Critical role of climate factors for groundwater potential mapping in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms

Guo, Xu; Gui, Xiaofan; Xiong, Hanxiang; Hu, Xiaojing; Li, Yonggang; Cui, Hao; Qiu, Yang; Ma, Chuanming

doi:10.1016/j.jhydrol.2023.129599

Cited by 52 publications

(13 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The spatiotemporal variations in soil salinization were analyzed. These methods all belong to ensemble learning, which is a technique that can improve the generalization ability and robustness of a single learner by combining the prediction results of multiple base learners [38]. These methods have been used by numerous studies to estimate soil salinization, whose results indicate that these methods have high accuracy and strong robustness [39][40][41][42].…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Variation and Future Predictions of Soil Salinization in the Werigan–Kuqa River Delta Oasis of China

He,

Ding,

Huang

et al. 2023

Sustainability

View full text Add to dashboard Cite

Soil salinization is a serious global issue; by 2050, without intervention, 50% of the cultivated land area will be affected by salinization. Therefore, estimating and predicting future soil salinity is crucial for preventing soil salinization and investigating potential arable land resources. In this study, several machine learning methods (random forest (RF), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGBoost)) were used to estimate the soil salinity in the Werigan–Kuqa River Delta Oasis region of China from 2001 to 2021. The cellular automata (CA)–Markov model was used to predict soil salinity types from 2020 to 2050. The LightGBM method exhibited the highest accuracy, and the overall prediction accuracy of the methods had the following order: LightGBM > RF > GBRT > XGBoost. Moderately saline, severely saline, and saline soils were dominant in the east and south of the research area, while non-saline and mildly saline soils were widely distributed in the inner oasis area. A marked decreasing trend in the soil salt content was observed from 2001 to 2021, with a decreasing rate of 4.28 g/kg·10 a−1. The primary change included the conversion of mildly and severely saline soil types to non-saline soil. The generalized difference vegetation index (51%), Bio (30%), and temperature vegetation drought index (27%) had the greatest influence, followed by variables associated with soil attributes (soil organic carbon and soil organic carbon stock) and terrain (topographic wetness index, slope, aspect, curvature, and topographic relief index). Overall, the CA–Markov simulation resulted exhibited suitable accuracy (kappa = 0.6736). Furthermore, areas with non-saline and mildly saline soils will increase while areas with other salinity levels will continue to decrease from 2020 to 2050. From 2046 to 2050, numerous areas with saline soil will be converted to non-saline soil. These results can provide support for salinization control, agricultural production, and soil investigations in the future. The gradual decline in soil salinization in the research area in the past 20 years may have resulted from large-scale land reclamation, which has turned saline alkali land into arable land and is also related to effective measures taken by the local government to control salinization.

show abstract

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Variation and Future Predictions of Soil Salinization in the Werigan–Kuqa River Delta Oasis of China

He,

Ding,

Huang

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…LightGBM is a widely used boosting method that has fixed the drawback of GBR when handling large datasets. The process incorporated gradient-based one-side sampling (GOSS) and exclusive feature bundling (EFB) techniques to aid in training without compromising model accuracy and performance, as outlined by [39]. The GOSS algorithm selectively excludes a significant portion of data instances with small gradients, focusing solely on those that contribute significantly to information gain.…”

Section: Light Gradient Boosting Regression (Lightgbm)mentioning

confidence: 99%

“…Then, for the remaining set A c , which constitutes (1 − a) × 100% instances with smaller gradients, a new subset B is formed through random sampling with a size of b × |A c |. Finally, the variance gain Ṽj (d) is employed to split data instances which can be expressed as follows [39,40]:…”

Section: Light Gradient Boosting Regression (Lightgbm)mentioning

confidence: 99%

A comprehensive machine learning-based investigation for the index-value prediction of 2G HTS coated conductor tapes

Bonab,

Russo,

Morandi

et al. 2024

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

Index-value, or so-called n-value prediction is of paramount importance for understanding the superconductors’ behaviour specially when modelling of superconductors is needed. This parameter is dependent on several physical quantities including temperature, the magnetic field’s density and orientation, and affects the behaviour of HTS devices made out of coated conductors in terms of losses and quench propagation. In this paper, a comprehensive analysis of many machine learning methods for estimating the n-value has been carried out. The results demonstrated that Cascade Forward Neural Network (CFNN) excels in this scope. Despite needing considerably higher training time when compared to the other attempted models, it performs at the highest accuracy, with 0.48 Root Mean Squared Error (RMSE) and 99.72% Pearson coefficient for goodness of fit (R-squared). On the other hand, the Rigid Regression method had the worst predictions with 4.92 RMSE and 37.29% R-squared. Also, Random Forest, boosting methods, and simple Feed Forward Neural Network can be considered as a middle accuracy model with faster training time than CFNN. The findings of this study not only advance modelling of superconductors but also pave the way for applications and further research on machine learning plug-and-play codes for superconducting studies including modelling of superconducting devices.

show abstract

“…It can handle both classification and regression problems. It is very popular among researchers since it can handle very non-linear datasets and demonstrate good performance in most of cases [85,86]. In fact, it is an ensemble ML algorithm that makes an estimation by the combination of the results of multiple weak learners like the decision tree method [87].…”

Section: Xgboostmentioning

confidence: 99%

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

Alipour Bonab,

Yazdani-Asrami

2024

Nano Ex.

View full text Add to dashboard Cite

The rising demand for advanced energy systems requires enhanced thermal management strategies to maximize resource utilization and productivity. This study intends to address the critical need for efficient heat transfer mechanisms in industrial energy systems, particularly those relying on pool boiling conditions, by mainly focusing on Critical Heat Flux (CHF). In fact, CHF keeps a limit in thermal system design, beyond which the efficiency of the system drops. Recent research materials have highlighted nanofluids' superior heat transfer properties over conventional pure fluids, like water, which makes them a considerable substitution for improving CHF in cooling systems. However, the broad variability in experimental outcomes challenges the development of a unified predictive model. Besides, Machine Learning (ML) based prediction has shown great accuracy for modelling of the designing parameters, including CHF. Utilizing ML algorithms—Cascade Forward Neural Network (CFNN), Extreme Gradient Boosting (XGBoost), Extra Tree, and Light Gradient Boosting Method (LightGBM)— four predictive models have been developed and the benchmark shows CFNN's superior accuracy with an average goodness of fit of 89.32%, significantly higher than any available model in the literature. Also, the iterative stability analysis demonstrated that this model with a 0.0348 standard deviation and 0.0268 mean absolute deviation is the most stable and robust method that its performance minorly changes with input data. The novelty of the work mainly lies in the prediction of CHF with these advanced algorithms models to enhance the reliability and accuracy of CHF prediction for designing purposes, which are capable to consider many effective parameters into account with very higher accuracy than mathematical fittings. This study not only explains the complex interplay of nanofluid parameters affecting CHF, but also offers practical implications for the design of more efficient thermal management systems, thereby contributing to the broader field of energy system enhancement through innovative cooling solutions.

show abstract

Critical role of climate factors for groundwater potential mapping in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms

Cited by 52 publications

References 99 publications

Spatiotemporal Variation and Future Predictions of Soil Salinization in the Werigan–Kuqa River Delta Oasis of China

Spatiotemporal Variation and Future Predictions of Soil Salinization in the Werigan–Kuqa River Delta Oasis of China

A comprehensive machine learning-based investigation for the index-value prediction of 2G HTS coated conductor tapes

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

Contact Info

Product

Resources

About