Long-term multi-step ahead forecasting of root zone soil moisture in different climates: Novel ensemble-based complementary data-intelligent paradigms

Jamei, Mehdi; Karbasi, Masoud; Malik, Anurag; Jamei, Mozhdeh; Kişi, Özgür; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1016/j.agwat.2022.107679

Cited by 25 publications

(4 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, approximately 60–80% of the dataset is used for training the models, and the rest is used for validation. To this end, cross-validation strategies such as k-fold cross-validation 58 , holdout, and walking-forward 59 approaches are promising to avoid overfitting. In this study, the holdout strategy was used, with 70% and 30% of the dataset used for training and testing, respectively.…”

Section: Methodsmentioning

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Generally, approximately 60-80% of the dataset is used for training the models, and the rest is used for validation. To this end, cross-validation strategies such as k-fold cross-validation (Jamei et al 2022c), holdout, and walking-forward (Gao et al 2021) approaches are promising to avoid overfitting. In this study, the holdout strategy was used, with 70% and 30% of the dataset used for training and testing, respectively.…”

Section: Lstm Layermentioning

confidence: 99%

Multistep 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. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Electrical conductivity (EC) is a key water quality metric for predicting the salinity and mineralization. In this study, the 10-day-ahead EC of two Australian rivers, Albert River and Barratta Creek, was forecasted using a novel deep learning algorithm, i.e., the convolutional neural network combined with long short-term memory (CNN-LSTM) model. The Boruta-extreme gradient boosting (XGBoost, XGB) feature selection method was used to determine the significant inputs (time series lagged data) for the model. The performance of the proposed Boruta-XGB-CNN-LSTM model was compared with those of three machine learning approaches: multi-layer perceptron neural network (MLP), K-nearest neighbor (KNN), and XGBoost, considering different statistical metrics such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error (MAPE). Ten years of data for both rivers were extracted, with data for seven (2012–2018) and three years (2019–2021) used for training and testing the models, respectively. The Boruta-XGB-CNN-LSTM algorithm outperformed the other models in forecasting the 1-day-ahead EC in both stations over the test dataset (R = 0.9429, RMSE = 45.6896, and MAPE = 5.9749 for Albert River; and R = 0.9215, RMSE = 43.8315, and MAPE = 7.6029 for Barratta Creek). In addition, the Boruta-XGB-CNN-LSTM model could effectively forecast the EC for the next 3–10 days. Nevertheless, the performance of the Boruta-XGB-CNN-LSTM model slightly deteriorated as the forecasting horizon increased from 3 to 10 days. Overall, the Boruta-XGB-CNN-LSTM model is an effective soft computing method for accurately predicting the EC fluctuation in rivers.

show abstract

“…Data were obtained from http://data.tpdc.ac.cn. The simultaneous observation dataset includes soil temperature and moisture as well as soil roughness [22,23]. In this paper, we used SM and soil roughness data (SRD) in September 19, 2018.…”

Section: A the Study Area And Materialsmentioning

confidence: 99%

“…a. GBDT GBDT is a gradient boosting decision tree, where the output of a GBDT model is the sum of several decision trees, each of which is a fit to the residuals of the previous combination of decision trees, a "correction" to the previous model ". Gradient boosting trees can be used for both regression problems (in this case known as CART regression trees) and classification problems (in this case known as classification trees [22,23].…”

Section: B Ensemble Learning Algorithmmentioning

confidence: 99%

Potential of Remote Sensing Images for Soil Moisture Retrieving Using Ensemble Learning Methods in Vegetation-Covered Area

Gao,

Wang,

Zhong

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Soil moisture (SM) plays a critical role in various fields such as agriculture, hydrology, and land-atmosphere interactions. Despite numerous studies investigating SM inversion using ensemble learning and microwave remote sensing, the optimal method remains uncertain. This study aims to evaluate the performance of the categorical boosting algorithm (CatBoost) in comparison to other multiple-boosting algorithms for SM prediction. Special emphasis is given to feature selection in a vegetation-covered area based on remote sensing imagery. Appropriate feature selection is vital for achieving accurate predictions, and this study focuses on identifying relevant features and assessing CatBoost's suitability for the task. The study incorporates several boosting algorithms including Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and CatBoost to estimate SM. Results indicate that radar backscatter coefficient, soil roughness, and digital elevation model (DEM) are crucial features for SM retrieval. Comparatively, CatBoost outperforms GBDT, XGBoost, and LightGBM in various feature combinations. The most favorable results are obtained when utilizing all features as inputs for the algorithm. These optimal results yield a mean absolute error (MAE) of 2.40 vol.%, mean relative error (MRE) of 0.16 vol.%, root mean square error (RMSE) of 3.26 vol.%, and Pearson correlation coefficient of 0.73. Additionally, the study analyzes the inversion results for different ranges of SM and Normalized Difference Vegetation Index (NDVI). Within the range of SM from 0 to 25 vol.% and NDVI from 0 to 0.7, utilizing all features yields the most accurate results.

show abstract

Long-term multi-step ahead forecasting of root zone soil moisture in different climates: Novel ensemble-based complementary data-intelligent paradigms

Cited by 25 publications

References 54 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

Multistep 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

Potential of Remote Sensing Images for Soil Moisture Retrieving Using Ensemble Learning Methods in Vegetation-Covered Area

Contact Info

Product

Resources

About