# Monitoring of Urban Black-Odor Water Using UAV Multispectral Data Based on Extreme Gradient Boosting

^{1}

^{2}

^{*}

## Abstract

**:**

^{2}= 0.99) and test dataset (R

^{2}= 0.94), and it achieved the best retrieval effect on image inversion in the shortest time, which made it the best-fit model compared with the RF regression (RFR) model and the SVR model. According to inversion results based on the XGBR model, there was only a small size of mild black-odor water in the study area, which showed the achievement of water pollution treatment in Guangzhou. The research provides a theoretical framework and technical feasibility for the application of the combination of algorithms and UAV-borne multispectral images in the field of water quality inversion.

## 1. Introduction

## 2. Methodology

#### 2.1. Framework for NCPI Inversion Model

#### 2.2. Study Area

#### 2.3. In Situ Data Collection

#### 2.4. Airborn Multispectral Imagery Preprocessing

#### 2.5. Spectral Data Preprocessing

#### 2.6. Modeling Approaches

#### 2.6.1. Nemerow Comprehensive Pollution Index

_{i}≤ 1 equals NBO; 1 < P

_{i}≤ 2 equals MBO; 2 < P

_{i}≤ 10 equals SBO, where P

_{i}stands for the NCPI of the i-th sample. The dimensionless linear relationship is shown in Figure 3. The horizontal axis node of the piecewise function is the classification threshold from Table 2.

_{max}represents the maximum of all the indices P

_{i}; W

_{i}indicates the weight of the water parameter in the i-th detection point; I

_{i}is the ratio of the i-th water quality parameter factor C

_{i}to its objective concentration S

_{i}. The objective concentrations are from different criteria of water quality [54]. S

_{DO}(5 mg/L) and S

_{AN}(1 mg/L) are both obtained from the class-Ⅲ water standard for surface water; S

_{SD}is set to 1.2 m according to the class A or B landscape-water standard. S

_{ORP}is decided as 50 mV from the classification standard of the pollution degree of black-odor water.

#### 2.6.2. Extreme Gradient Boosting Regression and Other Models

#### 2.6.3. Model Evaluation

^{2}), root mean square error (RMSE), and mean absolute error (MAE) were selected to evaluate the performance of models. These indicators are defined as follows:

^{2}ranges from 0 to 1. The closer its value is to 1, the stronger the interpretation ability of the input variables of the models to the inversion target. RMSE can reflect the deviation between retrieval values and real values and the value range of RMSE is (0, +∞). Its value will increase when the dispersion of the predicted value of the model is high. MAE is the mean of the absolute value of the error between the observed value and the predicted value. The value range of MAE is (0, +∞). The higher the value of MAE, the poorer the predictive performance of the model. Thus, a model with high R

^{2}, low RMSE, and low MAE is regarded as a qualified model for inversion.

## 3. Results

#### 3.1. In Situ Data Analysis

#### 3.2. Model Optimization and Accuracy Evaluation

^{2}of models with different parameters was shown in Figure 5. The iteration number was determined as 3250 based on Figure 5b. With the purpose of avoiding the over-fitting phenomenon and improving the model’s generalization ability, the value of gamma cannot be too small while the learning rate eta should be reduced. After the adjustment above, the regression model with parameters (gamma = 0.001, eta = 0.3, the iteration number = 3250) was selected as the inversion model. On the training data, R

^{2}was 0.99, RMSE was 0.01, and MAE was 0.01. On the test data set, R

^{2}was lower than that of the train data set with a value of 0.94 and both RMSE and MAE increased a little, in which case, the XGBR model l had remarkable generalization ability. It is worth noting that the difficulty of adjusting parameters is relatively low and the model is not easy to overfit.

^{2}of the training data set and the test data set reached 0.96 or more and, in which case, the performance of the SVR model was nearly the same as that of the XGBR model. However, compared with the XGBoost algorithm, it is difficult to generate an optimal model using the SVR due to the significant time cost and overfitting problem.

^{2}both reached 0.87, which was lower than those of the other two models. The RMSE of the RFR model is the largest among the 3 models. Thus, the performance of the RFR model is inferior to that of XGBR.

^{2}and lowest error rate including RMSE and MAE. The prediction accuracy of its test data set was comparable to that of the train data set. For the train data set, the RMSE and MAE of the SVR model were higher than those of the XGBR model despite the fact that they have the same value of R

^{2}. For the test data set, the prediction accuracy of the SVR model was slightly lower than that of the XGBR model. The retrieval accuracy of RFR model was significantly lower than that of the XGBR model since the RFR model had the lowest R

^{2}and highest RMSE on the train data set and test data set. Thus, both the XGBR model and SVR model can achieve higher inversion accuracy while the RFR model is the most inferior.

#### 3.3. UAV-Borne Image Inversion Based on Three Models

^{2}greater than 0.87 are qualified for inversion on UAV-borne multispectral images. The spectral average of a 5 × 5 pixel matrix was input into models to derive NCPI. Since true real values of NCPI range from 0.76 to 1.50, the inversion of effect will be considered poor if the predicted value is negative.

## 4. Discussion

^{2}= 0.36, RMSE = 0.10, MAE = 0.06) and test data set (R

^{2}= 0.40, RMSE = 0.25, MAE = 0.15) is much lower than that of machine learning algorithms (RFR, SVR, XGBR). The conclusion is the same as the result of Wei et al. [54]. For variables with weak linear correlation, XGBR is more applicable for establishing an inversion model of water quality since it enables to explore nonlinear relationships.

## 5. Conclusions

^{2}on the training and test dataset both reached 0.94 or higher. The RMSE was 0.01 and 0.09, respectively and the values of MAE were both lower than 0.07. The SVR model was the second best-fitting model with high values of R

^{2}on the training dataset (R

^{2}= 0.99) and test dataset (R

^{2}= 0.92). The RFR model performed worst since it had the lowest R

^{2}on the training dataset (R

^{2}= 0.87) and test dataset (R

^{2}= 0.87).

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Framework of the NCPI inversion model developed in the study. (

**a**) Step 1. (

**b**) Step 2. (

**c**) Step 3. (

**d**) Step 4.

**Figure 2.**Sampling points in study area. (

**a**) North Lake. (

**b**) West Lake and Middle Lake (left); East Lake (right). (

**c**) Chebei River.

**Figure 5.**The change in R

^{2}based on XGBR model as the number iterations increases. (

**a**) gamma = 0.001, eta = 0.6; (

**b**) gamma = 0.001, eta = 0.3.; (

**c**) gamma = 0.1, eta = 0.3.

**Figure 6.**Scatter plot of the observed values and predicted values of NCPI using 3 regression models. (

**a**) XGBR; (

**b**) SVR; (

**c**) RFR.

**Figure 7.**Inversion map of NCPI on the basis of UAV-borne images and 3 different models. (

**a**) XGBR; (

**b**) SVR; (

**c**) RFR.

**Figure 8.**Shadow and rare riverbed in inversion map based on the XGBR model. (

**a**) North Lake. (

**b**) West Lake and Middle Lake (left); East Lake (right). (

**c**) Chebei River.

Band Combination (BC) | Band Math | Reference | Band Combination (BC) | Band Math | Reference |
---|---|---|---|---|---|

BC1 | B1/B2 | Simple ratio | BC15 | B4/B3 | Simple ratio |

BC2 | B1/B3 | Simple ratio | BC16 | B4/B5 | Simple ratio |

BC3 | B1/B4 | Simple ratio | BC17 | B5/B1 | Simple ratio |

BC4 | B1/B5 | Simple ratio | BC18 | B5/B2 | Simple ratio |

BC5 | B2/B1 | Simple ratio | BC19 | B5/B3 | Simple ratio |

BC6 | B2/B3 | Simple ratio | BC20 | B5/B4 | Simple ratio |

BC7 | B2/B4 | Simple ratio | BCB1 | (B2 − B1)/(B2 + B1) | Normalized indices |

BC8 | B2/B5 | Simple ratio | 3BDA | (B3^{−1} − B4^{−1}) ×B5 | [59] |

BC9 | B3/B1 | Simple ratio | 3BDA_MOD | (B3^{−1} − B4^{−1}) | [60] |

BC10 | B3/B2 | Simple ratio | NDCI | (B4 − B3)/(B4 + B3) | [61] |

BC11 | B3/B4 | Simple ratio | NDVI | (B5 − B3)/(B5 + B3) | [61] |

BC12 | B3/B5 | Simple ratio | SABI | (B5 − B3)/(B1 + B2) | [62] |

BC13 | B4/B1 | Simple ratio | KIVU | (B1 − B3)/B2 | [63] |

BC14 | B4/B2 | Simple ratio | Kab1 | 1.67 − 3.94 × ln(B1) + 3.78 × ln(B2) | [64] |

Characteristic Index | Mild | Severe |
---|---|---|

SD(cm) | 25—10 | <10 |

DO(mg/L) | 0.2—2.0 | <0.2 |

ORP(mV) | −200—50 | <−200 |

AN(mg/L) | 8.0—15 | >15 |

Modeling Method | Training Data | Test Data | ||||
---|---|---|---|---|---|---|

R^{2} | RMSE | MAE | R^{2} | RMSE | MAE | |

RFR | 0.87 | 0.09 | 0.05 | 0.87 | 0.10 | 0.05 |

SVR | 0.99 | 0.02 | 0.02 | 0.92 | 0.09 | 0.09 |

XGBR | 0.99 | 0.01 | 0.01 | 0.94 | 0.09 | 0.07 |

Modeling Method | Computing Time (s) | Max Value | Min Value |
---|---|---|---|

In-situ Measurement | — | 1.50 | 0.76 |

RFR | 130.7 | 1.31 | 0.74 |

SVR | 109.4 | 0.92 | 0.82 |

XGBR | 88.1 | 1.51 | 0.62 |

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## Share and Cite

**MDPI and ACS Style**

Wang, F.; Hu, H.; Luo, Y.; Lei, X.; Wu, D.; Jiang, J. Monitoring of Urban Black-Odor Water Using UAV Multispectral Data Based on Extreme Gradient Boosting. *Water* **2022**, *14*, 3354.
https://doi.org/10.3390/w14213354

**AMA Style**

Wang F, Hu H, Luo Y, Lei X, Wu D, Jiang J. Monitoring of Urban Black-Odor Water Using UAV Multispectral Data Based on Extreme Gradient Boosting. *Water*. 2022; 14(21):3354.
https://doi.org/10.3390/w14213354

**Chicago/Turabian Style**

Wang, Fangyi, Haiying Hu, Yunru Luo, Xiangdong Lei, Di Wu, and Jie Jiang. 2022. "Monitoring of Urban Black-Odor Water Using UAV Multispectral Data Based on Extreme Gradient Boosting" *Water* 14, no. 21: 3354.
https://doi.org/10.3390/w14213354