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干旱区科学  2020, Vol. 12 Issue (5): 854-864    DOI: 10.1007/s40333-020-0097-3
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Precipitation forecasting by large-scale climate indices and machine learning techniques
Mehdi GHOLAMI ROSTAM, Seyyed Javad SADATINEJAD, Arash MALEKIAN*()
University of Tehran, Tehran 1417466191, Iran
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Abstract: 

Global warming is one of the most complicated challenges of our time causing considerable tension on our societies and on the environment. The impacts of global warming are felt unprecedentedly in a wide variety of ways from shifting weather patterns that threatens food production, to rising sea levels that deteriorates the risk of catastrophic flooding. Among all aspects related to global warming, there is a growing concern on water resource management. This field is targeted at preventing future water crisis threatening human beings. The very first stage in such management is to recognize the prospective climate parameters influencing the future water resource conditions. Numerous prediction models, methods and tools, in this case, have been developed and applied so far. In line with trend, the current study intends to compare three optimization algorithms on the platform of a multilayer perceptron (MLP) network to explore any meaningful connection between large-scale climate indices (LSCIs) and precipitation in the capital of Iran, a country which is located in an arid and semi-arid region and suffers from severe water scarcity caused by mismanagement over years and intensified by global warming. This situation has propelled a great deal of population to immigrate towards more developed cities within the country especially towards Tehran. Therefore, the current and future environmental conditions of this city especially its water supply conditions are of great importance. To tackle this complication an outlook for the future precipitation should be provided and appropriate forecasting trajectories compatible with this region's characteristics should be developed. To this end, the present study investigates three training methods namely backpropagation (BP), genetic algorithms (GAs), and particle swarm optimization (PSO) algorithms on a MLP platform. Two frameworks distinguished by their input compositions are denoted in this study: Concurrent Model Framework (CMF) and Integrated Model Framework (IMF). Through these two frameworks, 13 cases are generated: 12 cases within CMF, each of which contains all selected LSCIs in the same lead-times, and one case within IMF that is constituted from the combination of the most correlated LSCIs with Tehran precipitation in each lead-time. Following the evaluation of all model performances through related statistical tests, Taylor diagram is implemented to make comparison among the final selected models in all three optimization algorithms, the best of which is found to be MLP-PSO in IMF.
Key words:  backpropagation    genetic algorithms    machine learning    multilayer perceptron    particle swarm optimization    Taylor diagram
收稿日期:  2019-12-03      修回日期:  2020-04-30      接受日期:  2020-07-13      出版日期:  2020-09-10      发布日期:  2020-09-10      期的出版日期:  2020-09-10
引用本文:    
. [J]. 干旱区科学, 2020, 12(5): 854-864.
Mehdi GHOLAMI ROSTAM, Seyyed Javad SADATINEJAD, Arash MALEKIAN. Precipitation forecasting by large-scale climate indices and machine learning techniques. Journal of Arid Land, 2020, 12(5): 854-864.
链接本文:  
http://jal.xjegi.com/CN/10.1007/s40333-020-0097-3  或          http://jal.xjegi.com/CN/Y2020/V12/I5/854
Row Index Row Index
1 The Pacific/North American Pattern (PNA) 19 Arctic Oscillation (AO)
2 North Atlantic Oscillation (NAO) 20 Antarctic Oscillation (AAO)
3 West Pacific Pattern (WP) 21 Southern Oscillation Index (SOI)
4 North Pacific Pattern (NP) 22 Central Indian Precipitation
5 East Pacific Pattern (EP) 23 Northeast Brazil Rainfall Anomaly
6 Pacific Decadal Oscillation (PDO) 24 Tropical Northern Atlantic (TNA)
7 Eastern Pacific Oscillation (EPO) 25 Tropical Southern Atlantic (TSA)
8 North Oscillation Index (NOI) 26 Atlantic Meridional Mode (AMM)
9 El Nino - Southern Oscillation (ENSO) 27 Atlantic Multi-decadal Oscillation (AMO)
10 Multivariate ENSO Index (MEI) 28 Western Hemisphere Warm Pool (WHWP)
11 Extreme Eastern Tropical Pacific SST (Nino 1+2) 29 North Tropical Atlantic SST Index (NTA)
12 Central Tropical Pacific SST (Nino 4) 30 Oceanic NINO Index (ONI)
13 East Central Tropical Pacific SST (Nino 3.4) 31 Trans Nino Index (TNI)
14 Sahel Standardized Rainfall 32 Pacific Warm pool (PWP)
15 Eastern Asia/ Western Russia (EA/WR) 33 Indian Ocean Dipole (IOD)
16 Caribbean Index (CAR) 34 Solar Flux
17 Bivariate ENSO Time series (BEST) 35 Monthly totals Atlantic hurricanes and named tropical storms
18 Quasi-Biennial Oscillation (QBO) 36 North Sea-Caspian Pattern (NCP)
  
  
  
  
  
Time scale Model framework Lead-time (month) Z
Monthly CMF 3 0.84
Monthly CMF 8 0.62
Monthly IMF - 0.56
  
Method Model framework Lead-time (month) Training Validation Test
RMSE MAE RMSE MAE RMSE MAE
(mm) (mm) (mm) (mm) (mm) (mm)
BP-based MLP CMF 3 18.53 12.10 20.43 14.90 19.37 12.38
GA-based MLP CMF 8 19.34 14.61 20.36 14.26 17.39 13.59
PSO-based MLP IMF - 21.21 15.12 19.17 13.69 18.54 12.97
  
  
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