Abstract
Abstract Condensed water vapor in the atmosphere is observed as precipitation whenever moist air rises sufficiently enough to produce saturation, condensation, and the growth of precipitation particles. It is hard to measure the amount and concentration of total precipitation over time due to the changes in the amount of precipitation and the variability of climate. As a result of these, the modelling and forecasting of precipitation amount is challenging. For this reason, this study compares forecasting performances of different methods on monthly precipitation series with covariates including the temperature, relative humidity, and cloudiness of Muğla region, Turkey. To accomplish this, the performance of multiple linear regression, the state space model (SSM) via Kalman Filter, a hybrid model integrating the logistic regression and SSM models, the seasonal autoregressive integrated moving average (SARIMA), exponential smoothing with state space model (ETS), exponential smoothing state space model with Box-Cox transformation-ARMA errors-trend and seasonal components (TBATS), feed-forward neural network (NNETAR) and Prophet models are all compared. This comparison has yet to be undertaken in the literature. The empirical findings overwhelmingly support the SSM when modelling and forecasting the monthly total precipitation amount of the Muğla region, encouraging the time-varying coefficients extensions of the precipitation model.
Highlights
One of the most common problems in the world is the remarkable changes observed in climate
We present a comparison of the in-sample model fit and out-of-sample forecasting performance for the multiple linear regression (MLR), state space model (SSM), hybrid, seasonal autoregressive integrated moving average (SARIMA), exponential smoothing with state space model (ETS), TBATS, neural network with one hidden layer (NNETAR) and Prophet models and the same models applied with the covariates during the given time period
The issue of predicting precipitation is a challenging process since there are various natural parameters which are involved in the procedure which directly affect precipitation
Summary
One of the most common problems in the world is the remarkable changes observed in climate The effects of these changes on Earth and human beings cannot be ignored if we want to create a more habitable future. One of the most known is global warming, which has a direct effect on climate. There may be no solutions to this change in temperature, but if the amount of precipitation can be predicted, that will help make lives easier and the world more liveable. Planning for these types of future events is crucial during this tumultuous time. Making predictions about such unseasonable and changeable factors affecting the Earth requires some scrutiny and investigation
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