# Construction of an Integrated Drought Monitoring Model Based on Deep Learning Algorithms

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}and is the largest province in China in terms of land area [31]. The province has a variety of landforms, with the Altai Mountains in the north, the Kunlun Mountains-Alishan in the south, the Tianshan Mountains running through the central part of Xinjiang, and the Junger Basin and Tarim Basin between the three mountains (Figure 1). Due to its geographical location and topography, Xinjiang has a typical temperate continental climate, with short summers and long cold winters, and large differences between the minimum and maximum temperatures. Due to the scarcity of water resources and low rainfall, drought has become a major natural disaster in Xinjiang.

#### 2.2. Data

#### 2.2.1. MODIS Data

#### 2.2.2. CHIRPS Data

#### 2.2.3. GLDAS Data

#### 2.2.4. Meteorological Station Data

#### 2.3. Baseline Model

#### 2.3.1. Machine Learning Models

#### 2.3.2. Deep Forwarded Neural Network (DFNN)

#### 2.3.3. Convolutional Neural Network (CNN)

#### 2.3.4. Long Short-Term Memory (LSTM)

#### 2.4. Data Processing

#### 2.5. Convolutional Long Short-Term Memory (ConvLSTM)

_{t}and O

_{t}are the input and output gates, ft is the forget gate, C

_{t}is the memory cell, W represents the weight between connected neurons, C

_{t}is the cumulative state information, and σ is the sigmoid non-linear activation function that maps the output to a 0 to 1 distribution for easy convergence. A more detailed explanation of ConvLSTM can be found in [55].

#### 2.6. The Process of Building the Model

^{2}), root-mean-square errors (RMSEs), and mean absolute errors (MAEs) of the seven models were calculated to assess the performances of the models. The relative importance of each drought influence factor on the CDI was then analyzed. Finally, the spatial distribution of CDI-6 was plotted based on the drought events in a typical drought year to spatially validate the integrated drought monitoring model. A detailed description of the model construction process is shown in Figure 3.

#### 2.7. Assessment Indicators

^{2}), the root-mean-square error (RMSE), and the mean absolute error (MAE).

^{2}is generally used to assess the degree of conformity between the predicted and actual values, RMSE is used to measure the deviation between the predicted and actual values of the model deviation, and MAE can reflect the actual situation of the predicted value error [56]. x

_{i}denotes the CDI value of the model output, y

_{i}denotes the SPEI value, $\overline{x}$ denotes the mean of the CDI, $\overline{y}$ denotes the mean of the SPEI, and m denotes the sample size. R

^{2}values closer to 1 and RMSE and MAE values closer to 0 indicate better model performance.

#### 2.8. Correlation of a Single Remote Sensing Drought Index with Station SPEI

^{2}) between each single remote sensing drought index and the station drought index SPEI were calculated to analyze the ability of a single remote sensing drought index to monitor drought and the need to integrate data from multiple sources. The results are shown in Table 4. All remote sensing drought indices showed positive correlations with SPEI in general, with PCI having the highest correlation with SPEI-1, indicating that PCI is the most sensitive in monitoring short-term drought, and R

^{2}decreased with increasing SPEI time scales. SMCI had the highest R

^{2}value with SPEI-1 compared to other remote sensing indicators, suggesting that SMCI provides reliable information for monitoring short-term meteorological and agricultural drought. Similar to PCI, the correlation between TCI and SPEI-1 was higher than that of SPEI at other scales. In addition, the R

^{2}value between VCI and SPEI-12 was higher than that between VCI and SPEI at other scales, suggesting that drought information reflected in VCI has a longer lag time than that reflected in SMCI. LAI, ET, VHI, and VSWI also have the same pattern as VCI.

#### 2.9. Calibration of the Model

## 3. Results

#### 3.1. Comparison of Simulation Accuracy of Seven Models

^{2}of 0.874, an RMSE of 0.365, and an MAE of 0.265 between the CDI and the SPEI. The ConvLSTM also exhibited the highest monitoring accuracy at the 1-, 3-, and 6-month scales. Therefore, the ConvLSTM model had an advantage over its counterparts and met the best model criteria. In addition, CDI showed higher monitoring performance on the 12-month scale than on the 6-, 3-, or 1-month scales. This result may be due to the smoother time series in SPEI-12 compared to SPEI-6, SPEI-3, and SPEI-1, resulting in higher monitoring accuracy.

#### 3.2. Drought Consistency Analysis

#### 3.3. Correlation Analysis Based on Meteorological Drought Indices

#### 3.4. Correlation Analysis Based on Relative Soil Moisture

#### 3.5. Validation of the Spatial Distribution of Drought Development in a Typical Dry Year

#### 3.6. Relative Importance of Different Influencing Factors on Simulation Results

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Figure A1.**(

**a**) Architecture of the DFNN model used in this study; (

**b**) CNN model architecture; (

**c**) architecture of the LSTM model; and (

**d**) overall structure of the ConvLSTM network.

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**Figure 1.**Map of the study area. (

**a**) Elevation map and locations of meteorological stations. (

**b**) Land cover map in 2010.

**Figure 2.**ConvLSTM internal network architecture. X, H, C, i, f, and o are the input sequence, hidden state, memory cell, input gate, forget gate, and output gate, respectively.

**Figure 4.**Scatter plots at four scales (

**a**) SPI-1, (

**b**) SPI-3, (

**c**) SPI-6, (

**d**) SPI-12 with the corresponding scale CDI output from this model.

**Figure 6.**(

**a**–

**f**) are Spatial distribution of CDI-6 from ConvLSTM model with station drought distribution during March–August 2014.

Data Sources | Data Type | Variables | Temporal Resolution | Spatial Resolution | Coverage |
---|---|---|---|---|---|

MODIS | MOD13A1 | NDVI | 16 days | 500 m | Global |

MOD16A2 | ET | 8 days | 500 m | Global | |

MOD11A1 | LST | daily | 1000 m | Global | |

MOD15A2H | LAI | 8 days | 500 m | Global | |

UCSB-CHG | CHIRPS | Precipitation | Monthly | 0.25° × 0.25° | Global |

GLDAS | GLDAS-2.1 | Soil moisture | Monthly | 0.25° × 0.25° | Global |

Type of Variable | Factors | Drought Index | Formula | References |
---|---|---|---|---|

Independent variables | Precipitation | PCI | $\mathrm{PCI}=\frac{{\mathrm{P}}_{\mathrm{i}}-{\mathrm{P}}_{\mathrm{m}\mathrm{i}\mathrm{n}}}{{\mathrm{P}}_{\mathrm{m}\mathrm{a}\mathrm{x}}-{\mathrm{P}}_{\mathrm{m}\mathrm{i}\mathrm{n}}}$ (where P _{i} is the monthly precipitation and P_{max} and P_{min} are the monthly maximum and minimum precipitation) | [47] |

Vegetation | VCI | $\mathrm{V}\mathrm{C}\mathrm{I}=\frac{\mathrm{N}\mathrm{D}\mathrm{V}{\mathrm{I}}_{\mathrm{i}}-\mathrm{N}\mathrm{D}\mathrm{V}{\mathrm{I}}_{\mathrm{m}\mathrm{i}\mathrm{n}}}{\mathrm{N}\mathrm{D}\mathrm{V}{\mathrm{I}}_{\mathrm{m}\mathrm{a}\mathrm{x}}-\mathrm{N}\mathrm{D}\mathrm{V}{\mathrm{I}}_{\mathrm{m}\mathrm{i}\mathrm{n}}}$ (where NDVI _{i} is the monthly NDVI value and NDVI_{min} and NDVI_{max} are the monthly minimum and maximum NDVI values) | [48] | |

VHI | VHI = αVCI + (1 − α) TCI (α denotes a constant value set to 0.5) | [49] | ||

VSWI | $\mathrm{V}\mathrm{S}\mathrm{W}\mathrm{I}=\frac{\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}}{\mathrm{LST}}$ | [50] | ||

Temperature | TCI | $\mathrm{T}\mathrm{C}\mathrm{I}=\frac{\mathrm{L}\mathrm{S}{\mathrm{T}}_{\mathrm{i}}-\mathrm{L}\mathrm{S}{\mathrm{T}}_{\mathrm{m}\mathrm{i}\mathrm{n}}}{\mathrm{L}\mathrm{S}{\mathrm{T}}_{\mathrm{m}\mathrm{a}\mathrm{x}}-\mathrm{L}\mathrm{S}{\mathrm{T}}_{\mathrm{m}\mathrm{i}\mathrm{n}}}$ (where LST _{i} is the monthly LST value and LST_{max} and LST_{min} are the monthly maximum and minimum values) | [51] | |

Soil | SMCI | $\mathrm{S}\mathrm{M}\mathrm{C}\mathrm{I}=\frac{\mathrm{S}{\mathrm{M}}_{\mathrm{i}}-\mathrm{S}{\mathrm{M}}_{\mathrm{m}\mathrm{i}\mathrm{n}}}{\mathrm{S}{\mathrm{M}}_{\mathrm{m}\mathrm{a}\mathrm{x}}-\mathrm{S}{\mathrm{M}}_{\mathrm{m}\mathrm{i}\mathrm{n}}}$ (where SM _{i} is the monthly SM value SM_{min} and SM_{max} are the monthly minimum and maximum SM values) | [52] | |

Dependent variables | SPEI-1 SPEI-3 SPEI-6 SPEI-12 | $\mathrm{w}-\frac{{\mathrm{c}}_{0}+{\mathrm{c}}_{1}\mathrm{w}+{\mathrm{c}}_{2}{\mathrm{w}}^{2}}{1+{\mathrm{d}}_{1}\mathrm{w}+{\mathrm{d}}_{2}{\mathrm{w}}^{2}+{\mathrm{d}}_{3}{\mathrm{w}}^{3}}$ (w is defined as climatic water balance calculated based on the difference between precipitation and reference evapotranspiration, and c _{0}, c_{1}, c_{2}, d_{1}, d_{2}, and d_{3} are constants.) | [53] |

Drought Grade | Drought Condition | SPEI |
---|---|---|

I | No drought | −0.5 < SPEI |

II | Light drought | −1.0 < SPEI ≤ −0.5 |

III | Moderate drought | −1.5 < SPEI ≤ −1.0 |

IV | Severe drought | −2.0 < SPEI ≤ −1.5 |

V | Extreme drought | SPEI ≤ −2.0 |

**Table 4.**Correlation coefficient (R

^{2}) values of individual remotely sensed drought indices with different time scales of SPEI.

VCI | TCI | PCI | VSWI | LAI | ET | SMCI | VHI | |
---|---|---|---|---|---|---|---|---|

SPEI-1 | 0.082 | 0.362 | 0.581 | 0.065 | 0.079 | 0.035 | 0.412 | 0.114 |

SPEI-3 | 0.131 | 0.344 | 0.542 | 0.088 | 0.117 | 0.046 | 0.396 | 0.145 |

SPEI-6 | 0.232 | 0.238 | 0.421 | 0.149 | 0.184 | 0.059 | 0.367 | 0.189 |

SPEI-12 | 0.261 | 0.172 | 0.311 | 0.196 | 0.227 | 0.121 | 0.302 | 0.238 |

Parameter | DFNN Value | CNN Value | LSTM Value | ConvLSTM Value |
---|---|---|---|---|

Layers | 6 | 9 | 6 | 11 |

Batch size | 10 | 10 | 10 | 10 |

Epochs | 500 | 200 | 200 | 200 |

Learning rate | 0.001 | 0.001 | 0.001 | 0.001 |

Pool size | — | 1 | — | 2 |

Dropout | 0.2 | 0.2 | 0.2 | 0.2 |

Optimization | Adam | Adam | Adam | Adam |

Loss function | MSE | MSE | MSE | MSE |

Activation function | Relu | Relu | Relu | Relu |

Metrics | MAE | MAE | MAE | MAE |

Model | Index | SPEI-1 | SPEI-3 | SPEI-6 | SPEI-12 |
---|---|---|---|---|---|

RF | R^{2} | 0.227 | 0.564 | 0.624 | 0.751 |

RMSE | 0.996 | 0.722 | 0.971 | 0.515 | |

MAE | 0.809 | 0.561 | 0.522 | 0.398 | |

SVM | R^{2} | 0.078 | 0.498 | 0.547 | 0.681 |

RMSE | 1.034 | 0.791 | 0.739 | 0.592 | |

MAE | 0.824 | 0.598 | 0.569 | 0.451 | |

XGBoost | R^{2} | 0.132 | 0.516 | 0.598 | 0.726 |

RMSE | 1.016 | 0.781 | 0.668 | 0.559 | |

MAE | 0.878 | 0.572 | 0.531 | 0.422 | |

DFNN | R^{2} | 0.322 | 0.583 | 0.632 | 0.801 |

RMSE | 0.868 | 0.716 | 0.633 | 0.432 | |

MAE | 0.692 | 0.554 | 0.499 | 0.344 | |

CNN | R^{2} | 0.371 | 0.577 | 0.693 | 0.827 |

RMSE | 0.848 | 0.719 | 0.568 | 0.414 | |

MAE | 0.659 | 0.558 | 0.433 | 0.321 | |

LSTM | R^{2} | 0.359 | 0.559 | 0.686 | 0.819 |

RMSE | 0.855 | 0.725 | 0.590 | 0.421 | |

MAE | 0.671 | 0.562 | 0.449 | 0.331 | |

ConvLSTM | R^{2} | 0.423 | 0.613 | 0.723 | 0.874 |

RMSE | 0.812 | 0.671 | 0.561 | 0.365 | |

MAE | 0.623 | 0.522 | 0.424 | 0.265 |

Consistency Rate | SPEI-1 | SPEI-3 | SPEI-6 | SPEI-12 |
---|---|---|---|---|

No drought | 86.45% | 88.58% | 92.36% | 97.01% |

Light drought | 96.73% | 82.03% | 83.86% | 97.67% |

Moderate drought | 84.62% | 92.69% | 97.67% | 97.12% |

Severe drought | 58.13% | 68.86% | 95.12% | 96.51% |

Extreme drought | 35.56% | 44.81% | 76.82% | 66.46% |

Station Code | Station Name | Latitude (°N) | Longitude (°E) | Elevation (m) |
---|---|---|---|---|

51133 | Tacheng | 83.00 | 46.73 | 534.9 |

51379 | Jitai | 89.57 | 44.02 | 793.5 |

51431 | Yining | 81.33 | 43.95 | 662.5 |

51436 | Xinyuan | 83.30 | 43.45 | 928.2 |

51437 | Zhaosu | 81.13 | 43.15 | 1851 |

51656 | Korla | 86.13 | 41.75 | 931.5 |

51777 | Ruoqiang | 88.17 | 39.03 | 887.7 |

51811 | Shache | 77.27 | 38.43 | 1231.2 |

51931 | Yutian | 81.65 | 36.85 | 1422 |

Drought Grade | Drought Condition | CDI |
---|---|---|

I | No drought | 0 < CDI |

II | Light drought | −0.5 < CDI ≤ 0 |

III | Moderate drought | −1 < CDI ≤ −0.5 |

IV | Severe drought | −1.5 < CDI ≤ −1 |

V | Extreme drought | CDI ≤ −1.5 |

Impact Factors | Relative Importance (%) | |||
---|---|---|---|---|

CDI-1 | CDI-3 | CDI-6 | CDI-12 | |

PCI | 28.52 | 22.93 | 34.77 | 40.61 |

TCI | 17.81 | 14.73 | 13.38 | 12.33 |

VCI | 8.85 | 21.49 | 11.96 | 8.65 |

VHI | 19.48 | 14.84 | 11.85 | 11.68 |

VSWI | 6.06 | 7.05 | 7.88 | 8.58 |

LAI | 4.41 | 6.26 | 7.81 | 6.31 |

SMCI | 10.53 | 8.45 | 7.76 | 5.95 |

ET | 4.34 | 4.25 | 4.59 | 5.89 |

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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Zhang, Y.; Xie, D.; Tian, W.; Zhao, H.; Geng, S.; Lu, H.; Ma, G.; Huang, J.; Choy Lim Kam Sian, K.T.
Construction of an Integrated Drought Monitoring Model Based on Deep Learning Algorithms. *Remote Sens.* **2023**, *15*, 667.
https://doi.org/10.3390/rs15030667

**AMA Style**

Zhang Y, Xie D, Tian W, Zhao H, Geng S, Lu H, Ma G, Huang J, Choy Lim Kam Sian KT.
Construction of an Integrated Drought Monitoring Model Based on Deep Learning Algorithms. *Remote Sensing*. 2023; 15(3):667.
https://doi.org/10.3390/rs15030667

**Chicago/Turabian Style**

Zhang, Yonghong, Donglin Xie, Wei Tian, Huajun Zhao, Sutong Geng, Huanyu Lu, Guangyi Ma, Jie Huang, and Kenny Thiam Choy Lim Kam Sian.
2023. "Construction of an Integrated Drought Monitoring Model Based on Deep Learning Algorithms" *Remote Sensing* 15, no. 3: 667.
https://doi.org/10.3390/rs15030667