A hybrid system using Possibilistic fuzzy C-mean and Interval type-2 Fuzzy logic for forecasting: A review

The weather prediction has gained much attention around the world due to climate changes. It includes the temperature, wind, fog, humidity, and rainfall. In this study, only rainfall prediction is considered because it plays a vital role in planning and reforming the agricultural structures and in managing the storm water, runoff and pollution control systems. It is found that there is uncertainty in data (e.g. inconsistent, incomplete, human error, missing values, and device error in data) obtained through heterogeneous devices. It leads to uncertain rainfall prediction which adversely affects human life, time, and economy. To overcome these issues, different techniques are proposed by researchers for accurate rainfall prediction. However, there is still a need to reduce the uncertainty from the obtained data and manage it properly for accomplishing the accurate rainfall prediction. In this respect, a hybrid approach will be adopted by considering Possibilitic fuzzy C-mean and Interval type-2 Fuzzy logic methods to address problem of uncertainty in data. The outcome of this study may enrich the rainfall prediction accuracy.