  Long-term rainfall prediction is a challenging task especially in the modern
world where we are facing the major environmental problem of global warming. In
general, climate and rainfall are highly non-linear phenomena in nature
exhibiting what is known as the butterfly effect. While some regions of the
world are noticing a systematic decrease in annual rainfall, others notice
increases in flooding and severe storms. The global nature of this phenomenon
is very complicated and requires sophisticated computer modeling and simulation
to predict accurately. In this paper, we report a performance analysis for
Multivariate Adaptive Regression Splines (MARS)and artificial neural networks
for one month ahead prediction of rainfall. To evaluate the prediction
efficiency, we made use of 87 years of rainfall data in Kerala state, the
southern part of the Indian peninsula situated at latitude -longitude pairs
(8o29'N - 76o57' E). We used an artificial neural network trained using the
scaled conjugate gradient algorithm. The neural network and MARS were trained
with 40 years of rainfall data. For performance evaluation, network predicted
outputs were compared with the actual rainfall data. Simulation results reveal
that MARS is a good forecasting tool and performed better than the considered
neural network.
