Rainfall Forecasting in Sudan Using Computational Intelligence

Abstract

Weather forecasting is the application of science and technology to predict the state of the

atmosphere for a future time at a given location. Human kind has attempted to predict the

weather since ancient times. Generating predictions of meteorological events is very complex

process, because the atmosphere is unstable and the systems responsible for the events are the

culmination of the instabilities and involve nonlinear interaction between different spatial scales

from kilometers to hundreds of kilometers. The chaotic nature of the atmosphere limits the

validity of deterministic forecasts, but the increasing economic cost of adverse weather events

provides a strong reason to generate more accurate and updated weather forecasts.

Weather forecasting (particularly rainfall prediction) is one of the most imperatives,

important and demanding operational tasks and challenge made by meteorological services

around the world. It is a complicated procedure that includes numerous specialized fields of

knowledge. The task is complicated because in the field of meteorology all decisions are to be

taken with a degree of uncertainty, because the chaotic nature of the atmosphere limits the

validity of deterministic forecasts. Long term Rainfall prediction is very important for countries

whose economy depends mainly on agriculture, like many of the third World countries. It is

widely used in the energy industry and for efficient resource planning and management including

famine and disease control, rainwater catchment and ground water management. This thesis

studies long term rainfall prediction in Sudan using computational intelligence.

Monthly meteorological data obtained from Central Bureau of Statistics, Sudan from

2000 to 2012, for 24 meteorological stations distributed among the country has been used.

The relationship of rainfall in Sudan with some important parameters is investigated and

determined the most influencing variables on rainfall among the available ones.

The performance of base and Meta algorithms to deal with rainfall prediction problem is

explored and, compared.

A novel method to develop long-term rainfall prediction model by using ensemble

technique is proposed. The new novel ensemble model is constructed based of Meta classifier

Vote combined with three base classifiers. Several neuro-fuzzy Models using different types of

membership functions, different optimization methods and different dataset ratios for training

and testing are built.

The proposed models are evaluated and compared by using correlation coefficient, mean

absolute error and root mean-squared error as performance metrics. The empirical results

illustrate that the ANFIS neuro-fuzzy system and the ensemble Vote+3 models are able to

capture the dynamic behavior of the rainfall data and they produced satisfactory results, so they

may be very useful in long-term rainfall prediction.

Spatial analysis of rainfall in Sudan is conducted for the interval 2000-2012 on three levels (towns, states and regions) and rainfall maps are obtained.