The climate change implication on Jordan: A case study using GIS and Artificial Neural Networks for weather forecasting

Abstract

The meteorological data such as rainfall and temperatures, covering the period between 1979 and 2008, has been analyzed. The data were simulated using the geographic information systems (GIS) and computer software "MATLAB". The output results were converted into geographical maps. Three parameters were analyzed: annual mean maximum temperature, annual mean minimum temperature, and mean annual rainfall during the period . The analyzed results were also used to forecast for the period (2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018).The results show that no change has occurred in the mean annual rainfall in both northern and eastern part, while it has increased in the central region of Jordan. Although local temperatures fluctuate naturally, but over the past 50 years, the mean local temperature in Jordan has increased rapidly since 1992 by 1.5-2 • C.It is noticed from the data that the change in both maximum and minimum temperatures has clearly begun after 1991, in which this phenomenon may give an indication of changing point in climate of Jordan. As for prediction is concern, the show continuous increase in both maximum and minimum temperatures in the eastern, northern and southern regions of Jordan.The application of GIS in this study was successfully used to analyze the data and to produce 'easy to use' maps to understand the impact of global warming. This application is the first in terms of its applicability in Jordan. The authors believe that the results of this study will be of great help to the decision makers in the field of environment in Jordan.

Long-Term Weather Elements Prediction in Jordan using Adaptive Neuro-Fuzzy Inference System (ANFIS) with GIS Techniques

“…This means that the Country will witness a drawback in rainfall and more dryness as a lack of rainfall will be expected. These obtained results are in great consistency with previous studies which are related to rainfall predictions ( [1], [11], [16], [17]…”

Environmental modelling of visceral leishmaniasis by susceptibility-mapping using neural networks: a case study in north-western Iran

“…The ANN architecture consists of a number of interconnected neural neurons that are modelled by mathematical functions (Haykin, 1998). ANN learns from experience and can be trained to recognise patterns, classify data and forecast future events (Kohonen, 1996;Ripley, 1996;Bishop, 1997;Matouq et al, 2013). This makes ANN a powerful tool for modelling purposes, especially when the underlying data relationships are unknown (Lek and Guegan, 1999;Matouq et al, 2013).…”

What constitutes an equitable water share? A reassessment of equitable apportionment in the Jordan–Israel water agreement 25 years later

“…Rahman et al (2015) also show an overall decreasing trend in rainfall in Jordan, in particular in the drought years of 1995-2013, and Smiatek et al (2014) expect that rainfall amounts will continue to decrease in the area of the Jordan River. Matouq et al (2013) report that the mean annual maximum temperatures in Jordan, on the whole, are increasing and Smiatek et al (2014) forecast rising temperatures in the 21st century in the upper portion of the Jordan River Basin. Israel's Second National Communication on Climate Change (Israel Ministry of Environmental Protection, 2010) and Jordan's Third National Communication on Climate Change ( Jordan Ministry of Environment, 2014) both report that temperatures are expected to increase throughout the 21st century in comparison to the latter half of the 20th century, accompanied by an increase in extreme temperature events and a decrease in precipitation.…”