# Enhancing Weather Forecasting Integrating LSTM and GA
## 1 Introduction  
Weather variables play a vital role in many sectors and activities, affecting various aspects of our daily lives. From aviation to energy production, weather conditions have a significant impact on the performance and results of various sectors. Agriculture and renewable energy production are two sectors that are highly dependent on weather variables [1]. Weather variability presents significant challenges related with the variability and unpredictability of the data, making it difficult to rely on consistent patterns. Weather forecasting plays an important role in improving the performance and efficiency of different sectors, including agriculture, energy, and transportation [2, 3]. However, there are some problems associated with weather forecasts that can affect their accuracy such as traditional forecasting methods may struggle to accurately predict sudden changes, leading to less reliable weather forecasts; historical data can be incomplete or inaccurate, impacting the ability to model future conditions accurately; weather variables can have complex iterations between them and capturing these interactions in forecasting models is challenging, often requiring sophisticated algorithms and computational resources. In fact, the accuracy and reliability of weather forecasts have a huge impact on decision making, enabling effective planning, risk mitigation, and optimization of operations. For example, understanding and accurately predicting weather variables is critical to optimizing renewable energy production, reducing costs and minimizing environmental impact. By accurately forecasting weather conditions, grid operators can anticipate fluctuations in renewable energy output and plan for backup power sources or storage solutions to maintain a reliable supply of electricity. In the agricultural sector, weather variables play a crucial role in shaping farming practices, yield forecasting, and crop management. Farmers rely on a combination of weather variables such as temperature, rainfall, humidity, and wind patterns to makeinformed decisions about planting, irrigation, and harvesting. In fact, understanding weather patterns allows farmers to optimize their operations, mitigate risk, and ensure the overall health and productivity of their crops. On the other hand, solar and wind energy production is highly dependent on weather conditions, which determine the availability and efficiency of these sustainable energy systems. [PERSON] and [PERSON] (2018) emphasized the importance of weather forecasting in these areas because of its impact on productivity, resource allocation, and risk management. They proposed a transductive long short-term memory (T-LSTM) method to extract local information in weather time series. Regarding the historical data used in forecasts, missing values affect the accuracy of forecasts. For this reason, it is important use resourceful and flexible techniques. [PERSON] (2018) emphasized the importance of addressing missing data in statistical analysis, and highlighted the advantages of the multiple imputation (MI) approach and [PERSON] et al. (2018) discussed the significance of handling missing data in weather datasets, which is crucial for accurate climate forecasting impacting various sectors. The paper proposes a new technique for addressing missing values in weather data using machine learning algorithms, such as kernel ridge regression, linear regression, random forest, or support vector machine (SVM) imputation. In this paper, regarding the characteristics of the places under studies, we chose to use regression methods to estimate missing values due to their ability to leverage relationships between variables. Long short-term memory (LSTM) networks are a type of recurrent neural network (RNN) designed to model sequential data, making them highly suitable for weather forecasting.