A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods

Pouya Aghelpour,Ozgur Kisi,Seyed Mostafa Biazar,Babak Mohammadi,Zohreh Sourmirinezhad

doi:10.3390/ijgi9120701

Abstract

Precipitation deficit can affect different natural resources such as water, soil, rivers and plants, and cause meteorological, hydrological and agricultural droughts. Multivariate drought indexes can theoretically show the severity and weakness of various drought types simultaneously. This study introduces an approach for forecasting joint deficit index (JDI) and multivariate standardized precipitation index (MSPI) by using machine–learning methods and entropy theory. JDI and MSPI were calculated for the 1–12 months’ time window (JDI1–12 and MSPI1–12), using monthly precipitation data. The methods implemented for forecasting are group method of data handling (GMDH), generalized regression neural network (GRNN), least squared support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS) and ANFIS optimized with three heuristic optimization algorithms, differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO) as meta-innovative methods (ANFIS-DE, ANFIS-GA and ANFIS-PSO). Monthly precipitation, monthly temperature and previous amounts of the index’s values were used as inputs to the models. Data from 10 synoptic stations situated in the widest climatic zone of Iran (extra arid-cold climate) were employed. Optimal model inputs were selected by gamma test and entropy theory. The evaluation results, which were given using mean absolute error (MAE), root mean squared error (RMSE) and Willmott index (WI), show that the machine learning and meta-innovative models can present acceptable forecasts of general drought’s conditions. The algorithms DE, GA and PSO, could improve the ANFIS’s performance by 39.4%, 38.7% and 22.6%, respectively. Among all the applied models, the GMDH shows the best forecasting accuracy with MAE = 0.280, RMSE = 0.374 and WI = 0.955. In addition, the models could forecast MSPI better than JDI in the majority of cases (stations). Among the two methods used to select the optimal inputs, it is difficult to select one as a better input selector, but according to the results, more attention can be paid to entropy theory in drought studies.

Highlights

Drought forecasting is one of the major concerns to the water managers, agriculturalists and other water exploiters
The most accurate prediction of joint deficit index (JDI) is reported for Boshrouyeh station by inputs of entropy theory and group method of data handling (GMDH) model, with mean absolute error (MAE) = 0.338, root mean squared error (RMSE) = 0.456, and Willmott index (WI) = 0.864
The weakest performance belongs to the simple adaptive neuro-fuzzy inference system (ANFIS) model in East Isfahan station, which is given by inputs of gamma test with MAE = 0.626, RMSE = 1.207, and WI = 0.674

Summary

Introduction

Drought forecasting is one of the major concerns to the water managers, agriculturalists and other water exploiters. During the years 1998–2000, Iran experienced one of the worst and the costliest drought periods that occurred in the last 50 years During this three-year dry period, water scarcity reached a crisis point in more than 270 cities, thousands of villages ran out of drinking water, the rate of surface water flow decreased to 55%, and Iran’s dam and reservoirs have had to operate at their minimum volume capacity to transfer water because of the low inflows and high temperatures [1,2]. Among the most important of these indices, the multivariate standard precipitation index (MSPI) and joint deficit index (JDI) can be pointed out These indices combine some series of standard precipitation indices (SPI) that each of them indicates a certain type of drought [4]. The combination is based on the various statistical methods, and the result reflects the general status of the drought in a given month

Methods

Results

Discussion

Conclusion