamics for effective ecosystem management.

Keywords:
Machine learningPollutant inversion modelingRemote sensingReservoir-type rivers

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Watershed Planning within a Quantitative Scenario Analysis Framework
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Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Remote Sensing

Background:

  • Quantifying pollutant transport and transformation in reservoir-regulated rivers is challenging due to complex hydrodynamic and biogeochemical interactions.
  • Understanding these dynamics is crucial for managing water quality in vital river systems like the Yulin River, a tributary of the Three Gorges Reservoir.

Purpose of the Study:

  • To explore the spatiotemporal dynamics of water quality parameters (WQPs) in the Yulin River using field monitoring and satellite imagery.
  • To evaluate the performance of advanced machine learning algorithms (XGBoost, RF, CatBoost, GBDT) for retrieving WQPs.
  • To identify the primary drivers influencing pollutant dynamics in the reservoir-regulated river system.

Main Methods:

  • Combined 48 months of high-frequency field monitoring data (Jan 2020-Dec 2023) with Sentinel-2 multispectral imagery.
Water quality evolution
  • Systematically evaluated four machine learning algorithms (XGBoost, RF, CatBoost, GBDT) for retrieving chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), and chlorophyll-a (Chla).
  • Analyzed the influence of hydrological regulation and meteorological factors on WQPs.

    • Main Results:

    • XGBoost demonstrated superior performance in WQP retrieval, with R² values from 0.9154 to 0.9488 and low RMSE (0.0267-1.7351 mg/L).
    • Reservoir impoundment significantly increased chlorophyll-a (100%-1000% in 56.2% of the area) but had minor effects on TN (≤40% in 73% of the area).
    • Hydrological conditions strongly influenced COD and Chla in estuarine regions, with elevated levels during low-flow periods; meteorological factors showed weak correlations.

    Conclusions:

    • XGBoost is a robust tool for large-scale water quality parameter retrieval in reservoir-regulated river systems.
    • Hydrological regulation is the dominant factor controlling pollutant dynamics, particularly chlorophyll-a surges post-impoundment.
    • The developed XGBoost model provides high-precision, watershed-scale monitoring capabilities, offering valuable insights for river ecosystem management.