Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms

Lu, Qikai; Si, Wei; Wei, Lifei; Li, Zhongqiang; Xia, Zhihong; Song, Yong; Xia, Yu

doi:10.3390/rs13193928

Cited by 33 publications

(31 citation statements)

References 44 publications

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“…An R 2 score of 1 indicates perfect precision, while a score of 0 indicates that the model has the worst prediction performance. The RMSE and MAE are the statistical values of the error between the predicted value and the observed value [42]. The value range of RMSE is (0, +∞).…”

Section: Inversion Model Development Methodsmentioning

confidence: 99%

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Dynamics and Drivers of Water Clarity Derived from Landsat and In-Situ Measurement Data in Hulun Lake from 2010 to 2020

Zhao

Zhang

Guo

et al. 2022

Water

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Water clarity (Secchi disk depth, SDD), as a proxy of water transparency, provides important information on the light availability to the lake ecosystem, making it one of the key indicators for evaluating the water ecological environment, particularly in nutrient-rich inland lakes. Hulun Lake, the fifth largest lake in China, has faced severe water quality challenges in the past few decades, e.g., high levels of phosphorus and nitrogen, leading to lake eutrophication. However, under such a serious context, the temporal and spatial dynamics of SDD in Hulun Lake are still unclear. In this paper, we obtained the best model input parameters by using stepwise linear regression models to test field measurements against remote sensing band information, and then developed the SDD satellite algorithm suitable for Hulun Lake by comparing six models (i.e., linear, quadratic, cubic, exponential, power, and logarithmic). The results showed that (1) B3/(B1 + B4) [red/(blue-near-infrared)] was the most sensitive parameter for transparency (R = 0.84) and the exponential model was the most suitable transparency inversion model for Hulun Lake (RMSE = 0.055 m, MAE = 0.003 m), (2) The annual mean SDD of Hulun Lake was higher in summer than in autumn, the summer SDD decreased from 2010 (0.23 m) to 2020 (0.17 m), and the autumn SDD increased from 2010 (0.06 m) to 2020 (0.16 m). The SDD in the littoral zones of Hulun Lake was less than that in the central part; (3) meteorological conditions (i.e., precipitation and wind speed) were highly correlated with the variation of SDD. Cropland expansion was the possible reason for the low SDD at the entrance of Hulun Lake flow. The findings of this study have important implications for the development and implementation of ecological protection and restoration strategies in the Hulun Lake basin.

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Section: Inversion Model Development Methodsmentioning

confidence: 99%

“…If the dispersion of the inverse model is high, the values of RMSE and MAE will be enlarged. A model with high R 2 , low RMSE, and low MAE is deemed as a suitable model for quantitative inversion [42].…”

Section: Inversion Model Development Methodsmentioning

confidence: 99%

Dynamics and Drivers of Water Clarity Derived from Landsat and In-Situ Measurement Data in Hulun Lake from 2010 to 2020

Zhao

Zhang

Guo

et al. 2022

Water

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“…Multiobjective particle swarm optimization-deep belief net (MOPSO-DBN) is a new hybrid prediction model that improves the parameters of deep belief net (DBN) for shortterm traffic flow prediction using a multiobjective particle swarm optimization approach [2]. To capture nonlinear traffic dynamics, the LSTM neural network is used and the model is trained.…”

Section: Related Workmentioning

confidence: 99%

“…e tourist sector has also entered into a new era of rapid growth. Tourism and leisure industry in China has entered a new age of large-scale tourism; tourism has emerged as a signi cant source of pleasure as well as relaxation for modern Chinese and is gradually becoming an intrinsic part of daily life [2]. Liuzhou is a rich tourism city in China, which is known for its own ethnic culture, prehistoric culture, Liu Zongyuan culture, folk song culture, rare stone culture, and urban landscape, among other things.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tourism Demand in Liuzhou Region Based on Machine Learning

2022

Mobile Information Systems

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Liuzhou is a rich tourism city in China. It is well known for its ethnic and prehistoric culture, folk songs, rare stones, and urban landscape. The demand of tourism in Liuzhou city is increasing day by day. Therefore, a system is needed to accurately predict the increasing demand of tourism in Liuzhou city. For this reason, based on the examination of historical visitor data in natural sites especially in Liuzhou city, this research employs an important machine learning and deep learning approach. The purpose of this study is to identify the consumption patterns and improve prediction, prejudgment, and preparation abilities by incorporating them into an intelligent tourism service platform, preventing visitors from travelling at inconvenient times and providing appropriate suggestions to scenic site managers. In this paper, the Sparse Principal Component Analysis-Long-Short-Term Memory (SPCA-LSTM) model has been updated to the Sparse Principal Component Analysis Convolutional Neural Network Long-Short-Term Memory (SPCA-CNNLSTM) model to accurately predict the tourist traffic during the holidays. Convolutional and pooling layers are added to the network topology to extract the local characteristics of the input data. The data of passenger traffic and influencing factors of Liuzhou scenic area from the months of Sept. 2015 to Nov. 2019 were used as the data set for the experiments. A hybrid-forecasting model is also proposed in the paper, which first removes noise from international crude oil data using compression-aware denoising preprocessing and then combines compression-aware denoising with machine learning using artificial neural network (ANN) and support vector regression (SVR). The experimental result shows that the SPCA-CNNLSTM model predicts better values than the SPCA-LSTM model.

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“…In recent years, the development of unmanned aerial vehicle (UAV) aerial photogrammetry makes it convenient and reliable to obtain the terrain information of the underlying surface [10][11][12][13]. It has been widely used in monitoring dynamic changes in mountain glaciers [10][11][12][13][14], river morphology [15,16] and surface deformation [17], water quality [18], water turbidity [19], flood events [20], and changes in the coastal zone [21]. For river monitoring, many studies have been carried out using UAV data for river information gathering and discharge estimation [22] and river terraces and water erosion change monitoring [23].…”

Section: Introductionmentioning

confidence: 99%

Estimation of River Discharge Using Unmanned Aerial Vehicle (UAV) Based on Manning Formula for an Ungauged Alpine River in the Eastern Qilian Mountains

Cai

Gao

Fan

et al. 2022

Water

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River discharge is crucial to water resources development and ecological protection. However, in some arid areas of northwest China, it is still difficult to measure discharge accurately. In this study, unmanned aerial vehicle (UAV) imagery has been used to estimate river discharge at two river sections in the upper reaches of the Shiyang River in the eastern part of the Qilian Mountains based on the Manning formula. The estimated discharges at those two sections are 1.16 m3/s and 3.11 m3/s, respectively. Taking the discharges measured by an acoustic Doppler current profiler (ADCP) as the reference, the relative error of the estimates is below 5%, which is accurate enough for water resources management in mountain basin regions. Multiple high-resolution satellite images were also used to calculate water discharges at the two sections, which were in good agreement with the discharges estimated from UAVs. This study demonstrates the feasibility of using UAVs to estimate river discharge, which is of great significance for future regional-scale water resource assessments.

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Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms

Cited by 33 publications

References 44 publications

Dynamics and Drivers of Water Clarity Derived from Landsat and In-Situ Measurement Data in Hulun Lake from 2010 to 2020

Dynamics and Drivers of Water Clarity Derived from Landsat and In-Situ Measurement Data in Hulun Lake from 2010 to 2020

Prediction of Tourism Demand in Liuzhou Region Based on Machine Learning

Estimation of River Discharge Using Unmanned Aerial Vehicle (UAV) Based on Manning Formula for an Ungauged Alpine River in the Eastern Qilian Mountains

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