# Medium-Sized Lake Water Quality Parameters Retrieval Using Multispectral UAV Image and Machine Learning Algorithms: A Case Study of the Yuandang Lake, China

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## Abstract

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^{2}. This study proposed utilising a multispectral uncrewed aerial vehicle (UAV) to collect large-scale data and retrieve multiple water quality parameters using machine learning algorithms. An alternate processing method is proposed to process large and repetitive lake surface images for mapping the water quality data to the image. Machine learning regression methods (Random Forest, Gradient Boosting, Backpropagation Neural Network, and Convolutional Neural Network) were used to construct separate water quality inversion models for ten water parameters. The results showed that several water quality parameters (COD

_{Mn}, temperature, pH, DO, and NC) can be retrieved with reasonable accuracy (R

^{2}= 0.77, 0.75, 0.73, 0.67, and 0.64, respectively), although others (NH

_{3}-N, BGA, TP, Turbidity, and Chl-a) have a determination coefficient (R

^{2}) less than 0.6. This work demonstrated the tremendous potential of employing multispectral data in conjunction with machine learning algorithms to retrieve multiple water quality parameters for monitoring medium-sized bodies of water.

## 1. Introduction

_{3}-N) [10,15,17], electrical conductivity (EC) [13,14], pH [9,13] and dissolved oxygen (DO) [10,13,14] which are important indicators for inland water quality. The water retrieval models used in the studies are eXtreme Gradient Boosting (XGBoost) [10], Support Vector Regression (SVR) [10,14,15], Random Forest (RF) [10,15], Multiple Linear Regression (MLR) [14], Extreme Learning Machine Regression (ELR) [14], Gradient Boosting Machine (GBM) [15] Convolutional Neural Networks (CNN) [11,12], and Artificial Neural Network (ANN) [10,15]. According to this research, satellite remote sensing technology is relatively mature in the application of water quality monitoring and can yield decent results. However, its applicability in remote sensing of water environments of small- and medium-sized lakes, reservoirs, and rivers is limited due to the spatial resolution, temporal resolution, and occlusions caused by atmospheric clouds [18,19]. As a result, new strategies must be developed to monitor the water quality characteristics in small- to medium-sized water bodies.

_{3}-N [25], and permanganate index (COD

_{Mn}) [26]. The algorithms used to retrieve the water quality parameters are RF [25,26,27,28], XGBoost [25,26,27], SVR [28], Backpropagation Neural Network (BP) [26], CNN [28], ELR [28], Deep Neural Network (DNN) [25], ANN [27], and matching pixel by pixel (MPP) algorithm [24,29]. However, for other water parameters such as temperature, EC, DO, and pH, some research indicate that UAV is used to collect water samples or measurements directly to obtain the concentration; the retrieval of those parameters using remotely sensed UAV data has not been widely explored [22]. Hence, more investigations are required to study the feasibility of multispectral UAV remote sensing data in retrieving multiple water parameters. Despite the fact that UAVs are capable of monitoring at a variety of geographic scales, including rivers, reservoirs, and lakes, research on UAVs has only focused on small-scale data collecting and water quality monitoring [22,25,26,30,31]. According to the statistic of the number of lakes and surface area provided in China, 90% of the lakes are found with a surface area within 1 to 50 km

^{2}[32]. Despite the high number of small-to-medium scaled lakes, the monitoring method has not been investigated thoroughly for medium-sized water bodies due to extensive and prolonged data collection, as well as distinct data processing techniques. UAV remote sensing data collection is constrained by the flight duration, weather and the data requirement for building high-quality orthomosaic maps [20]. Due to the existing limitations of UAVs, data collection and processing are more challenging for medium-sized lakes. Hence, our research proposed the use of multispectral UAV remote sensing data for medium-sized lake water quality monitoring and investigated the inversion of multiple water parameters using machine learning algorithms for inland water monitoring.

_{3}-N, TP, and COD

_{Mn}.

## 2. Materials and Methods

#### 2.1. Study Area

^{2}, which is a reasonable representation of medium-sized lakes. Based on the satellite images of the lake from 2019 to 2021, it is observed that there were algae blooms occurring throughout the months of August and September. Temperature increment during the summer season caused the occurrence of algae bloom, which will have a major impact on the water ecosystem and generate serious environmental problems. The Yuandang Lake is one of the important lakes in the Yangtze Delta region, which is essential for economic, ecological, and social benefits and is a habitat for many species. In the overall development strategy in the Yangtze Delta region, the water quality of the lake needs to be monitored and improved to support the development of green economy. Thus, it is vital to monitor the water quality of the Yuandang lake, and study is undertaken for the entire region of the lake. Figure 1 depicts the geographic location of the study region.

#### 2.2. Data Collection

#### 2.2.1. UAV Multispectral Image Collection

#### 2.2.2. In Situ Water Data Collection

_{3}-N, TP, and COD

_{Mn}, which are essential for determining the standards of lake eutrophication and popular indicators for organic pollution. Two 2.5-L bottles of water are collected at a depth of 50 cm at each sampling location for spectrophotometric analysis to determine the exact water quality concentration.

#### 2.3. Data Processing

#### 2.3.1. UAV Multispectral Image Data Processing

#### 2.3.2. In Situ Water Data Processing

#### 2.3.3. Mapping of UAV and Water Data

#### 2.3.4. Calculation of Band Indices

#### 2.3.5. Machine Learning Models

#### 2.3.6. Accuracy Evaluation

^{2}), root mean square error (RMSE), and mean absolute error (MAE) were used to quantify the accuracy and performance of the model using new data (Equations (1)–(3), respectively).

^{2}ranges from 0 to 1. An R

^{2}value of 1 denotes perfect precision, whereas a score of 0 denotes the model’s lowest prediction performance. The value range of RMSE is (0, +∞). High RMSE indicates that the model’s predicted value has a high degree of deviation. MAE is the mean of the absolute value of the error between the predicted value and the observed value. A model with a high R

^{2}, low RMSE, and a low MAE is considered suitable for quantitative inversion.

## 3. Results

#### 3.1. Data Analysis

_{3}-N ranged from 0.2658–1.1499 mg/L within 5–10 August, with the mean and standard deviation of 0.6394 ± 0.2235 mg/L; the highest concentration was obtained on 5 August, TP ranged from 0.5809–1.1499 mg/L with mean and standard deviation of 0.7967 ± 0.1512 mg/L. Based on the concentration of TP, a decreasing trend is observed in the concentration values.

_{Mn}, it ranged from 3.4296–7.5345 mg/L within 5–10 August, with the mean and standard deviation of 6.1657 ± 0.8656 mg/L; the highest concentration was obtained on 5 August where COD

_{Mn}ranged from 6.8058–7.5345 mg/L with mean and standard deviation of 7.1753 ± 0.2098 mg/L.

#### 3.2. Spectral Index and Water Quality Parameters Correlation Analysis

_{3}-N, TP, COD

_{Mn}, and 84 spectral indices. Among the three water parameters, COD

_{Mn}has the highest correlation coefficients with spectral indices, where the highest correlated spectral index is S2 with a 0.749 correlation value. S15 is the most correlated spectral index for TP, with a correlation value of −0.5121, and S58 is the most correlated spectral index for NH

_{3}-N.

_{3}-N, TP, BGA, and Chl-a show a weaker correlation with the features. Eventually, the highest correlated spectral indices were selected as variables to establish the inversion model.

#### 3.3. Results of Multivariate Regression Models

_{3}-N, TP, COD

_{Mn}, Chl-a, and BGA. According to the results, the RF model performed the best out of all models, while the CNN model performed the worst. On the training dataset, the RF model achieved around 0.88–0.95 R

^{2}for all five parameters inversion; however, the R

^{2}of the testing dataset was lowest for Chl-a (R

^{2}= 0.33) while highest for COD

_{Mn}(R

^{2}= 0.78) and moderate for NH

_{3}-N (R

^{2}= 0.51), TP (R

^{2}= 0.45), and BGA (R

^{2}= 0.43). On the training dataset, the GB model achieved around 0.65–0.95 R

^{2}for all five parameters inversion; however, the R

^{2}of the testing dataset was lowest for Chl-a (R

^{2}= 0.32) and TP (R

^{2}= 0.32) while highest for COD

_{Mn}(R

^{2}= 0.74) and moderate for NH

_{3}-N (R

^{2}= 0.55) and BGA (R

^{2}= 0.45). In comparison, the BP model achieved around 0.25–0.65 R

^{2}in the training dataset and 0.15–0.52 R

^{2}in the testing dataset for all five parameters. The worst-performing BP model was the inversion of the NH

_{3}-N (R

^{2}= 0.15) water parameter, while the best-performing model was the inversion of BGA (R

^{2}= 0.52). CNN model, on the other hand, achieved around 0.20–0.67 R

^{2}for the training dataset and 0.15–0.64 R

^{2}for the testing dataset. The best-performing CNN model was for COD

_{Mn}(R

^{2}= 0.64). Overall, COD

_{Mn}has the best inversion result.

_{3}-N is the GB model (R

^{2}= 0.55), whereas the best model for retrieving water parameter TP, COD

_{Mn}, and Chl-a is the RF model (R

^{2}= 0.45, R

^{2}= 0.78, and R

^{2}= 0.33); the best model for retrieving water parameter BGA is BP model (R

^{2}= 0.52).

^{2}for all five parameters inversion; however, the R

^{2}of the testing dataset was lowest for Turbidity (R

^{2}= 0.34) while highest for pH (R

^{2}= 0.73), temperature (R

^{2}= 0.70), DO (R

^{2}= 0.67), and NC (R

^{2}= 0.64). On the training dataset, the GB model achieved around 0.77–0.90 R

^{2}for all five parameters inversion; however, the R

^{2}of the testing dataset was lowest for Turbidity (R

^{2}= 0.30) while highest for temperature (R

^{2}= 0.75) and pH (R

^{2}= 0.67) and moderate for DO (R

^{2}= 0.62) and NC (R

^{2}= 0.59). In comparison, the BP model achieved around 0.36–0.68 R

^{2}in the training dataset and 0.31–0.64 R

^{2}in the testing dataset for all five parameters. The worst-performing BP model was the inversion of Turbidity (R

^{2}= 0.31), while the best-performing model was the inversion of temperature (R

^{2}= 0.64). The CNN model, on the other hand, achieved around 0.32–0.44 R

^{2}for the training dataset and 0.35–0.40 R

^{2}for the testing dataset. The best-performing CNN model was for NC (R

^{2}= 0.40). Overall, pH, NC, DO, and temperature have good inversion results.

^{2}= 0.34), whereas the best model for retrieving water parameter pH, NC, and DO is the RF model (R

^{2}= 0.72, R

^{2}= 0.64, and R

^{2}= 0.67); the best model for retrieving water parameter temperature is GB model (R

^{2}= 0.75).

^{2}or higher were COD

_{Mn}, temperature, pH, DO, and NC, with the R

^{2}of 0.77, 0.75, 0.73, 0.67, and 0.64. Turbidity and Chl-a have the lowest R

^{2}with only 0.34 and 0.33.

## 4. Discussion

#### 4.1. Correlated Features of Different Water Quality Parameters

_{3}-N, TP, COD

_{Mn}, pH, BGA, Chl-a, EC, DO, temperature, and Turbidity. Based on previous studies, water quality parameters can be classified into two categories: optically active and optically inactive. Among the ten measured water parameters, optically active parameters include Chl-a, temperature, Turbidity, EC, and BGA, while optically inactive parameters include NH

_{3}-N, TP, DO, COD

_{Mn}, and pH [14,42,43,44].

#### 4.2. Performance of ML Models in Water Quality Monitoring

_{Mn}, TP, NH

_{3}-N, Turbidity, and Chl-a can be used to compare with other studies [25,26,28]. Two other studies employed the exact same multispectral UAV device as ours, but the flight altitude settings were substantially different, with one set at 150–160 m, one at 10 m, and ours at 500 m [26,28]. At the same time, the other study employed a multirotor UAV with Rededge-MX multi-spectral camera flying at 160–165 m for image data collection [25]. Table 7 displays the regression modelling results of several water parameters using different ML models.

_{Mn}using the RF model with the R

^{2}of 0.778 with the RMSE of 0.304 mg/L. The best result for TP, NH

_{3}-N, Turbidity, and Chl-a was using the GA_XGBoost ML model [25]. The author combined the characteristic of adaptive search from GA with the high efficiency and flexibility of XGBoost to obtain good modelling results. However, some of the remaining water parameters (temperature, pH, DO, NC, and BGA) obtained decent results (R

^{2}= 0.75, 0.73, 0.67, 0.64, and 0.52) but they are not comparable because UAV multispectral was not previously utilised to model those parameters. Although some of the water parameters are optically inactive, UAVs have the advantage of obtaining high-resolution data that can be used to improve the modelling of those parameters. This work validates the high accuracy of obtaining numerous optical and non-optical active parameters using ML algorithms; however, it does have certain limitations. Further research using various ML and Deep Learning models in conjunction with multispectral data is necessary.

#### 4.3. Limitations

## 5. Conclusions

^{2}. The multispectral data were collected and processed systematically, and different band equations were used to generate the spectral indices, with the optimal indices chosen for modelling based on Pearson correlation analysis. ML regression methods, including RF, GB, BP, and CNN, were used to construct separate water quality retrieval models for NH

_{3}-N, TP, COD

_{Mn}, pH, BGA, Chl-a, EC, DO, temperature, and Turbidity. Water parameters including R2, temperature, pH, DO, and NC (R

^{2}= 0.77, 0.75, 0.73, 0.67, and 0.64) all achieved satisfactory results, while NH

_{3}-N, BGA, TP, Turbidity, and Chl-a obtained poor results (R

^{2}= 0.55, 0.52, 0.45, 0.34, and 0.33). The feature variables derived from multispectral data demonstrate notable advantages in terms of water quality inversion for several water parameters and predicted water quality changes and spatial distributions more effectively and accurately, especially for water bodies at larger scales. Consequently, this study provides an efficient and practical way for monitoring optically active and inactive water parameters, and future research should strongly consider the adoption of multispectral UAVs to retrieve and monitor spatiotemporal changes in multiple water quality parameters of medium-sized water bodies.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Geography location of Yuandang Lake: (

**a**) Jiangsu Province in China; (

**b**) Yuandang Lake; (

**c**) Satellite image of Yuandang Lake.

**Figure 2.**Set of calibration plate images captured using DJI P4 Multispectral UAV: (

**a**) RGB image; (

**b**) Blue band image; (

**c**) Green band image; (

**d**) Red band image; (

**e**) Red edge band image; (

**f**) NIR band image.

**Figure 6.**Stacked multispectral bands image of lakeside: (

**a**) Before correction; (

**b**) After correction.

**Figure 9.**Pearson Correlation of water parameters NH

_{3}-N, TP, and COD

_{Mn}with 84 spectral indices.

**Figure 10.**Pearson Correlation of water parameters temperature, DO, and EC with 84 spectral indices.

**Figure 11.**Pearson Correlation of water parameters pH, Turbidity, BGA, and Chl-a with 84 spectral indices.

Band | Wavelength Range (nm) |
---|---|

Blue (B1) | 450 ± 16 |

Green (B2) | 560 ± 16 |

Red (B3) | 650 ± 16 |

Red Edge (B4) | 730 ± 16 |

Near Infrared (B5) | 840 ± 16 |

Index | Formula | Index | Formula | Index | Formula |
---|---|---|---|---|---|

S1 | B1 | S30 | $\mathrm{B}2/\mathrm{B}1$ | S59 | $(\mathrm{B}4-\mathrm{B}3)/(\mathrm{B}4+\mathrm{B}3)$ |

S2 | B2 | S31 | $\mathrm{B}2/\mathrm{B}3$ | S60 | $(\mathrm{B}5-\mathrm{B}3)/(\mathrm{B}5+\mathrm{B}3)$ |

S3 | B3 | S32 | $\mathrm{B}2/\mathrm{B}4$ | S61 | $(\mathrm{B}5-\mathrm{B}3)/(\mathrm{B}1+\mathrm{B}2)$ |

S4 | B4 | S33 | $\mathrm{B}2/\mathrm{B}5$ | S62 | $(\mathrm{B}1-\mathrm{B}3)/\mathrm{B}2$ |

S5 | B5 | S34 | $\mathrm{B}3/\mathrm{B}1$ | S63 | $1.67-3.94\times \mathrm{ln}\left(\mathrm{B}1\right)+3.78\times \mathrm{ln}\left(\mathrm{B}2\right)$ |

S6 | $\mathrm{B}1-\mathrm{B}2$ | S35 | $\mathrm{B}3/\mathrm{B}2$ | S64 | $\left(\right(\mathrm{B}5-\mathrm{B}3)/(\mathrm{B}5+\mathrm{B}3\left)\right)\times (\mathrm{B}5/\mathrm{B}3)$ |

S7 | $\mathrm{B}1-\mathrm{B}3$ | S36 | $\mathrm{B}3/\mathrm{B}4$ | S65 | $2\times \mathrm{B}3-\mathrm{B}2-\mathrm{B}1-1.4\times \mathrm{B}2-\mathrm{B}3$ |

S8 | $\mathrm{B}1-\mathrm{B}4$ | S37 | $\mathrm{B}3/\mathrm{B}5$ | S66 | $(\mathrm{B}5-(\mathrm{B}3+\mathrm{B}2)/2)/(\mathrm{B}5+(\mathrm{B}3+\mathrm{B}2)/2)$ |

S9 | $\mathrm{B}1-\mathrm{B}5$ | S38 | $\mathrm{B}4/\mathrm{B}1$ | S67 | $(\mathrm{B}2-\mathrm{B}1)/(\mathrm{B}2+\mathrm{B}1)$ |

S10 | $\mathrm{B}2-\mathrm{B}3$ | S39 | $\mathrm{B}4/\mathrm{B}2$ | S68 | $(\mathrm{B}{2}^{2}-\mathrm{B}3\times \mathrm{B}1)/(\mathrm{B}{2}^{2}+\mathrm{B}3\times \mathrm{B}1)$ |

S11 | $\mathrm{B}2-\mathrm{B}4$ | S40 | $\mathrm{B}4/\mathrm{B}3$ | S69 | $(\mathrm{B}5/\mathrm{B}4)-1$ |

S12 | $\mathrm{B}2-\mathrm{B}5$ | S41 | $\mathrm{B}4/\mathrm{B}5$ | S70 | $\mathrm{B}2-\mathrm{B}3/\mathrm{B}2+\mathrm{B}2-\mathrm{B}1$ |

S13 | $\mathrm{B}3-\mathrm{B}4$ | S42 | $\mathrm{B}5/\mathrm{B}1$ | S71 | $(2\times \mathrm{B}2-\mathrm{B}3-\mathrm{B}1)/(2\times \mathrm{B}2+\mathrm{B}3+\mathrm{B}1)$ |

S14 | $\mathrm{B}3-\mathrm{B}5$ | S43 | $\mathrm{B}5/\mathrm{B}2$ | S72 | $\mathrm{B}2/(\mathrm{B}{3}^{\mathrm{a}}\times \mathrm{B}{1}^{1-\mathrm{a}})\mathrm{a}=0.667$ |

S15 | $\mathrm{B}4-\mathrm{B}5$ | S44 | $\mathrm{B}5/\mathrm{B}3$ | S73 | $(\mathrm{B}{2}^{2}-\mathrm{B}{1}^{2})/(\mathrm{B}{2}^{2}+\mathrm{B}{1}^{2})$ |

S16 | $\mathrm{B}1+\mathrm{B}2$ | S45 | $\mathrm{B}5/\mathrm{B}4$ | S74 | $\left(2.5\times \left(\mathrm{B}5-\mathrm{B}3\right)\right)/\left(\mathrm{B}5+2.4\times \mathrm{B}3+1\right)$ |

S17 | $\mathrm{B}1+\mathrm{B}3$ | S46 | $(\mathrm{B}1-\mathrm{B}2)/(\mathrm{B}1+\mathrm{B}2)$ | S75 | $(\mathrm{B}5-\mathrm{B}1)/(\mathrm{B}5+\mathrm{B}1)$ |

S18 | $\mathrm{B}1+\mathrm{B}4$ | S47 | $(\mathrm{B}1-\mathrm{B}3)/(\mathrm{B}1+\mathrm{B}3)$ | S76 | $0.441\times \mathrm{B}3-0.881\times \mathrm{B}2+0.385\times \mathrm{B}1+18.787$ |

S19 | $\mathrm{B}1+\mathrm{B}5$ | S48 | $(\mathrm{B}1-\mathrm{B}4)/(\mathrm{B}1+\mathrm{B}4)$ | S77 | $(\mathrm{B}5+\mathrm{B}2-2\times \mathrm{B}1)/(\mathrm{B}5+\mathrm{B}2+2\times \mathrm{B}1)$ |

S20 | $\mathrm{B}2+\mathrm{B}3$ | S49 | $(\mathrm{B}1-\mathrm{B}5)/(\mathrm{B}1+\mathrm{B}5)$ | S78 | $2\times \mathrm{B}2-\mathrm{B}3-\mathrm{B}1$ |

S21 | $\mathrm{B}2+\mathrm{B}4$ | S50 | $(\mathrm{B}2-\mathrm{B}3)/(\mathrm{B}2+\mathrm{B}3)$ | S79 | $(\mathrm{B}5/\mathrm{B}2)-1$ |

S22 | $\mathrm{B}2+\mathrm{B}5$ | S51 | $(\mathrm{B}2-\mathrm{B}4)/(\mathrm{B}2+\mathrm{B}4)$ | S80 | $(\mathrm{B}5-\mathrm{B}2)/(\mathrm{B}5+\mathrm{B}2)$ |

S23 | $\mathrm{B}3+\mathrm{B}4$ | S52 | $(\mathrm{B}2-\mathrm{B}5)/(\mathrm{B}2+\mathrm{B}5)$ | S81 | $(\mathrm{B}1-\mathrm{B}3)/\mathrm{B}2$ |

S24 | $\mathrm{B}3+\mathrm{B}5$ | S53 | $(\mathrm{B}3-\mathrm{B}4)/(\mathrm{B}3+\mathrm{B}4)$ | S82 | $\left(\right(\mathrm{B}5/\mathrm{B}4)-1)/((\mathrm{B}5/\mathrm{B}4)+1)$ |

S25 | $\mathrm{B}4+\mathrm{B}5$ | S54 | $(\mathrm{B}3-\mathrm{B}5)/(\mathrm{B}3+\mathrm{B}5)$ | S83 | $\left(\right(\mathrm{B}5/\mathrm{B}3)-1)/((\mathrm{B}5/\mathrm{B}3)+1)$ |

S26 | $\mathrm{B}1/\mathrm{B}2$ | S55 | $(\mathrm{B}4-\mathrm{B}5)/(\mathrm{B}4+\mathrm{B}5)$ | S84 | $(\mathrm{B}5-\mathrm{B}4)/(\mathrm{B}5+\mathrm{B}4)$ |

S27 | $\mathrm{B}1/\mathrm{B}3$ | S56 | $(\mathrm{B}2-\mathrm{B}1)/(\mathrm{B}2+\mathrm{B}1)$ | ||

S28 | $\mathrm{B}1/\mathrm{B}4$ | S57 | ${(\mathrm{B}3}^{-1}-{\mathrm{B}4}^{-1})\times \mathrm{B}5$ | ||

S29 | $\mathrm{B}1/\mathrm{B}5$ | S58 | ${(\mathrm{B}3}^{-1}-{\mathrm{B}4}^{-1})$ |

**Table 3.**Statistical analysis of water samples measurements data of three days. N represents the number of sampling points. Units are mg/L.

Date | NH_{3}-N (mg/L) | TP (mg/L) | COD_{Mn} (mg/L) | |
---|---|---|---|---|

5 August 2022 (N = 20) | Max | 1.1499 | 0.1694 | 7.5345 |

Min | 0.5809 | 0.0815 | 6.8058 | |

Mean | 0.7967 | 0.1227 | 7.1573 | |

SD | 0.1512 | 0.0250 | 0.2098 | |

8 August 2022 (N = 20) | Max | 1.0833 | 0.1632 | 5.9810 |

Min | 0.4037 | 0.0676 | 3.4296 | |

Mean | 0.7128 | 0.1201 | 5.3174 | |

SD | 0.1758 | 0.0298 | 0.5582 | |

10 August 2022 (N = 20) | Max | 0.7209 | 0.1986 | 6.3950 |

Min | 0.2658 | 0.1016 | 5.3114 | |

Mean | 0.4148 | 0.14998 | 6.0241 | |

SD | 0.1211 | 0.03413 | 0.3031 | |

All data | Max | 1.1499 | 0.1986 | 7.5345 |

Min | 0.2658 | 0.0676 | 3.4296 | |

Mean | 0.6394 | 0.1308 | 6.1657 | |

SD | 0.2235 | 0.0333 | 0.8656 |

**Table 4.**Statistical analysis of continuous water quality measurement data of three days. N represents the number of sampling points.

Date | Chl-a (ug/L) | BGA (ug/L) | Turbidity (NTU) | pH | NC (uS/cm) | DO (mg/L) | Temperature (°C) | |
---|---|---|---|---|---|---|---|---|

5 August 2022 (N = 1519) | Max | 64.080 | 89.330 | 412.000 | 9.980 | 738.000 | 9.040 | 39.061 |

Min | 0.480 | 0.270 | 4.850 | 7.930 | 12.000 | 5.930 | 32.654 | |

Mean | 13.753 | 12.286 | 140.631 | 9.004 | 591.214 | 7.687 | 35.504 | |

SD | 10.055 | 12.919 | 98.038 | 0.317 | 65.726 | 0.683 | 1.522 | |

8 August 2022 (N = 1767) | Max | 38.300 | 21.630 | 65.550 | 9.140 | 678.000 | 12.880 | 33.497 |

Min | 1.840 | 2.560 | 16.990 | 7.890 | 6.200 | 5.280 | 31.473 | |

Mean | 8.769 | 7.224 | 33.889 | 8.738 | 609.149 | 9.239 | 32.611 | |

SD | 3.948 | 2.852 | 8.608 | 0.238 | 65.002 | 1.410 | 0.441 | |

10 August 2022 (N = 1546) | Max | 88.330 | 78.710 | 119.260 | 9.610 | 707.000 | 19.070 | 34.925 |

Min | 1.280 | 1.000 | 11.890 | 8.130 | 6.300 | 5.030 | 30.839 | |

Mean | 14.908 | 12.239 | 37.503 | 8.923 | 630.293 | 9.305 | 33.197 | |

SD | 11.410 | 11.481 | 16.659 | 0.294 | 52.032 | 1.867 | 0.939 | |

All data | Max | 88.330 | 89.330 | 412.000 | 9.980 | 738.000 | 19.070 | 39.061 |

Min | 0.480 | 0.270 | 4.850 | 7.890 | 6.200 | 5.030 | 30.839 | |

Mean | 12.300 | 10.420 | 68.600 | 8.881 | 610.276 | 8.772 | 33.708 | |

SD | 9.300 | 10.172 | 74.279 | 0.304 | 63.331 | 1.590 | 1.618 |

**Table 5.**Multivariate regression results between multispectral band-derived features and the corresponding water quality data of NH

_{3}-N, TP, COD, Chl-a and BGA.

NH_{3}-N (mg/L) | TP (mg/L) | COD_{Mn} (mg/L) | Chl-a (ug/L) | BGA (ug/L) | |||
---|---|---|---|---|---|---|---|

RF | Training | R^{2} | 0.8798 | 0.8852 | 0.9526 | 0.9047 | 0.9271 |

RMSE | 0.0545 | 0.0078 | 0.1605 | 0.8272 | 0.6244 | ||

Testing | R^{2} | 0.5104 | 0.4454 * | 0.7777 * | 0.3330 * | 0.4312 | |

RMSE | 0.1211 | 0.0158 * | 0.3038 * | 2.2747 * | 1.7475 | ||

MAE | 0.0962 | 0.0123 * | 0.2478 * | 1.6845 * | 1.2739 | ||

GB | Training | R^{2} | 0.8421 | 0.7908 | 0.9454 | 0.6463 | 0.7322 |

RMSE | 0.0637 | 0.0105 | 0.1605 | 1.6011 | 1.2086 | ||

Testing | R^{2} | 0.5460 * | 0.3205 | 0.7351 | 0.3193 | 0.4468 | |

RMSE | 0.1204 * | 0.0162 | 0.4192 | 2.1301 | 1.6738 | ||

MAE | 0.0904 * | 0.0110 | 0.3641 | 1.5929 | 1.2613 | ||

BP | Training | R^{2} | 0.2768 | 0.3598 | 0.6510 | 0.2517 | 0.5772 |

RMSE | 0.1329 | 0.0191 | 0.4350 | 3.6743 | 2.5188 | ||

Testing | R^{2} | 0.1474 | 0.2871 | 0.7607 | 0.2060 | 0.5160 * | |

RMSE | 0.1481 | 0.0154 | 0.3295 | 3.9491 | 2.5766 * | ||

MAE | 0.1141 | 0.0132 | 0.2550 | 2.8076 | 1.8558 * | ||

CNN | Training | R^{2} | 0.2768 | 0.2953 | 0.6662 | 0.2029 | 0.4984 |

RMSE | 0.1329 | 0.0196 | 0.4227 | 3.9036 | 2.6807 | ||

Testing | R^{2} | 0.1475 | 0.2841 | 0.6397 | 0.1932 | 0.4331 | |

RMSE | 0.1481 | 0.0177 | 0.3862 | 3.6436 | 2.9746 | ||

MAE | 0.1142 | 0.0141 | 0.3066 | 2.6692 | 2.0371 |

**Table 6.**Multivariate regression results between multispectral band-derived features and the corresponding water quality data of Turbidity, pH, NC, DO and temperature.

Turbidity (NTU) | pH | NC (uS/cm) | DO (mg/L) | Temperature (°C) | |||
---|---|---|---|---|---|---|---|

RF | Training | R^{2} | 0.9090 | 0.9596 | 0.9426 | 0.9572 | 0.9592 |

RMSE | 19.5934 | 0.0382 | 4.5606 | 0.2195 | 0.2096 | ||

Testing | R^{2} | 0.3403 | 0.7262 * | 0.6449 * | 0.6728 * | 0.7071 | |

RMSE | 55.8828 | 0.0997 * | 11.1834 * | 0.6157 * | 0.5648 | ||

MAE | 27.3925 | 0.0670 * | 7.6322 * | 0.4104 * | 0.3430 | ||

GB | Training | R^{2} | 0.8014 | 0.8347 | 0.7744 | 0.8334 | 0.9061 |

RMSE | 28.7807 | 0.0758 | 9.0432 | 0.4425 | 0.4625 | ||

Testing | R^{2} | 0.3040 | 0.6706 | 0.5864 | 0.6161 | 0.7547 * | |

RMSE | 58.7329 | 0.1111 | 12.0692 | 0.6745 | 0.7402 * | ||

MAE | 30.6318 | 0.0808 | 8.6639 | 0.4847 | 0.4783 * | ||

BP | Training | R^{2} | 0.3609 | 0.6819 | 0.4924 | 0.5877 | 0.6807 |

RMSE | 52.1333 | 0.1073 | 24.6138 | 0.9955 | 0.8015 | ||

Testing | R^{2} | 0.3127 | 0.6383 | 0.4469 | 0.5434 | 0.6359 | |

RMSE | 55.5892 | 0.1140 | 28.0632 | 1.0763 | 0.8228 | ||

MAE | 31.2054 | 0.0831 | 18.1743 | 0.7440 | 0.5692 | ||

CNN | Training | R^{2} | 0.3925 | 0.3157 | 0.4141 | 0.4196 | 0.4397 |

RMSE | 48.4631 | 0.1570 | 27.7655 | 1.1796 | 1.0511 | ||

Testing | R^{2} | 0.3433 * | 0.3547 | 0.4033 | 0.3758 | 0.3792 | |

RMSE | 50.3219 * | 0.1529 | 28.3178 | 1.2641 | 1.1083 | ||

MAE | 27.2034 * | 0.1203 | 17.6918 | 0.9437 | 0.7495 |

Parameters | Models | Source | R^{2} | RMSE |
---|---|---|---|---|

COD_{Mn} | RF | Our study | 0.778 * | 0.304 * |

BP-RF | [26] | 0.270 | 0.770 | |

TP | RF | Our study | 0.445 | 0.016 |

BP | [26] | 0.430 | 0.053 | |

GA_XGBoost | [25] | 0.699 * | 0.034 * | |

NH_{3}-N | GB | Our study | 0.546 | 0.120 |

GA_XGBoost | [25] | 0.694 * | 0.163 * | |

Turbidity | GB | Our study | 0.343 | 50.322 |

GA_XGBoost | [25] | 0.597 * | 10.127 * | |

Chl-a | RF | Our study | 0.333 | 2.274 |

RF-XGB | [26] | 0.500 | 1.770 | |

GA_XGBoost | [25] | 0.855 * | 0.046 * | |

CNN | [28] | 0.790 | 8.770 |

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## Share and Cite

**MDPI and ACS Style**

Lo, Y.; Fu, L.; Lu, T.; Huang, H.; Kong, L.; Xu, Y.; Zhang, C.
Medium-Sized Lake Water Quality Parameters Retrieval Using Multispectral UAV Image and Machine Learning Algorithms: A Case Study of the Yuandang Lake, China. *Drones* **2023**, *7*, 244.
https://doi.org/10.3390/drones7040244

**AMA Style**

Lo Y, Fu L, Lu T, Huang H, Kong L, Xu Y, Zhang C.
Medium-Sized Lake Water Quality Parameters Retrieval Using Multispectral UAV Image and Machine Learning Algorithms: A Case Study of the Yuandang Lake, China. *Drones*. 2023; 7(4):244.
https://doi.org/10.3390/drones7040244

**Chicago/Turabian Style**

Lo, Ying, Lang Fu, Tiancheng Lu, Hong Huang, Lingrong Kong, Yunqing Xu, and Cheng Zhang.
2023. "Medium-Sized Lake Water Quality Parameters Retrieval Using Multispectral UAV Image and Machine Learning Algorithms: A Case Study of the Yuandang Lake, China" *Drones* 7, no. 4: 244.
https://doi.org/10.3390/drones7040244