Evaluation of short-term streamflow prediction methods in Urban river basins

Huang, Xinxing; Li, Yifan; Tian, Zhan; Ye, Qinghua; Ke, Qian; Fan, Dongli; Mao, Ganquan; Chen, Aifang; Liu, Junguo

doi:10.1016/j.pce.2021.103027

Cited by 16 publications

(10 citation statements)

References 59 publications

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“…Using the data from these two sources, a lightweight neural-network model was built and trained to predict the extent of erosion and accretion in the Tongtian River Basin. The lightweight neural-network model randomly selects the training set and the test set according to the ratio of 7:3 [41][42][43].…”

Section: The Lightweight Intelligent Predictive Modelmentioning

confidence: 99%

“…This study refers to related studies of the same order of magnitude for the machinelearning model-building process [41][42][43][44][45]. Given the amount of hydrological data in this study, on the premise of making full use of the implicit function relationship between each input hydrological feature vector and the alluvial area of the Tongtian River, the number of hidden layers of the neural network is set as two to ensure the generalization and robustness of the model.…”

Section: The Lightweight Intelligent Predictive Modelmentioning

confidence: 99%

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Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network

et al. 2022

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The Tongtian River is the source of the Yangtze River and is a national key ecological reserve in China. Monitoring and predicting the changes and mechanisms of the Tongtian River channel morphology are beneficial to protecting the “Asian Water Tower”. This study aims to quantitatively monitor and predict the accretion and erosion area of the Tongtian River channel morphology during the past 30 years (1990–2020). Firstly, the water bodies of the Tongtian River were extracted and the accretion and erosion areas were quantified using 1108 Landsat images based on the combined method of three water-body indices and a threshold, and the surface-water dataset provided by the European Commission Joint Research Centre. Secondly, an intelligent lightweight neural-network model was constructed to predict and analyze the accretion and erosion area of the Tongtian River. Results indicate that the Tongtian River experienced apparent accretion and erosion with a total area of 98.3 and 94.9 km2, respectively, during 1990–2020. The braided (meandering) reaches at the upper (lower) Tongtian River exhibit an overall trend of accretion (erosion). The Tongtian River channel morphology was determined by the synergistic effect of sediment-transport velocity and streamflow. The lightweight neural network well-reproduced the complex nonlinear processes in the river-channel morphology with a final prediction error of 0.0048 km2 for the training session and 4.6 km2 for the test session. Results in this study provide more effective, reasonable, and scientific decision-making aids for monitoring, protecting, understanding, and mining the evolution characteristics of rivers, especially the complex change processes of braided river channels in alpine regions and developing countries.

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Section: The Lightweight Intelligent Predictive Modelmentioning

confidence: 99%

Section: The Lightweight Intelligent Predictive Modelmentioning

confidence: 99%

Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network

et al. 2022

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“…ANN can use previous data to make a reasonably accurate prediction of the modeled parameters. It may be used to simulate any physical occurrence [55], making it suitable for a wide range of hydrological applications, including water demand forecasting [29,44], streamflow forecasting [56,57], drought prediction [58,59], and water quality predictions [60,61]. This research applied the multilayer perceptron (MLP) network (a feed-forward, backpropagation network) to simulate water level.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Hybrid Technique to Improve the River Water Level Forecasting Using Artificial Neural Network‐Based Marine Predators Algorithm

Mohammed

Zubaidi

Al‐Ansari

et al. 2022

Advances in Civil Engineering

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Water level (WL) forecasting has become a difficult undertaking due to spatiotemporal fluctuations in climatic factors and complex physical processes. This paper proposes a novel hybrid machine learning model based on an artificial neural network (ANN) and the Marine Predators algorithm (MPA) for modeling monthly water levels of the Tigris River in Al-Kut, Iraq. Data preprocessing techniques are employed to enhance data quality and determine the optimal input model. Historical data for water level and climatic factors data are utilized from 2011 to 2020 to build and assess the model. MPA-ANN algorithm’s performance is compared with recent constriction coefficient-based particle swarm optimization and chaotic gravitational search algorithm (CPSOCGSA-ANN) and slime mold algorithm (SMA-ANN) to reduce uncertainty and raise the prediction range. The finding demonstrated that singular spectrum analysis is a highly effective method to denoise time series. MPA-ANN outperformed CPSOCGSA-ANN and SMA-ANN algorithms based on different statistical criteria. The suggested novel methodology offers good results with scatter index (SI) = 0.0009 and coefficient of determination (R2 = 0.98).

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“…Due to the uncertainty of rainfall events, there is currently no uniform and clear criteria for classifying rainfall events. Based on recent research considering the effects of rainfall confluence time and rainfall duration, 180 min was used as the minimum time interval between two rainfall events, and the cumulative rainfall of each event had to be greater than 3 mm (Huang et al, 2021). According to the amount of rainfall in 24 h, all rainfall events were divided into six categories: light rain (< 10 mm); moderate rain (10-24.9 mm); heavy rain (25-49.9 mm); torrential rain (50-99.9 mm); rainstorm (100-249.9 mm); and extraordinary rainstorm (> 250 mm).…”

Section: Datamentioning

confidence: 99%

Prediction of River Pollution Under the Rainfall-Runoff Impact by Artificial Neural Network: A Case Study of Shiyan River, Shenzhen, China

Tian

Yang

et al. 2022

Front. Environ. Sci.

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Climate change and rapid urbanization have made it difficult to predict the risk of pollution in cities under different types of rainfall. In this study, a data-driven approach to quantify the effects of rainfall characteristics on river pollution was proposed and applied in a case study of Shiyan River, Shenzhen, China. The results indicate that the most important factor affecting river pollution is the dry period followed by average rainfall intensity, maximum rainfall in 10 min, total amount of rainfall, and initial runoff intensity. In addition, an artificial neural network model was developed to predict the event mean concentration (EMC) of COD in the river based on the correlations between rainfall characteristics and EMC. Compared to under light rain (< 10 mm/day), the predicted EMC was five times lower under heavy rain (25–49.9 mm/day) and two times lower under moderate rain (10–24.9 mm/day). By converting the EMC to chemical oxygen demand in the river, the pollution load under non-point-source runoff was estimated to be 497.6 t/year (with an accuracy of 95.98%) in Shiyan River under typical rainfall characteristics. The results of this study can be used to guide urban rainwater utilization and engineering design in Shenzhen. The findings also provide insights for predicting the risk of rainfall-runoff pollution and developing related policies in other cities.

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Evaluation of short-term streamflow prediction methods in Urban river basins

Cited by 16 publications

References 59 publications

Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network

Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network

Hybrid Technique to Improve the River Water Level Forecasting Using Artificial Neural Network‐Based Marine Predators Algorithm

Prediction of River Pollution Under the Rainfall-Runoff Impact by Artificial Neural Network: A Case Study of Shiyan River, Shenzhen, China

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