A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction

Sahoo, Bibhuti Bhusan; Sankalp, Sovan; Kisi, Özgür

doi:10.1007/s11269-023-03552-7

Cited by 12 publications

(1 citation statement)

References 77 publications

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“…ML model development involves several steps, such as data pre-processing, internal parameter tuning, and input feature optimization, and several advancements have been made in the relevant domains 9 , 22 – 24 . Notably, the focus of this study is deep learning (DL) models, as a recently developed subset of ML models 25 , 26 , and their integration with feature input optimization algorithms 27 to establish a hybrid ML model for river EC prediction.…”

Section: Introductionmentioning

confidence: 99%

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm

Karbasi,

Ali,

Bateni

et al. 2024

Sci Rep

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Electrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In the current research, the EC of two Australian rivers (Albert River and Barratta Creek) was forecasted for up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with Long Short-Term Memory Model, CNN-LSTM). The Boruta-XGBoost feature selection method was used to determine the significant inputs (time series lagged data) to the model. To compare the performance of Boruta-XGB-CNN-LSTM models, three machine learning approaches—multi-layer perceptron neural network (MLP), K-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) were used. Different statistical metrics, such as correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error, were used to assess the models' performance. From 10 years of data in both rivers, 7 years (2012–2018) were used as a training set, and 3 years (2019–2021) were used for testing the models. Application of the Boruta-XGB-CNN-LSTM model in forecasting one day ahead of EC showed that in both stations, Boruta-XGB-CNN-LSTM can forecast the EC parameter better than other machine learning models for the test dataset (R = 0.9429, RMSE = 45.6896, MAPE = 5.9749 for Albert River, and R = 0.9215, RMSE = 43.8315, MAPE = 7.6029 for Barratta Creek). Considering the better performance of the Boruta-XGB-CNN-LSTM model in both rivers, this model was used to forecast 3–10 days ahead of EC. The results showed that the Boruta-XGB-CNN-LSTM model is very capable of forecasting the EC for the next 10 days. The results showed that by increasing the forecasting horizon from 3 to 10 days, the performance of the Boruta-XGB-CNN-LSTM model slightly decreased. The results of this study show that the Boruta-XGB-CNN-LSTM model can be used as a good soft computing method for accurately predicting how the EC will change in rivers.

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Section: Introductionmentioning

confidence: 99%

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm

Karbasi,

Ali,

Bateni

et al. 2024

Sci Rep

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A novel hybrid approach based on outlier and error correction methods to predict river discharge using meteorological variables

Shabbir,

Chand,

Iqbal

2024

Environ Ecol Stat

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Suspended Load Modeling of River Using Soft Computing Techniques

Moradinejad

2024

Water Resour Manage

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The phenomenon of sediment transport has always affected many river and civil structures. Not knowing the exact amount causes a lot of damage. Therefore, it is very important to correctly estimate the sediment load of rivers in terms of sedimentation, erosion, and ood control. In this research, adaptive neuro-fuzzy models (ANFIS), gene expression programming (GEP), support vector regression (SVR), Group Method of Data Handling (GMDH), and the classical method of sediment rating curve (SRC) were used to model and prediction. For this purpose, the daily data of temperature, rainfall, sediment, and discharge of the Jalair station located in the Markazi province of Iran were used. The results obtained from these ve methods were compared with each other and with the measured data. To evaluate the used methods, correlation coe cient, root mean square error, mean absolute error, and Taylor diagram were used. The results show the acceptable performance of data mining methods compared to the Sediment rating curve. Also, the superiority of the model (GEP) was shown with the highest coe cient of determination R 2 with a value of 0.98 and the lowest root mean square error RMSE in terms of tons per day with a value of 3721. The e ciency of the ANFIS and GMDH model with R 2 values of 0.93, 0.98, and RMSE values of 16556, and 18638 was somewhat better than the SVR model with an R 2 value of 0.90 and RMSE value of 35158. Data mining-based methods can be used as an alternative to estimate the suspended load of the river.

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A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction

Cited by 12 publications

References 77 publications

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm

A novel hybrid approach based on outlier and error correction methods to predict river discharge using meteorological variables

Suspended Load Modeling of River Using Soft Computing Techniques

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