Stream Flow Forecasting in Mahanadi River Basin using Artificial Neural Networks

Sahoo, Abinash; Samantaray, Sandeep; Ghose, Dillip K.

doi:10.1016/j.procs.2019.08.154

Cited by 44 publications

(13 citation statements)

References 12 publications

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“…Streamflow data display a complicated nonlinear pattern and are regulated by multiple factors, such as temperature, precipitation, evaporation, and basin physical conditions, among others. Streamflow predictions by employing process-based physical models need a large amount of data, strenuous model building and testing, and many proper parameters while artificial neural networks can create complex nonlinear mapping and can be adapted for detecting subtle changes in the hydroclimatic environment (Figure 4), and it has been widely used in streamflow prediction (e.g., [54][55][56][57]).…”

Section: Methodsmentioning

confidence: 99%

Streamflow Changes in the Headwater Area of Yellow River, NE Qinghai-Tibet Plateau during 1955–2040 and Their Implications

Chang-lei

Jin

et al. 2021

Water

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Human activities have substantially altered present-day flow regimes. The Headwater Area of the Yellow River (HAYR, above Huanghe’yan Hydrological Station, with a catchment area of 21,000 km2 and an areal extent of alpine permafrost at ~86%) on the northeastern Qinghai-Tibet Plateau, Southwest China has been undergoing extensive changes in streamflow regimes and groundwater dynamics, permafrost degradation, and ecological deterioration under a warming climate. In general, hydrological gauges provide reliable flow records over many decades and these data are extremely valuable for assessment of changing rates and trends of streamflow. In 1998–2003, the damming of the Yellow River by the First Hydropower Station of the HAYR complicated the examination of the relations between hydroclimatic variables and streamflow dynamics. In this study, the monthly streamflow rate of the Yellow River at Huanghe’yan is reconstructed for the period of 1955–2019 using the double mass curve method, and then the streamflow at Huagnhe’yan is forecasted for the next 20 years (2020–2040) using the Elman neural network time-series method. The dam construction (1998–2000) has caused a reduction of annual streamflow by 53.5–68.4%, and a more substantial reduction of 71.8–94.4% in the drier years (2003–2005), in the HAYR. The recent removal of the First Hydropower Station of the HAYR dam (September 2018) has boosted annual streamflow by 123–210% (2018–2019). Post-correction trends of annual maximum (QMax) and minimum (QMin) streamflow rates and the ratio of the QMax/QMin of the Yellow River in the HAYR (0.18 and 0.03 m3·s−1·yr−1 and −0.04 yr−1, respectively), in comparison with those of precorrection values (−0.11 and −0.004 m3·s−1·yr−1 and 0.001 yr−1, respectively), have more truthfully revealed a relatively large hydrological impact of degrading permafrost. Based on the Elman neural network model predictions, over the next 20 years, the increasing trend of flow in the HAYR would generally accelerate at a rate of 0.42 m3·s−1·yr−1. Rising rates of spring (0.57 m3·s−1·yr−1) and autumn (0.18 m3·s−1·yr−1) discharge would see the benefits from an earlier snow-melt season and delayed arrival of winter conditions. This suggests a longer growing season, which indicates ameliorating phonology, soil nutrient availability, and hydrothermal environments for vegetation in the HAYR. These trends for hydrological and ecological changes in the HAYR may potentially improve ecological safety and water supplies security in the HAYR and downstream Yellow River basins.

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Section: Methodsmentioning

confidence: 99%

Streamflow Changes in the Headwater Area of Yellow River, NE Qinghai-Tibet Plateau during 1955–2040 and Their Implications

Chang-lei

Jin

et al. 2021

Water

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“…2). The ANN has been widely used in streamflow prediction (Dolling and Varas, 2002;Sahoo et al, 2019;Sequoira and Luna, 2019).…”

Section: Methodsmentioning

confidence: 99%

Changes in flow regimes of the Yellow River in the Headwater Area of the Yellow River on the northeastern Qinghai-Tibet Plateau, SW China during 1955-2040

Jin

Liang

et al. 2020

Preprint

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Human disturbance has substantially altered real-time flow regimes. The Headwater Area of the Yellow River (HAYR, above Huanghe'yan Hydrological Station) on the northeastern Qinghai-Tibet Plateau, Southwest China has been undergoing extensively streamflow changes, permafrost degradation and ecological deterioration under a warming climate. However, the damming of the Yellow River complicates examining the relations between hydroclimatic variables and streamflow dynamics. In this study, monthly streamflow of the Yellow River (YR) at the Huanghe'yan Hydrological Station is reconstructed for 1955-2019 dusing

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“…In contrast, deep learning models are widely applied in different tasks, including processing, analyzing, designing, estimating, filtering, and detection tasks [42]. e popular deep learning models applied in different fields of studies are Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Radial Basis Function Networks (RBFN), and Generative Adversarial Network (GAN) [21,25,[43][44][45][46]. e time series analysis models used in this study were specifically chosen, and they are briefly discussed in detail in the following sections.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Short-Term Daily Univariate Streamflow Forecasting Using Deep Learning Models

Wegayehu

Muluneh

2022

Advances in Meteorology

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Hydrological forecasting is one of the key research areas in hydrology. Innovative forecasting tools will reform water resources management systems, flood early warning mechanisms, and agricultural and hydropower management schemes. Hence, in this study, we compared Stacked Long Short-Term Memory (S-LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Unit (GRU) with the classical Multilayer Perceptron (MLP) network for one-step daily streamflow forecasting. The analysis used daily time series data collected from Borkena (in Awash river basin) and Gummera (in Abay river basin) streamflow stations. All data sets passed through rigorous quality control processes, and null values were filled using linear interpolation. A partial autocorrelation was also applied to select the appropriate time lag for input series generation. Then, the data is split into training and testing datasets using a ratio of 80 : 20, respectively. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2) were used to evaluate the performance of the proposed models. Finally, the findings are summarized in model variability, lag time variability, and time series characteristic themes. As a result, time series characteristics (climatic variability) had a more significant impact on streamflow forecasting performance than input lagged time steps and deep learning model architecture variations. Thus, Borkena’s river catchment forecasting result is more accurate than Gummera’s catchment forecasting result, with RMSE, MAE, MAPE, and R2 values ranging between (0.81 to 1.53, 0.29 to 0.96, 0.16 to 1.72, 0.96 to 0.99) and (17.43 to 17.99, 7.76 to 10.54, 0.16 to 1.03, 0.89 to 0.90) for both catchments, respectively. Although the performance is dependent on lag time variations, MLP and GRU outperform S-LSTM and Bi-LSTM on a nearly equal basis.

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Stream Flow Forecasting in Mahanadi River Basin using Artificial Neural Networks

Cited by 44 publications

References 12 publications

Streamflow Changes in the Headwater Area of Yellow River, NE Qinghai-Tibet Plateau during 1955–2040 and Their Implications

Streamflow Changes in the Headwater Area of Yellow River, NE Qinghai-Tibet Plateau during 1955–2040 and Their Implications

Changes in flow regimes of the Yellow River in the Headwater Area of the Yellow River on the northeastern Qinghai-Tibet Plateau, SW China during 1955-2040

Short-Term Daily Univariate Streamflow Forecasting Using Deep Learning Models

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