Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment

Filling Gaps in Daily Precipitation Series Using Regression and Machine Learning in Inter-Andean Watersheds

As precipitation is a fundamental component of the global hydrological cycle that governs water resource distribution, the understanding of its temporal and spatial behavior is of great interest, and exact estimates of it are crucial in multiple lines of research. Meteorological data provide input for hydroclimatic models and predictions, which generally lack complete series. Many studies have addressed techniques to fill gaps in precipitation series at annual and monthly scales, but few have provided results at a daily scale due to the complexity of orographic characteristics and in some cases the non-linearity of precipitation. The objective of this study was to assess different methods of filling gaps in daily precipitation data using regression model (RM) and machine learning (ML) techniques. RM included linear regression (LRM) and multiple regression (MRM) algorithms, while ML included multiple regression algorithms (ML-MRM), K-nearest neighbors (ML-KNN), gradient boosting trees (ML-GBT), and random forest (ML-RF). This study covered the Malas, Omas, and Cañete River (MOC) watersheds, which are located on the Pacific Slope of central Peru, and a nineteen-year period of records (2001–2019). To assess model performance, different statistical metrics were applied. The results showed that the optimized machine learning (OML) models presented the least variability in estimation errors and the best approximation of the actual data from the study zone. In addition, this investigation shows that ML interprets and analyzes non-linear relationships between rain gauges at a daily scale and can be used as an efficient method of filling gaps in daily precipitation series.

Assessing Machine Learning Models for Gap Filling Daily Rainfall Series in a Semiarid Region of Spain

The presence of missing data in hydrometeorological datasets is a common problem, usually due to sensor malfunction, deficiencies in records storage and transmission, or other recovery procedures issues. These missing values are the primary source of problems when analyzing and modeling their spatial and temporal variability. Thus, accurate gap-filling techniques for rainfall time series are necessary to have complete datasets, which is crucial in studying climate change evolution. In this work, several machine learning models have been assessed to gap-fill rainfall data, using different approaches and locations in the semiarid region of Andalusia (Southern Spain). Based on the obtained results, the use of neighbor data, located within a 50 km radius, highly outperformed the rest of the assessed approaches, with RMSE (root mean squared error) values up to 1.246 mm/day, MBE (mean bias error) values up to −0.001 mm/day, and R2 values up to 0.898. Besides, inland area results outperformed coastal area in most locations, arising the efficiency effects based on the distance to the sea (up to an improvement of 63.89% in terms of RMSE). Finally, machine learning (ML) models (especially MLP (multilayer perceptron)) notably outperformed simple linear regression estimations in the coastal sites, whereas in inland locations, the improvements were not such significant.

Hybrid Model for Time Series of Complex Structure with ARIMA Components

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

Assessing the Best Gap-Filling Technique for River Stage Data Suitable for Low Capacity Processors and Real-Time Application Using IoT

Hydrometeorological data sets are usually incomplete due to different reasons (malfunctioning sensors, collected data storage problems, etc.). Missing data do not only affect the resulting decision-making process, but also the choice of a particular analysis method. Given the increase of extreme events due to climate change, it is necessary to improve the management of water resources. Due to the solution of this problem requires the development of accurate estimations and its application in real time, this work present two contributions. Firstly, different gap-filling techniques have been evaluated in order to select the most adequate one for river stage series: (i) cubic splines (CS), (ii) radial basis function (RBF) and (iii) multilayer perceptron (MLP) suitable for small processors like Arduino or Raspberry Pi. The results obtained confirmed that splines and monolayer perceptrons had the best performances. Secondly, a pre-validating Internet of Things (IoT) device was developed using a dynamic seed non-linear autoregressive neural network (NARNN). This automatic pre-validation in real time was tested satisfactorily, sending the data to the catchment basin process center (CPC) by using remote communication based on 4G technology.