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Journal of Arid Land
Journal of Arid Land  2020, Vol. 12 Issue (5): 854-864    DOI: 10.1007/s40333-020-0097-3
Research article     
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     
Received: 03 December 2019      Published: 10 September 2020
Corresponding Authors:
About author: *Corresponding author: Arash MALEKIAN (E-mail: malekian@ut.ac.ir)
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GHOLAMI ROSTAM Mehdi
Javad SADATINEJAD Seyyed
MALEKIAN Arash
Cite this article:

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.

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http://jal.xjegi.com/10.1007/s40333-020-0097-3     OR     http://jal.xjegi.com/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)
Table 1 Initial large-scale climate indices (National Oceanic and Atmospheric Administration (2015)
Fig. 1 Schematic study trajectory diagram. As can be seen, after providing the inputs of two frameworks (i.e., concurrent model framework and integrated model framework), three methods, namely, backpropagation-based (BP-based) multilayer perceptron (MLP), genetic algorithm-based (GA-based) MLP and particle swarm optimization-based (PSO-based) MLP were targeted.
Fig. 2 Schematic topology of a three-layer MLP (Multilayer perceptron) applied in this study. In the input layer for each input, one neuron should be considered; the number of the neurons of the hidden layer in each model was selected based on trial and error and represented in its relating section; the number of the neurons in the output layer, as can be seen, is the modeled precipitation measure for each month.
Fig. 3 Diagram of statistical comparison among the models. The reference point on x-axis is the standard deviation of the observed dataset and the radial distance from this point (dashed contour) represents the RMSE (root mean square error) of the model; the dotted contours, which are the radial distance from the origin, shows the standard deviation of the simulated sets. It is clear that the most precise model is the one closer to the reference point (Taylor, 2001).
Fig. 5 Performance of all generated cases in three optimization methods. The 0-11 of x-axis shows the framework of CMF (concurrent model framework). IMF, integrated model framework. MAE, mean absolute error; RMSE, root mean square error.
Time scale Model framework Lead-time (month) Z
Monthly CMF 3 0.84
Monthly CMF 8 0.62
Monthly IMF - 0.56
Table 2 Z-test for selected cases in each method
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
Table 3 Selected models and their RMSE (root mean square error) and MAE (mean absolute error) under training, validation and test steps
Fig. 6 Result of Taylor diagram. As can be seen, the performance of all cases are in an approximately same range; however, the MLP-PSO is the closest case to the reference point.
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