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.
Bushara, N (2021). Rainfall Forecasting in Sudan Using Computational Intelligence. Afribary. Retrieved from https://tracking.afribary.com/works/rainfall-forecasting-in-sudan-using-computational-intelligence
Bushara, Nazim "Rainfall Forecasting in Sudan Using Computational Intelligence" Afribary. Afribary, 20 May. 2021, https://tracking.afribary.com/works/rainfall-forecasting-in-sudan-using-computational-intelligence. Accessed 24 Nov. 2024.
Bushara, Nazim . "Rainfall Forecasting in Sudan Using Computational Intelligence". Afribary, Afribary, 20 May. 2021. Web. 24 Nov. 2024. < https://tracking.afribary.com/works/rainfall-forecasting-in-sudan-using-computational-intelligence >.
Bushara, Nazim . "Rainfall Forecasting in Sudan Using Computational Intelligence" Afribary (2021). Accessed November 24, 2024. https://tracking.afribary.com/works/rainfall-forecasting-in-sudan-using-computational-intelligence