Prediction of Daily Climate Using Long Short-Term Memory (LSTM) Model

TLDR

Climate prediction is vital for agriculture, disaster management, and urban planning, yet traditional physical models are computationally intensive and often fail to capture local patterns. The study investigates using LSTM networks to predict daily temperature, precipitation, and humidity. An LSTM model with two recurrent layers and three dense layers, trained on Delhi’s historical climate data using the Adam optimizer and MSE/MAE loss, was developed to forecast short‑term trends. The model successfully captured temporal dependencies, achieving satisfactory temperature forecasting accuracy and demonstrating the promise of LSTM networks for climate prediction.

Abstract

Climaate prediction plays a vital role in various sectors, including agriculture, disaster management, and urban planning. Traditional methods for climate forecasting often rely on complex physical models, which require substantial computational resources and may not accurately capture local weather patterns. This study explores the potential of Long Short-Term Memory (LSTM) networks, a type of recurrent neural network, for predicting daily climate variables such as temperature, precipitation, and humidity. Utilizing historical climate data from the city of Delhi, we developed an LSTM model to forecast short-term climate trends. The model consists of two LSTM layers followed by three Dense layers and is compiled with the Adam optimizer, mean squared error loss, and mean absolute error as a metric. Our results demonstrate the model's capability to capture temporal dependencies in climate data, achieving a satisfactory level of accuracy in temperature forecasting. This research underscores the potential of machine learning techniques, particularly LSTM networks, in enhancing climate prediction and contributing to more informed decision-making in weather-sensitive sectors.