Using Artificial Neural Networks and SPI Measure Techniques to Forecast the Risk of Drought in Iraq and Its Impact on Environment
The world suffers from drought, which has a negative impact on human, economic, social, cultural and tourism fields. As science progressed and developed, several ways of reducing drought were found. This phenomenon is also called (aridity and infertility, and water retention), it means a severe shortage of water resources due to low precipitation and low rainfall over a specific normal period time, which are causing heavy losses in agricultural production, and the occurrence of disasters and human calamities such as starvation, and it is forcing some population to emigrate collectively. The artificial neural networks (ANN) and the Standard Rain Index (SPI) were used in the analysis of the rainfall for all Iraqi governorates for the period 1991-2016 monthly. This study shows that the best model of the neural network is [19-3-1] according to AIC to forecast the amount of rainfall, and that the Iraqi provinces over next 10 years are exposed to a different behavior of climate between moderate dry and average humidity, and increase the area of desertification.