Artificial neural network models for prediction of standardized precipitation index in central Mexico

Some of the effects of climate change may be related to a change in patterns of rainfall intensity or rainfall scarcity. So, humanity is facing environmental challenges due to an increase in the occurrence of droughts. Forecasting of droughts based on cumulative influence of rainfall could be greatly beneficial for mitigating adverse consequences on water-sensitive sectors such as agriculture. Then, predictive models of drought indices could help in assessing water scarcity situations, droughts identification and their severity characterization. In this paper, we tested the feasibility of the Artificial Neural Network as a data-driven model for predicting the monthly Standardized Precipitation Index in 4 regions (Semi-desert, Highlands, Canyons and Mountains) of north-central México using predictive variable data from 1965 to 2004 as training data and simulated data for the period 2005-2014. The best model was found using the Hyperbolic Tangent as activation function and the Adaptive Moment Estimation (Adam) algorithm as optimization method. The best model was set to the following architecture: 26-12-1 network with 4 weights and 365 trainable parameters. Based on analysis of scatter plot between predicted and observed Standardized precipitation Index values for the test dataset, the Coefficient of Determination was between 0.84 and 0.88. In terms of quantitative statistics averaged over the test set, Artificial Network Model performed very well in predicting Standardized Precipitation Index at the four analyzed regions. This was verified by all-region average value of performance statistics Mean Absolute Error (0.0805), Mean Square Error (0.0144) and the Coefficient of Determination (0.8671). In a nutshell we summarize that the Artificial Network models developed and tested in this study had good prediction skills of the monthly Standardized Precipitation Index for stations and its drought-related properties in the study region.