Rainfall prediction using deep learning techniques

Predicting rainfall accurately is crucial for various sectors such as agriculture, water resource management, disaster preparedness, and climate research. Traditional methods of rainfall prediction often rely on numerical weather models, which may have limitations in accuracy and efficiency. In recent years, deep learning techniques have emerged as promising tools for improving the accuracy of rainfall prediction. This paper provides an overview of the application of deep learning techniques in rainfall prediction, discussing various methodologies, challenges, and future directions in this field. Predicting rainfall is the most challenging assignment in meteorology. We have developed a rainfall prediction model in our work that may be readily estimated via the use of LSTM and artificial intelligence approaches. This rainfall calculator is an advanced approach. For the application of this kind of strategy and its accuracy findings, the deep learning approach is most beneficial. The memory sequence data measurement process uses a long short-term memory method, which computes historical data quickly and produces the best forecast. Since agriculture is the main source of income for the majority of the population in this nation, this prediction method is essential. Crop yields will rise and agricultural expenses will decrease with prompt rainfall assessment. Our model, which will assist us in estimating the quantity of rainfall, was developed taking into account all of these variables. To achieve this, we have gathered information from six areas. We have used six factors in our prediction: temperature, dew point, humidity, wind pressure, wind direction, and wind speed. Our task was completed with 76% accuracy after all of our data was analysed. For improved results, we also concentrate on a large dataset on long-term weather.