Modelling Temperature In Machakos County Using LSTM

Abstract

Temperature forecasting is a essential component in numerous sectors including agri- culture, health, disaster management, and water resources. Precise predictions are

vital for decision-making and planning, especially in regions with complex climatic patterns like Machakos County, Kenya. Traditional statistical models fail to capture the nonlinear and inter-variable relationships in temperature data, necessitating advanced approaches. This study addressed this gap by applying a Long Short-Term Memory (LSTM) model to predict daily temperatures in Machakos County. The key objective of the project was to model daily temperature using Long short term memory network that can provide precise temperature forecasts, with specific goals including the fitting of the LSTM model, Comparison of performance the model with Auto Regressive Integrated Moving Average(ARIMA), Predicting thirty days daily average ground temperature and assessing perfomance of LSTM model as prediction period advances. The study involved pre-processing of NASA POWER project data set, followed by exploratory analysis of data and then fitting requisite model using python programming language. Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage

Error and Diebold-Mariano statistical test was used to assess model Performance fol- lowed by comparison and forecasting.

The study established that ARIMA and LSTM performance is equivalent in Short term predictions (7-days) at 5% level while Long Short Term Memory model outperforms ARIMA in long term predictions (30-days) at the same level. LSTM was used for forecasting of 30 days temperature with lower error of 0.91 ◦C