Prediction of processes in the geosciences essentially involves two methods: dynamical modeling and statistical methods. While the former is important for understanding the dynamics of the system the latter is usually deployed for making actual forecasts of natural phenomena. The underlying principle behind both is same- the modeling of deterministic laws that govern the system. After the sound developments in the theory of computational intelligence techniques such as Artificial Neural Networks, Genetic Algorithms and Fuzzy logic coupled with advanced frequency domain transformations using Wavelet transforms has opened a new area of research and a development of sophisticated statistical models that can correctly forecast natural events based on the past history. This has immense capability of being useful to the community of scientists as well as for socio-economical development of mankind.

The journal of Applied Computational Intelligence in Geosciences is aimed at bringing out specific models for the prediction of natural phenomena. The objective is to bring out relevant publications in the field so as to attract more and more researchers involved in the modeling of such phenomena to use the techniques of computational intelligence to the fullest. It is expected that authors shall describe the modeling of specific phenomena of specific area of the globe and avoid general discussions.

Scope:

As a guideline such phenomena may include long range, medium range or short range forecasting of meteorological, Ocean ographical and geo-scientific parameters such as

1. Rainfall
2. Winds
3. Sea surface temperatures
4. Sea surface heights
5. Southern oscillation and El-Nino
6. Sea ice processes of the poles
7. Seismic signals, Tsunami etc.

Further, application and development of following techniques is sought:

1. Artificial Neural Networks
2. Genetic Algorithms
3. Fuggy Logic
4. A hybrid system involving the above
5. Principle Components Analysis
6. Spectral Analysis
7. Wavelet Transforms
8. Other local and global prediction algorithms