Assessment of XGBoost to Estimate Total Sediment Loads in Rivers

Piraei, Reza; Afzali, Seied Hosein; Niazkar, Majid

doi:10.1007/s11269-023-03606-w

Cited by 18 publications

(4 citation statements)

References 30 publications

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“…The neurons positioned within a specific layer are connected to the neurons in adjacent layers, while no connection among neurons of a single layer is allowed. This architecture enables ANN to serve as a suitable ML model in various fields of research, particularly for water quality [22][23][24][25] and other applications in water resources management [26,27].…”

Section: Machine Learning Models 231 Artificial Neural Networkmentioning

confidence: 99%

“…This ensemble learning method constructs multiple decision trees (Figure 3), each one trained on a randomly selected subset of the data and features use of bagging or bootstrap aggregation [30]. The decision trees are combined to produce the final output, which is determined by aggregating the predictions of all individual trees [26]. Furthermore, the final output of RF is the mode of classes for classification and the mean prediction for regression, respectively.…”

Section: Random Forestmentioning

confidence: 99%

“…This study utilized three boosting-based algorithms including AdaBoost (AB), Gradient Boosting Regressor (GBR), and XGBoost Regressor (XGBR). More detailed information regarding these boosting-based algorithms can be found in [26].…”

Section: Boosting-based Algorithmsmentioning

confidence: 99%

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A Machine Learning-Based Framework for Water Quality Index Estimation in the Southern Bug River

Masood,

Niazkar,

Zakwan

et al. 2023

Water

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River water quality is of utmost importance because the river is not only one of the key water resources but also a natural habitat serving its surrounding environment. In a bid to address whether it has a qualified quality, various analytics are required to be considered, but it is challenging to measure all of them frequently along a river reach. Therefore, estimating water quality index (WQI) incorporating several weighted analytics is a useful approach to assess water quality in rivers. This study explored applications of ten machine learning (ML) models to estimate WQI for the Southern Bug River, which is the second-longest river in Ukraine. The ML methods considered in this study include artificial neural networks (ANNs), Support Vector Regressor (SVR), Extreme Learning Machine, Decision Tree Regressor, random forest, AdaBoost (AB), Gradient Boosting Regressor, XGBoost Regressor (XGBR), Gaussian process (GP), and K-nearest neighbors (KNN). Each data measurement consists of nine analytics (NH4, BOD5, suspended solids, DO, NO3, NO2, SO4, PO4, Cl), while the quantity of data is more than 2700 data points. The results indicated that all ML models demonstrate satisfactory performance in predicting WQI. However, GP outperformed the other models, followed by XGBR, SVR, and KNN. Furthermore, ANN and AB demonstrated relatively weaker performance. Moreover, a reliability assessment conducted on both training and testing datasets also confirmed the results of the comparative analysis. Overall, the results enhance the assertion that ML models can sufficiently predict WQI, thereby enhancing water quality management.

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Section: Machine Learning Models 231 Artificial Neural Networkmentioning

confidence: 99%

Section: Random Forestmentioning

confidence: 99%

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A Machine Learning-Based Framework for Water Quality Index Estimation in the Southern Bug River

Masood,

Niazkar,

Zakwan

et al. 2023

Water

Self Cite

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“…In meteorological and hydrological drought forecasting, the use of machine learning models to construct regional meteorological and hydrological drought forecasting models has made some progress. Compared with the traditional statistical regression methods, Support Vector Machine (SVM) [26], Back Propagation Neural Network (BPNN) [27], eXtreme Gradient Boosting (XGBoost) [28], random forest (RF) [29,30], and other machine learning algorithms have significant advantages in processing large-scale and multi-source remote sensing data and have been gradually used in meteorological and hydrological forecasting. For example, Elbeltagi et al [31] investigated the prediction accuracy of SPI-based RF, Random Tree (RT), and Gaussian Process Regression (GPR-PUK kernel) models for forecasting meteorological droughts in semi-arid regions.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Hydrological and Meteorological Composite Drought Characteristics Based on Baseflow and Precipitation

Huang,

Zhang,

Liu

et al. 2024

Water

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Traditional univariate drought indices may not be sufficient to reflect comprehensive information on drought. Therefore, this paper proposes a new composite drought index that can comprehensively characterize meteorological and hydrological drought. In this study, the new drought index was established by combining the standardized precipitation index (SPI) and the standardized baseflow index (SBI) for the Jiaojiang River Basin (JRB) using the copula function. The prediction model was established by training random forests on past data, and the driving force behind the combined drought index was explored through the LIME algorithm. The results show that the established composite drought index combines the advantages of SPI and SBI in drought forecasting. The monthly and annual droughts in the JRB showed an increasing trend from 1991 to 2020, but the temporal characteristics of the changes in each subregion were different. The accuracies of the trained random forest model for heavy drought in Baizhiao (BZA) and Shaduan (SD) stations were 83% and 88%, respectively. Furthermore, the Local Interpretable Model-Agnostic Explanations (LIME) interpretation identified the essential precipitation, baseflow, and evapotranspiration features that affect drought. This study provides reliable and valid multivariate indicators for drought monitoring and can be applied to drought prediction in other regions.

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Drought analysis using innovative trend analysis and machine learning models for Eastern Black Sea Basin

Niazkar,

Piraei,

Eryılmaz Türkkan

et al. 2023

Theor Appl Climatol

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Assessment of XGBoost to Estimate Total Sediment Loads in Rivers

Cited by 18 publications

References 30 publications

A Machine Learning-Based Framework for Water Quality Index Estimation in the Southern Bug River

A Machine Learning-Based Framework for Water Quality Index Estimation in the Southern Bug River

Assessment of Hydrological and Meteorological Composite Drought Characteristics Based on Baseflow and Precipitation

Drought analysis using innovative trend analysis and machine learning models for Eastern Black Sea Basin

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