Deep Learning-based Weather Prediction: A Focused Case Study on Mosul City, Iraq

Background: Despite technological advancements, accurate weather forecasting remains a complex and challenging task. This looks at climate forecasts in Mosul City, Iraq, using a deep getting-to-know-you model that uses Long Short-Term Memory (LSTM) networks. More than a century's worth of historical weather records, including temperature, humidity, and precipitation, was used to teach and validate the model. The results demonstrate the effectiveness of LSTM in enhancing the dependability of climate forecasting, with an accuracy charge of above 88%. This takes a look at offers a strong basis for similar studies and operational forecasting structures while demonstrating the innovative capability of deep mastering in meteorological packages. Aims: The study presents an intensive teaching model using a long short-term memory (LSTM) network to predict weather conditions in Mosul City, Iraq, with an accuracy rate of more than 88%. The research examines deep learning ability in meteorological and weather applications and suggests future research on LSTM variants and network architecture. Study Design: The study takes a look at outlines and the procedure of constructing a Long Short-Term Memory (LSTM) model using Pandas. The dataset is loaded, preprocessed, and normalized using MinMaxScaler. Sequence creation is carried out through the use of Keras's Sequential API. The version is compiled using the Adam optimizer and MSE loss function for regression duties. The version is trained on the dataset, making predictions for the next day's climate in Mosul. Place and Duration of Study: Departments: Administrative Institute at Northern Technical University, Institution: Northern Technical University, Location: Mosul, Iraq, Duration: April 23 – May 2, 2024. Methodology: The method for predicting destiny climate entails loading a weather dataset, preprocessing it, creating sequences, building an LSTM version with MSE and MAE metrics, compiling the model with the usage of the Adam optimizer, and using mean squared mistakes for regression tasks. The LSTM version is educated on the dataset through the use of the match () technique, and the model predicts the next day's climate using inverse transformation and information manipulation. The predictions are displayed and stored in a brand-new CSV file for efficient time series analysis and preservation of ancient climate information. Results: A weather forecasting version becomes advanced through the use of Long Short-Term Memory (LSTM) neural networks and weather facts for Mosul. The model produced correct predictions for destiny climate parameters like humidity, temperature extremes, rainfall, and UV index. The model finished with an 88% average accuracy throughout all variables, with the lowest accuracy (60%) occurring on April 29 because of combined errors in humidity and MAX temperature. The version has proven reliable performance for temperature and humidity but calls for refinement for rainfall prediction, mainly throughout high-variability periods. The 88% common accuracy offers actionable insights for agricultural and disaster control planning. Conclusion: A weather forecasting model that uses Long Short-Term Memory (LSTM) neural networks and Mosul metropolis weather records completed 88% common accuracy for destiny weather parameters like humidity, temperature extremes, rainfall, and UV index. The version offers actionable insights for agricultural and catastrophe control planning; however, it calls for refinement for rainfall prediction. The findings should improve weather forecasts, useful resource groups in choice-making, and observe industries like catastrophe alleviation, transportation, and agriculture.