# Simulation of Chlorophyll a Concentration in Donghu Lake Assisted by Environmental Factors Based on Optimized SVM and Data Assimilation

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}and having a mean depth of 2.16 m with a maximum depth of 4.66 m [51]. Donghu Lake is one of the largest urban lakes in China, being mainly composed of Guozheng Lake, Tangling Lake, Miaohu Lake, Tuanhu Lake, Houhu Lake, and Shuiguo Lake. The average annual water temperature is 16.7 °C, the annual potential evaporation is 1269.6 mm, and the average annual precipitation is 1180 mm. The precipitation is concentrated from April to July, accounting for about 60% of the annual precipitation [52].

#### 2.2. Support Vector Machine and Optimization Algorithms

#### 2.2.1. Support Vector Machine (SVM)

#### 2.2.2. Simulated Annealing (SA)

#### 2.2.3. Genetic Algorithm (GA)

#### 2.2.4. Artificial Bee Colony (ABC)

#### 2.2.5. Particle Swarm Optimization (PSO)

#### 2.3. Modeling and Simulation

#### 2.3.1. Data Pre-Processing

#### 2.3.2. Modeling

#### 2.3.3. Simulation and Model Analysis

## 3. Results and Discussion

#### 3.1. Data and Datasets

#### 3.2. Parameter Optimization and Modeling

#### 3.3. Simulation and Accuracy Comparison

#### 3.4. The Performance Analysis of the Col-SVM Model

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 4.**The comparison of chlorophyll a concentration distribution map in Donghu Lake. (

**a**) Chlorophyll a concentration distribution map based on observed values. (

**b**) Chlorophyll a concentration distribution map based on simulated values.

Dataset | Number of Sampling Points | Minimum (μg/L) | Maximum (μg/L) | Average Value (μg/L) |
---|---|---|---|---|

20171115 | 29 | 6.91 | 50.65 | 23.00 |

20171217 | 42 | 5.25 | 157.72 | 49.00 |

20180326 | 43 | 3.57 | 162.29 | 44.77 |

20181026 | 43 | 17.46 | 140.93 | 57.25 |

Dataset | Number of Sampling Points | Minimum (μg/L) | Maximum (μg/L) | Average Value (μg/L) |
---|---|---|---|---|

Training dataset | 28 | 3.71 | 159.49 | 48.37 |

Testing dataset | 15 | 3.57 | 162.29 | 38.06 |

Model | $\mathit{C}$ | $\mathit{\gamma}$ |
---|---|---|

SVM model | 55.82 | 0.02047 |

SA-SVM model | 15.27 | 0.5561 |

GA-SVM model | 14.67 | 0.5959 |

ABC-SVM model | 11.29 | 0.5316 |

PSO-SVM model | 0.84 | 0.1890 |

Model | RMSE (μg/L) | MAPE | NSE | Bias |
---|---|---|---|---|

SVM model | 20.64 | 0.5180 | 0.8397 | −0.06823 |

SA-SVM model | 11.11 | 0.1702 | 0.9536 | −0.02846 |

GA-SVM model | 10.88 | 0.1673 | 0.9555 | −0.02978 |

ABC-SVM model | 11.93 | 0.1714 | 0.9465 | −0.02605 |

PSO-SVM model | 20.52 | 0.4727 | 0.8413 | −0.06613 |

Model | RMSE (μg/L) | MAPE | NSE | Bias |
---|---|---|---|---|

SVM model | 19.09 | 0.8477 | 0.7725 | −0.02226 |

SA-SVM model | 9.78 | 0.9600 | 0.2878 | −0.01188 |

GA-SVM model | 9.61 | 0.9614 | 0.2885 | −0.01275 |

ABC-SVM model | 10.40 | 0.9548 | 0.2913 | −0.01101 |

PSO-SVM model | 18.11 | 0.8630 | 0.6271 | −0.00221 |

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**MDPI and ACS Style**

Tang, X.; Huang, M.
Simulation of Chlorophyll a Concentration in Donghu Lake Assisted by Environmental Factors Based on Optimized SVM and Data Assimilation. *Water* **2022**, *14*, 2353.
https://doi.org/10.3390/w14152353

**AMA Style**

Tang X, Huang M.
Simulation of Chlorophyll a Concentration in Donghu Lake Assisted by Environmental Factors Based on Optimized SVM and Data Assimilation. *Water*. 2022; 14(15):2353.
https://doi.org/10.3390/w14152353

**Chicago/Turabian Style**

Tang, Xiaodong, and Mutao Huang.
2022. "Simulation of Chlorophyll a Concentration in Donghu Lake Assisted by Environmental Factors Based on Optimized SVM and Data Assimilation" *Water* 14, no. 15: 2353.
https://doi.org/10.3390/w14152353