Assess the Bottom Sediment Quality of the Saigon River and its Tributaries in Binh Duong Using an Artificial Neural Network

Assessment of bottom sediment quality is vital in water resources management. The study collected sediment data at five monitoring stations in the Saigon River and its tributaries from 2013 to 2023. These monitoring data was used to construct our feedforward neutral network (FFNN) model. The model’s accuracy is 95.5% in assessing sediment quality. The study also presents a speedy method for evaluating the quality of bottom sediments in Vietnam.

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Publication Details

Journal
VNU Journal of Science Earth and Environmental Sciences
Published
2025-07-30
DOI
https://doi.org/10.25073/2588-1094/vnuees.5232
Primary Topic
Hydrological Forecasting Using AI
Type
article
Field-Weighted Citation Impact
0.00
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article

Assess the Bottom Sediment Quality of the Saigon River and its Tributaries in Binh Duong Using an Artificial Neural Network

Thanh N. Nguyen, Tran Thi Anh Thu, Le Trong Dieu Hien
VNU Journal of Science Earth and Environmental Sciences
Hydrological Forecasting Using AI
article

Assess the Bottom Sediment Quality of the Saigon River and its Tributaries in Binh Duong Using an Artificial Neural Network

Thanh N. Nguyen, Tran Thi Anh Thu, Le Trong Dieu Hien
article en

Abstract

Assessment of bottom sediment quality is vital in water resources management. The study collected sediment data at five monitoring stations in the Saigon River and its tributaries from 2013 to 2023. These monitoring data was used to construct our feedforward neutral network (FFNN) model. The model’s accuracy is 95.5% in assessing sediment quality. The study also presents a speedy method for evaluating the quality of bottom sediments in Vietnam.

VNU Journal of Science Earth and Environmental Sciences
Openalex Percentile: Top 83%
Hydrological Forecasting Using AI
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