Abstract
Precipitation is the most relevant element in the hydrological cycle and vital for the biosphere. However, when extreme precipitation events occur, the consequences could be devastating for humans (droughts or floods). An accurate prediction of precipitation helps decision-makers to develop adequate mitigation plans. In this study, linear and nonlinear models with lagged predictors and the implementation of a nonlinear autoregressive model with exogenous variables (NARX) network were used to predict monthly rainfall in Labrado and Chirimachay meteorological stations. To define a suitable model, ridge regression, lasso, random forest (RF), support vector machine (SVM), and NARX network were used. Although the results were “unsatisfactory” with the linear models, the specific direct influences of variables such as Niño 1 + 2, Sahel rainfall, hurricane activity, North Pacific Oscillation, and the same delayed rainfall signal were identified. RF and SVM also demonstrated poor performance. However, RF had a better fit than linear models, and SVM has a better fit than RF models. Instead, the NARX model was trained using several architectures to identify an optimal one for the best prediction twelve months ahead. As an overall evaluation, the NARX model showed “good” results for Labrado and “satisfactory” results for Chirimachay. The predictions yielded by NARX models, for the first six months ahead, were entirely accurate. This study highlighted the strengths of NARX networks in the prediction of chaotic and nonlinear signals such as rainfall in regions that obey complex processes. The results would serve to make short-term plans and give support to decision-makers in the management of water resources.
Highlights
Precipitation is one of the main components in the hydrological cycle [1, 2]
Each of the fifty models was fitted with λ 10d, with d {−2, −1.9, −1.8, . . . , 9.9, 10}. e fitted models obtained for each λ that produced the best performance were selected
For Labrado station (Figure 4(a)), the performance grows as lags increase to 18 where the performance in the test set fell. e models fitting the best, for both ridge and lasso, were obtained with around 16 lags
Summary
Precipitation is one of the main components in the hydrological cycle [1, 2]. It is one of the most important variables associated with atmospheric circulation in meteorological studies [3]. It is the main source of recharge in water balance studies from local to regional scales [4]. Is reality is evident in high mountain regions [6] with high time-space variability like the Andes mountain range. An accurate prediction of precipitation (temporal and spatial) can help decision-makers to assess in advance both flood and drought situations [10, 11], and it could support extreme hydrological management and diminish the impacts on the population
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