# Improved Monthly and Seasonal Multi-Model Ensemble Precipitation Forecasts in Southwest Asia Using Machine Learning Algorithms

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## Abstract

**:**

## 1. Introduction

## 2. Data

## 3. Methods

## 4. Results and Discussion

#### 4.1. Monthly Forecasts

#### 4.2. Seasonal Forecasts

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

ANN | artificial neural network |

DJF | December, January, February (winter) |

ERA5 | ECMWF re-analysis dataset 5 |

JJA | June, July, August (summer) |

KGE | Kling–Gupta efficiency coefficient |

MAM | March, April, May (spring) |

NMME | North American multi-model ensemble |

NSE | Nash–Sutcliffe efficiency coefficient |

RF | Random forest |

RMSE | Root mean squared error |

SON | September, October, November (autumn) |

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**Figure 2.**General architecture of a single (hidden) layer perceptron neural network. f is the activation function.

**Figure 13.**The values of RMSE during the DJF for test data: (

**d**–

**f**) have the same range of RMSE in the color bar, unlike the other models in (

**a**–

**c**).

Models | Abbreviation | Members | Lead Times | Hindcast Period |
---|---|---|---|---|

GEM-NEMO | NEMO | 10 | 12 (0.5–11.5 months) | 1981–2018 |

NASA-GEOSS2S | NASA | 4 | 9 (0.5–8.5 months) | 1981–2017 |

CanCM4i | CanCM4i | 10 | 12 (0.5–11.5 months) | 1981–2018 |

COLA-RSMAS-CCSM4 | CCSM4 | 10 | 12 (0.5–11.5 months) | 1982–2021 |

Lead Time | 1 | 2 | … | 9 | |
---|---|---|---|---|---|

Month | |||||

1 | M(1,1) | M(1,2) | … | M(1,9) | |

2 | M(2,1) | M(2,2) | … | M(2,9) | |

⋮ | ⋮ | ⋮ | … | ⋮ | |

12 | M(12,1) | M(12,2) | … | M(12,9) |

Lead Time | L_{1} | L_{2} | L_{3} | L_{4} | |
---|---|---|---|---|---|

Month | |||||

January | January | February | March | April | |

February | February | March | April | May | |

March | March | April | May | June | |

April | April | May | June | July | |

May | May | June | July | August | |

June | June | July | August | September | |

July | July | August | September | October | |

August | August | September | October | November | |

September | September | October | November | December | |

October | October | November | December | January | |

November | November | December | January | February | |

December | December | January | February | March |

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**MDPI and ACS Style**

Pakdaman, M.; Babaeian, I.; Bouwer, L.M. Improved Monthly and Seasonal Multi-Model Ensemble Precipitation Forecasts in Southwest Asia Using Machine Learning Algorithms. *Water* **2022**, *14*, 2632.
https://doi.org/10.3390/w14172632

**AMA Style**

Pakdaman M, Babaeian I, Bouwer LM. Improved Monthly and Seasonal Multi-Model Ensemble Precipitation Forecasts in Southwest Asia Using Machine Learning Algorithms. *Water*. 2022; 14(17):2632.
https://doi.org/10.3390/w14172632

**Chicago/Turabian Style**

Pakdaman, Morteza, Iman Babaeian, and Laurens M. Bouwer. 2022. "Improved Monthly and Seasonal Multi-Model Ensemble Precipitation Forecasts in Southwest Asia Using Machine Learning Algorithms" *Water* 14, no. 17: 2632.
https://doi.org/10.3390/w14172632