# Deep BLSTM-GRU Model for Monthly Rainfall Prediction: A Case Study of Simtokha, Bhutan

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## Abstract

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## 1. Introduction

- We proposed a hybrid framework of BLSTM and GRU for rainfall prediction.
- No prior deep learning techniques have been used on the dataset. The results of this paper will serve as the baseline for future researchers.
- A detailed analysis of the proposed framework is presented through extensive experiments.
- Finally, a comparison with different deep learning models is also discussed.

## 2. Literature Review

## 3. Proposed System

#### 3.1. Dataset Description

#### 3.2. Data Preprocessing

#### 3.3. Evaluation Metrics

#### 3.4. BLSTM

#### 3.5. GRU

#### 3.6. BLSTM-GRU Model

## 4. Experiment and Results

#### 4.1. Result Summary

#### 4.2. Comparative Analysis

## 5. Conclusions and Future Work

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**The proposed model is composed of 7 layers including the input and output layers. The embedding is generated by the Bidirectional Long Short Term Memory (BLSTM) and Gated Recurrent Unit (GRU) layer. The batch normalization is used for normalizing the data, and the dense layer performs the prediction.

**Figure 2.**Map of Bhutan showing major river basins and the annual precipitation (mm). The study area is indicated in the legend.

**Figure 4.**Data preprocessing. The data are preprocessed in 6 stages with the arrowheads showing the flow of data.

**Figure 5.**Many (12) to One LSTM utilized in the experiment. Each sample of data contains 12 time-steps of previous data. We used 12 months of previous data to predict the rainfall of the next month (n + 1).

**Figure 6.**RMSE and MSE values of 6 existing models including linear regression and the proposed model. It is clear from the figure that our model outperformed the existing models for the same task.

**Figure 7.**The plots of actual monthly rainfall values over Simtokha collected from NCHM and predicted rainfall values for the years 2016 and 2017, where the x-axis and y-axis represent months and monthly rainfall values (scaled) respectively. The blue line shows the actual values and the orange line shows the predicted values. Subfigure ‘a’ shows that CNN is not able to predict the peak monthly rainfall values correctly. Results of recurrent neural networks shown by subfigure ‘b’, ‘c’ and ‘d’ are better than that of MLP (subfigure ‘e’). Subfigure ‘f’ shows that the proposed model is able to generalize better and gives the best output.

**Figure 9.**Pearson Correlation Coefficient values of 5 existing deep learning models and our proposed model. The score of the proposed model was the highest among the models.

Author & Year | Region (Global or Local) | Daily- Monthly- Yearly | Types of NN | Rainfall Predicting Variables | Accuracy Measure |
---|---|---|---|---|---|

Luk et al. [12] | Western Sydney | 15 min rainfall prediction | MLFN, PRNN, TDNN | NA | NMSE |

Huang et al. [11] | Bangkok | 4 years of hourly data from 1997–2003 | MLP and FFNN | NA | Efficiency index (EI) |

Abhishek et al. [4] | Karnataka, India | 8 months of data from 1960 to 2010 | BPFNN, BPA, LRN and CBP | Average humidity and average wind speed | MSE |

Nayak et al. [10] | Survey Paper | NA | ANN | NA | NA |

Darjee et al. [5] | Survey Paper | Monthly, Yearly | ANN, (FFNN, RNN, TDNN) | Maximum and minimum temperatures | NA |

Hardwinarto et al. [18] | East Kalimantan- Indonesia | Data used from 1986–2008 | BPNN | NA | MSE |

Khajure et al. [15] | NA | Daily records for 5 years | NN and a fuzzy inference system | Temperature, humidity, dew point, visibility, pressure and windspeed. | MSE |

Kumar and Tyagi [19] | Nilgiri district Tamil Nadu, India | Monthly rain- fall prediction (Data from 1972–2002) | BPNN, RBFNN | NA | MSE |

Wahyuni et al. [16] | Tengger East Java | Data used from 2005 to 2014 | BPNN | Changes caused by climate change | RMSE |

Kashiwao et al. [13] | Japan | Rainfall data from the in- ternet as \“big data” was used. | ANN MLP and RBFN | Atm. pressure, precipitation, humidity, temp., vapor pressure, wind, velocity. | Validation using JMA. |

Mishra et al. [17] | North India | North India for the period 1871–2012. | FFNN | Rainfall records of previous 2 months and current month | Regression analysis, MRE and MSE |

Rainfall Parameters | Units |
---|---|

Maximum Temperature (${t}_{max}$) | ${}^{\circ}$C |

Minimum Temperature (${t}_{min}$) | ${}^{\circ}$C |

Rainfall | Millimeters (mm) |

Relative Humidity | Percentage (%) |

Sunshine | Hours (h) |

Wind Speed | Meters per second (m/s) |

Name | Formula |
---|---|

MSE | $\frac{1}{n}{\sum}_{t=1}^{n}{({x}_{i}-{y}_{i})}^{2}$ |

RMSE | $\sqrt{\frac{1}{n}{\sum}_{t=1}^{n}{({x}_{i}-{y}_{i})}^{2}}$ |

${R}^{2}$ | $1-\frac{{\sum}_{i=0}^{{n}_{\mathrm{samples}}-1}{({x}_{i}-{y}_{i})}^{2}}{{\sum}_{i=0}^{{n}_{\mathrm{samples}}-1}{({x}_{i}-\overline{y})}^{2}}$ |

Correlation | $\frac{{\sum}_{i=1}^{n}({x}_{i}-\overline{x})({y}_{i}-\overline{y})}{\sqrt{{\sum}_{i=1}^{n}{({x}_{i}-\overline{x})}^{2}{\sum}_{i=1}^{n}{({y}_{i}-\overline{y})}^{2}}}$ |

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**MDPI and ACS Style**

Chhetri, M.; Kumar, S.; Pratim Roy, P.; Kim, B.-G.
Deep BLSTM-GRU Model for Monthly Rainfall Prediction: A Case Study of Simtokha, Bhutan. *Remote Sens.* **2020**, *12*, 3174.
https://doi.org/10.3390/rs12193174

**AMA Style**

Chhetri M, Kumar S, Pratim Roy P, Kim B-G.
Deep BLSTM-GRU Model for Monthly Rainfall Prediction: A Case Study of Simtokha, Bhutan. *Remote Sensing*. 2020; 12(19):3174.
https://doi.org/10.3390/rs12193174

**Chicago/Turabian Style**

Chhetri, Manoj, Sudhanshu Kumar, Partha Pratim Roy, and Byung-Gyu Kim.
2020. "Deep BLSTM-GRU Model for Monthly Rainfall Prediction: A Case Study of Simtokha, Bhutan" *Remote Sensing* 12, no. 19: 3174.
https://doi.org/10.3390/rs12193174