Prediction of Total Nitrogen and Phosphorus in Surface Water by Deep Learning Methods Based on Multi-Scale Feature Extraction

He, M.; Wu, Shujuan; Huang, Binbin; Kang, Chuanxiong; Gui, Faliang

doi:10.3390/w14101643

Cited by 14 publications

(6 citation statements)

References 76 publications

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“…where k is a conversion factor equal to 1 for metric units, S 0 represents the reach slope, and R h is the hydraulic radius, equivalent to cross-sectional area divided by perimeter (Harmsen, 1955). For wide river channels, the effect of the channel sides is negligible, so depth can be used instead of hydraulic radius.…”

Section: Parameter Values and Variable Channel Dimensionsmentioning

confidence: 99%

Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning

Bindas,

Tsai,

Liu

et al. 2024

Water Resources Research

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Recently, rainfall‐runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics‐NN models—particularly, a genre called differentiable modeling that intermingles NNs with physics to learn relationships between variables. However, hydrologic routing simulations, necessary for simulating floods in stem rivers downstream of large heterogeneous basins, had not yet benefited from these advances and it was unclear if the routing process could be improved via coupled NNs. We present a novel differentiable routing method (δMC‐Juniata‐hydroDL2) that mimics the classical Muskingum‐Cunge routing model over a river network but embeds an NN to infer parameterizations for Manning's roughness (n) and channel geometries from raw reach‐scale attributes like catchment areas and sinuosity. The NN was trained solely on downstream hydrographs. Synthetic experiments show that while the channel geometry parameter was unidentifiable, n can be identified with moderate precision. With real‐world data, the trained differentiable routing model produced more accurate long‐term routing results for both the training gage and untrained inner gages for larger subbasins (>2,000 km2) than either a machine learning model assuming homogeneity, or simply using the sum of runoff from subbasins. The n parameterization trained on short periods gave high performance in other periods, despite significant errors in runoff inputs. The learned n pattern was consistent with literature expectations, demonstrating the framework's potential for knowledge discovery, but the absolute values can vary depending on training periods. The trained n parameterization can be coupled with traditional models to improve national‐scale hydrologic flood simulations.

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Section: Parameter Values and Variable Channel Dimensionsmentioning

confidence: 99%

Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning

Bindas,

Tsai,

Liu

et al. 2024

Water Resources Research

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“…f g represents the current global best fitness value. f w represents the current global worst fitness value [35]. K is a random value for sparrow movement direction, where k∈[−1, 1].…”

Section: Learning Rate Optimization Based On Sparrow Search Algorithmmentioning

confidence: 99%

Multi-Altitude Corn Tassel Detection and Counting Based on UAV RGB Imagery and Deep Learning

Niu,

Nie,

et al. 2024

Drones

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In the context of rapidly advancing agricultural technology, precise and efficient methods for crop detection and counting play a crucial role in enhancing productivity and efficiency in crop management. Monitoring corn tassels is key to assessing plant characteristics, tracking plant health, predicting yield, and addressing issues such as pests, diseases, and nutrient deficiencies promptly. This ultimately ensures robust and high-yielding corn growth. This study introduces a method for the recognition and counting of corn tassels, using RGB imagery captured by unmanned aerial vehicles (UAVs) and the YOLOv8 model. The model incorporates the Pconv local convolution module, enabling a lightweight design and rapid detection speed. The ACmix module is added to the backbone section to improve feature extraction capabilities for corn tassels. Moreover, the CTAM module is integrated into the neck section to enhance semantic information exchange between channels, allowing for precise and efficient positioning of corn tassels. To optimize the learning rate strategy, the sparrow search algorithm (SSA) is utilized. Significant improvements in recognition accuracy, detection efficiency, and robustness are observed across various UAV flight altitudes. Experimental results show that, compared to the original YOLOv8 model, the proposed model exhibits an increase in accuracy of 3.27 percentage points to 97.59% and an increase in recall of 2.85 percentage points to 94.40% at a height of 5 m. Furthermore, the model optimizes frames per second (FPS), parameters (params), and GFLOPs (giga floating point operations per second) by 7.12%, 11.5%, and 8.94%, respectively, achieving values of 40.62 FPS, 14.62 MB, and 11.21 GFLOPs. At heights of 10, 15, and 20 m, the model maintains stable accuracies of 90.36%, 88.34%, and 84.32%, respectively. This study offers technical support for the automated detection of corn tassels, advancing the intelligence and precision of agricultural production and significantly contributing to the development of modern agricultural technology.

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“…Total nitrogen (TN) and total phosphorus (TP) stand as primary nutrients instigating water body eutrophication [50]. Phosphate and nitrate emerge as critical limiting nutrients, fostering eutrophication in freshwater systems [50].…”

Section: Total Nitrogen/total Phosphorus Ratiomentioning

confidence: 99%

“…Sample point The influx of phosphorus (P) and nitrogen (N) into freshwater systems can trigger the overgrowth of aquatic plants, oxygen depletion, and deleterious alterations in aquatic fauna diversity [49]. Total nitrogen (TN) and total phosphorus (TP) stand as primary nutrients instigating water body eutrophication [50]. Phosphate and nitrate emerge as critical limiting nutrients, fostering eutrophication in freshwater systems [50].…”

Section: Chemical Oxygen Demand (Cod) and Total Dissolved Solids (Tdss)mentioning

confidence: 99%

Impact of the Citrus Industry on the Water Quality of the Filobobos River in Veracruz, Mexico

Sandoval Herazo,

González-Moreno,

Carreto-Hernandez

et al. 2024

Sustainability

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Veracruz, Mexico, boasts abundant water resources. However, the region is facing challenges stemming from population growth and industrial expansion, leading to a notable increase in wastewater discharge into surface water bodies. This discharge has resulted in significant alterations to water quality. In this study, we analyzed the water quality in the Bobos River, Veracruz, with a focus on sustainability. A total of 12 samples were collected from various points along the reservoir. Parameters including temperature, pH, dissolved oxygen (DO), conductivity, total nitrogen (TN), nitrate, chemical oxygen demand (COD), total phosphorus (TP), phosphate, dissolved solids (DSs), and fecal coliforms (FCs) were measured and analyzed. The pH levels ranged from slightly acidic (6.40 ± 0.71) to slightly alkaline (8.65 ± 0.07), with the lowest dissolved oxygen concentration recorded at 0.15 ± 0.07 mg L−1. Conductivity varied between 0.26 and 3.81 mS. Total nitrogen concentrations ranged from 0.21 ± 3.8 to 0.491 ± 1.3 mg L−1. Dissolved solid concentrations were measured at 0.39 ± 0.05 mg L−1, while the final sampling point exhibited elevated values for temperature, nitrate, chemical oxygen demand, total phosphorus, and coliforms (30.35 ± 0.21 °C, 0.35, 389.00 ± 11.31, 13.20 ± 0.85 mg L−1, and 3.2 × 105 ± 3.707107 × 104, respectively). To address these concerning trends and safeguard public health and environmental integrity, the implementation of continuous monitoring and stringent control measures is imperative.

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Prediction of Total Nitrogen and Phosphorus in Surface Water by Deep Learning Methods Based on Multi-Scale Feature Extraction

Cited by 14 publications

References 76 publications

Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning

Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning

Multi-Altitude Corn Tassel Detection and Counting Based on UAV RGB Imagery and Deep Learning

Impact of the Citrus Industry on the Water Quality of the Filobobos River in Veracruz, Mexico

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