Abstract
Weather elements are the most important parameters in metrological and hydrological studies especially in semi-arid regions, like Jordan. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used here to predict the minimum and maximum temperature of rainfall for the next 10 years using 30 years’ time series data for the period from 1985 to 2015. Several models were used based on different membership functions, different methods of optimization, and different dataset ratios for training and testing. By combining a neural network with a fuzzy system, the hybrid intelligent system results in a hybrid Neuro-Fuzzy system which is an approach that is good enough to simulate and predict rainfall events from long-term metrological data. In this study, the correlation coefficient and the mean square error were used to test the performance of the used model. ANFIS has successfully been used here to predict the minimum and maximum temperature of rainfall for the coming next 10 years and the results show a good consistence pattern compared to previous studies. The results showed a decrease in the annual average rainfall amounts in the next 10 years. The minimum average annual temperature showed the disappearance of a certain predicted zone by ANFIS when compared to actual data for the period 1985-2015, and the same results behavior has been noticed for the average annual maximum.
Highlights
Rainfall plays an enormous role in climate classification
Rainfall forecasting is a non-linear forecasting process that varies according to area. It is strongly influenced by climate change. Another parameter which is considered an element of climate is the temperature, in its both average annual maximum temperatures and the average annual minimum temperature; both temperatures can give a good indication about the behavior of the weather
In this study it is clear that the model Adaptive Neuro-Fuzzy Inference System (ANFIS) has presented good results for simulating and predicting rainfall precipitation in the arid regions
Summary
Rainfall plays an enormous role in climate classification. As a result, the climate in any area is strongly influenced by rainfall [5]. Rainfall forecasting is a non-linear forecasting process that varies according to area It is strongly influenced by climate change. Another parameter which is considered an element of climate is the temperature, in its both average annual maximum temperatures and the average annual minimum temperature; both temperatures can give a good indication about the behavior of the weather. In this study, both temperatures are introduced in the simulation process for the predication as well as for the rainfall
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