Drought prediction based on SPI and SPEI with varying timescales using LSTM recurrent neural network

Abstract Over years, natural calamities like drought have taken a huge toll on human life and resources. As the prediction methods increase, the effects of natural calamities can be reduced to an extent by preplanning and providing warnings to the people. Metrological drought indices like standardized precipitation index and standardized precipitation evapotranspiration index are used to identify drought and its severity level. By forecasting these indices, the occurrences of drought are predicted using the prediction models which help the society to take preventive measures due to the effect of drought. Many research works on prediction majorly focused on statistical methods such as Holt–Winters and ARIMA, but these methods lack accuracy to provide long-term forecasts. However, with advances in the area of machine learning especially artificial neural networks and deep neural networks, there seems to be a method to predict drought in the long term with a good accuracy. Long short-term memory is used in recurrent neural network to predict the drought indices which handle the real-time nonlinear data well and good that can help authorities better prepare and mitigate natural disasters. In this paper, we compare the 1-, 6- and 12-month prediction of the ARIMA statistical model with LSTM using multivariate input in hopes of bettering said performance.

[1]  Mohammad Taghi Dastorani,et al.  Application of artificial neural networks on drought prediction in Yazd (Central Iran) , 2011 .

[2]  H. C. S. Thom,et al.  a Note on the Gamma Distribution , 1958 .

[3]  John A. Dracup,et al.  The Quantification of Drought: An Evaluation of Drought Indices , 2002 .

[4]  Muhammad Faisal,et al.  Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model , 2017, ArXiv.

[5]  N. Guttman.  ACCEPTING THE STANDARDIZED PRECIPITATION INDEX: A CALCULATION ALGORITHM 1 , 1999 .

[6]  Jan Adamowski,et al.  Drought forecasting using new machine learning methods / Prognozowanie suszy z wykorzystaniem automatycznych samouczących się metod , 2013 .

[7]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[8]  Qiongji Jin,et al.  Design of Deep Belief Networks for Short-Term Prediction of Drought Index Using Data in the Huaihe River Basin , 2012 .

[9]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[10]  William H. Press,et al.  Numerical Recipes 3rd Edition: The Art of Scientific Computing , 2007 .

[11]  T. McKee,et al.  THE RELATIONSHIP OF DROUGHT FREQUENCY AND DURATION TO TIME SCALES , 1993 .

[12]  S. Poornima,et al.  A survey of predictive analytics using big data with data mining , 2018, Int. J. Bioinform. Res. Appl..

[13]  Amir AghaKouchak,et al.  A Nonparametric Multivariate Multi-Index Drought Monitoring Framework , 2014 .

[14]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[15]  S. Vicente‐Serrano,et al.  A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index , 2009 .

[16]  P SinghV,et al.  最大エントロピー原理(POME)による3パラメータ対数ロジスティック分布のパラメータ推定 , 1993 .

[17]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[18]  O. Kisi,et al.  Wavelet and neuro-fuzzy conjunction model for precipitation forecasting , 2007 .

[19]  Zhongqing Su,et al.  A Hybrid Particle Swarm Optimization (PSO)-Simplex Algorithm for Damage Identification of Delaminated Beams , 2012 .

[20]  S. Morid,et al.  Drought forecasting using artificial neural networks and time series of drought indices , 2007 .

[21]  M. N. Kulkarni.  A new tool for predicting drought: An application over India , 2015, Scientific Reports.

[22]  David R. Cox,et al.  Time Series Analysis , 2012 .

[23]  Vijay P. Singh,et al.  Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome , 1993 .

[24]  C. W. Thornthwaite.  An approach toward a rational classification of climate. , 1948 .

[25]  Isabella Bordi,et al.  Extreme value analysis of wet and dry periods in Sicily , 2007 .

[26]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .