Evaluating optically and non-optically active water quality and its response relationship to hydro-meteorology using multi-source data in Poyang Lake, China

Fu, Bolin; Lao, Zhinan; Liang, Yiyin; Sun, Jun; He, X. T.; Deng, Tengfang; He, Wen; Fan, Donglin; Gao, Ertao; Hou, Qingxi

doi:10.1016/j.ecolind.2022.109675

Cited by 17 publications

(6 citation statements)

References 63 publications

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“…Compared with satellite imagery, UAV imagery has a high spatial resolution and smaller pixel size, which greatly attenuates the influence of mixed pixels. Due to this characteristic, the error between the actual reflectance of the water at the sampling point and the reflectance of the corresponding pixel is significantly reduced [30]. To sum up, these results show the advantages of high-resolution UAV multispectral imagery in the field of water quality inversion for small/medium-sized rivers.…”

Section: Comparison Of Inversion Accuracy With Other Researchmentioning

confidence: 63%

“…Currently, instances of water quality inversion are not uncommon, and extensive research has been conducted on methods for water quality inversion in large bodies of water such as lakes, rivers, reservoirs, and urban landscape waterways [24][25][26][27][28]. Successful outcomes have been achieved in lake water quality inversion by Li et al [29], Fu et al [30], and Wang et al [31], while research by Tan et al [20], Cao et al [32], and Ding et al [33] on large river water quality inversion has produced relevant results. Simultaneously, reservoir water quality inversion has been accomplished by He et al [15], Qian et al [34], and Jiang et al [35].…”

Section: Introductionmentioning

confidence: 99%

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Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms

Chen,

Yao,

Zhu

et al. 2024

Water

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Remote sensing technology applications for water quality inversion in large rivers are common. However, their application to medium/small-sized water bodies within rural areas is limited due to the low spatial resolution of remote sensing images. In this work, a typical small rural river was selected, and high-resolution unmanned aerial vehicle (UAV) multispectral images and ground monitoring data of the river were obtained. Then, a comparative analysis of three univariate regression models and nine machine learning models (Ridge Regression (RR), Support Vector Regression (SVR), Grid Search Support Vector Regression (GS-SVR), Random Forest (RF), Grid Search Random Forest (GS-RF), eXtreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Catboost Regression (CBR)) for their accuracy in the prediction of turbidity (TUB), total nitrogen (TN), and total phosphorus (TP) was performed. TUB can be achieved by simple statistical regression models. The CBR model exhibited the best performance for the three index inversions on the test set evaluation metrics: R2 (0.90~0.92), RMSE (7.57 × 10−3~1.59 mg/L), MAE (0.01~1.30 mg/L), RPD (3.21~3.56), and NSE (0.84~0.92). The water pollution of the study area was closely related to its land-use pattern, excessive and irrational fertilizer application, and distribution of pollutant outlets.

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Section: Comparison Of Inversion Accuracy With Other Researchmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms

Chen,

Yao,

Zhu

et al. 2024

Water

View full text Add to dashboard Cite

show abstract

“…In contrast, UAV remote sensing can eliminate atmospheric interference and efficiently capture water spectral parameters. To sum up, water quality inversion based on UAV remote sensing achieves higher accuracy [69,70]. Meanwhile, compared with other widely used machine learning models, for instance, Chen et al used the GA-XGBoost model to invert water quality parameters of small and medium-sized rivers, and the R 2 values of their models were in the range of 0.597 to 0.855 [71].…”

Section: Compare With Other Studiesmentioning

confidence: 99%

Water Quality Prediction of Small-Micro Water Body Based on the Intelligent-Algorithm-Optimized Support Vector Machine Regression Method and Unmanned Aerial Vehicles Multispectral Data

Yao,

Chen,

et al. 2024

Sustainability

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Accurate prediction of spatial variation in water quality in small microwaters remains a challenging task due to the complexity and inherent limitations of the optical properties of small microwaters. In this paper, based on unmanned aerial vehicles (UAV) multispectral images and a small amount of measured water quality data, the performance of seven intelligent algorithm-optimized SVR models in predicting the concentration of chlorophyll (Chla), total phosphorus (TP), ammonia nitrogen (NH3-N), and turbidity (TUB) in small and micro water bodies were compared and analyzed. The results show that the Gray Wolf optimized SVR model (GWO-SVR) has the highest comprehensive performance, with R2 of 0.915, 0.827, 0.838, and 0.800, respectively. In addition, even when dealing with limited training samples and different data in different periods, the GWO-SVR model also shows remarkable stability and portability. Finally, according to the forecast results, the influencing factors of water pollution were discussed. This method has practical significance in improving the intelligence level of small and micro water body monitoring.

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“…GBR is a powerful machine learning technique used for predictive modeling and regression analysis [33]. It works by iteratively improving a set of weak models by combining them in a weighted manner to obtain a more accurate model.…”

Section: Gradient Boosting Regressor (Gbr)mentioning

confidence: 99%

Artificial Intelligence Models for Predicting Ground Vibrations in Deep Underground Mines to Ensure the Safety of Their Surroundings

Tao,

Chen,

Xiao

et al. 2024

Applied Sciences

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Ground vibrations induced by underground mining blasting has a significant impact on the stability and safety of surface buildings near mines. Due to the thick rock layers overlying underground mines, there is presently limited accuracy in regard to predicting ground vibrations induced by underground mine blasting. Therefore, this study aims to improve the accuracy of predicting ground vibrations induced by underground blasting by comprehensively measuring the peak particle velocity (PPV) in all three directions and independently considering on the impact of vertical distance. Random forest regression (RFR), bagging regression (BR), and gradient boosting regression (GBR) were used to regress the X-axis PPV (X-PPV), Y-axis PPV (Y-PPV), and Z-axis PPV (Z-PPV) based on blasting records measured at an iron mine. In addition, a genetic algorithm, gray wolf optimizer (GWO), and a particle swarm optimization were used to optimize the parameters of the RFR, BR, and GBR. The comparison results show that GWO-GBR is the optimal model for the prediction of the X-PPV (R2 = 0.8072), Y-PPV (R2 = 0.9147), and Z-PPV (R2 = 0.9265), respectively. Thus, the GWO-GBR model proposed in this study is considered a highly reliable model for predicting ground vibrations induced by underground mine blasting to ensure the safety of the mines’ surroundings.

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Evaluating optically and non-optically active water quality and its response relationship to hydro-meteorology using multi-source data in Poyang Lake, China

Cited by 17 publications

References 63 publications

Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms

Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms

Water Quality Prediction of Small-Micro Water Body Based on the Intelligent-Algorithm-Optimized Support Vector Machine Regression Method and Unmanned Aerial Vehicles Multispectral Data

Artificial Intelligence Models for Predicting Ground Vibrations in Deep Underground Mines to Ensure the Safety of Their Surroundings

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