Nowadays, beside the political wars that mankind faces from and the economical war that all the world face of, there is a big clear problem that threats the Earth planet as a whole. It is the nature war or we can call the biological catastrophes. Human behavior was the main reason that faces call for nature diseases and disasters that threat human health and wealth as a whole. In addition to the wrong human behavior we also have to include the Sun’s activity in the mind. Looking at the Sun and its activity that threats Earth we have to focus on two of the most important elements of the solar activity, they are the ultraviolet irradiance and the sunspots number.
Overexposure to UV radiation is the primary environmental risk factor in the development of UV-related adverse health effects, which include diseases of the eye (e.g., cataract, lens capsule deformation, ocular melanoma, etc.), skin wrinkles, delayed tanning, sunburn, carcinoma and also molecular changes within the cells. As a result, it is of great interest to assess the changes in the level of UV radiation reaching the Earth’s surface.
On the other hand, solar flares are affecting the smooth operation of our modern society. A sufficiently large solar flare ejects coronal material from the core of the sun, and this material negatively effects the operation of satellites. We rely on satellites for communication, something absolutely crucial for the operation of our modern society.
Despite the recognize importance of UV and solar activity, little attention has been given in Egypt to the routine collection of its data. So it is of great interest to assess the past changes in its level. Therefore, many efforts have been carried out in inferring this information from other available data sets. A survey of the existing literature on the issue reveals three distinctive methodologies. The first method refers to the radiative transfer models. These models are governed by complex interactions of dynamic and physical processes which are very sensitive to a diverse array of atmospheric variables. In spite of its significance, the main source of the predictable uncertainties of these models comes from the limited availability of the high quality fine resolution data. The second method is to devise statistical models which attempt to determine the underlying relationship between a set of input data and targets. Regression modeling is an example of such a statistical approach. One of the main advantages of this method is that it uses the individual spot measurements that are taken at short time steps, and thus it contains the short-term variability. But, the main limitation imposed by these models is that they will under perform when used to model nonlinear systems and they show a latitude dependency. The third method has more recently been introduced and employs artificial intelligence techniques such as artificial neural networks. This technique mimics somewhat the learning processes of a human brain. In our study, artificial neural network and a fuzzy rule based approach has been proposed to achieve the UV and sun activity prediction task.
