## 1 Introduction  
Meteorology has always been an element of great importance for humanity due to the various activities that depend on the climatic condition. A good example is agriculture [1], as the climate is one of the main factors determining the productivity of a crop; it is also used for predicting natural disasters such as floods and droughts [2]. These events cannot be inhibited, but when anticipated, it can contribute to a premeditated plan of action, avoiding further damage. The rainfall frequency, unfortunately, has been suffering interference, from the evolutionary actions of the human race [3]. In recent years, it is common to find reports of large cities declaring a state of emergency and rationing for lack of water, as occurred recently in the capital of Belo Horizonte in the state of Minas Gerais, which carries the title of water tank in Brazil, because it is the cradle of watersheds critical nationally. Along with several great cities, it has suffered a strong impact from drought [4]. In contrast to the lack of water, it has problems with excess: floods and landslides [2]. If a river or stream receives a considerable amount of water from rain and cannot support it, it ends up overflowing and causing floods, which bring with it destruction, damage, and even death. The facts cited above are just some of the impacts that rain has on society. Within this context, this work was developed for the use of neural networks in the forecast of volumetric rainfall, based on a fuzzy rule system, developing a precise forecast, and contributing to the anticipation of possible tragedies s in places that occur phenomena of greater magnitude, as well as helping in a better control of agriculture. Another purpose was to support air and sea transportation, avoiding accidents in their trajectories. Briefly, when anticipating a possible problem, it is also possible to anticipate solutions and reduce their impacts, such as evacuating areas at risk of landslides or flooding [5]. Currently, many kinds of research are being developed to apply models of artificial neural networks in rain forecasts, river basins, and water studies. For example, it was applied to the problem of forecasting the flow of the Nile River in Egypt [6], using a temporal series as a reference to compare tests among several prediction methods of neural networks. Besides, there was a study using stochastic autoregressive mean models in time series for rainfall forecasting in the Apennines mountains [7], Italy. Another exciting study was carried out due to the need for greater precision in the prediction of time series. In this model, the researchers used a hybrid neural network on monthly flow data from the Colorado River at Lees Ferry, USA [8]. ANN was also applied to predict the short-term flow of the Winnipeg River in Northwest Ontario, Canada. The developed ANNs consistently outperformed conventional models during the test phase for all four predictions [9]. Another study presented a new procedure to identify the structure and parameters of three-layer ANN models and demonstrate the potential of such models to simulate hydrological behavior, providing a better representation of the rain in river basin flow near Collins, Mississippi, USA [10]. Lastly, the importance of rainfall prediction in water studies used a genetic algorithm coupled to ANN to simulate a rainfall field, implemented to predict rainfall several times using rain gauge in the Upper Watershed Parramatta, in the western suburbs of Sydney, Australia [11]. Thus, it is not an innovation in water studies and meteorology, which has always been side-by-side with the technology. However, when dealing with neural networks, there is a range of possibilities and paths. This paper presents a path that it contributes to the studies already developed. The use of intelligent models to aid in the prediction of meteorological aspects is vital to assist government managers and people connected to the industry.