A new approach for processing climate missing databases applied to daily rainfall data in Soummam watershed, Algeria

Abstract

Missing data is a very frequent problem in climatology, it influences on the quality of results that will afford in hydrological studies, as well as water resources management. This paper proposes a new imputation algorithm, based on the optimization of some regression methods, which are hot deck, k-nearest-neighbors imputation, weighted k-nearest-neighbors imputation, multiple imputation, linear regression and simple average method. The choice of these methods was justified by qualitative and quantitative statistical tests analysis. However, the reliability of obtained results depends mainly on percentage of missing data, choice of neighboring stations and data missingness mechanism which should be missing at random. During the study it was found that the most of stations in Soummam watershed don't have a good correlation because the large loss in rainfall data or the geology of watershed which gives a relationship between station position and rainfall variability. For this case, principal component analysis is applied on a set of stations; it showed a positive impact of altitude, latitude and longitude on correlation index between selected stations. The graphical analysis of the normal law on RMSE values, which were obtained by applying the proposed technique in several random cases of missingness, that are 4%, 8%, 12% and 16% respectively, it confirmed the validity and the performance of this approach.

Assessment of the performance of CORDEX regional climate models in simulating rainfall and air temperature over southwest Ethiopia

“…The choice of the methods is based on percentage of data missed and choice of neighboring stations. When the amount of the data filled are less than 5%, linear regression can be used by identifying the relationship between the observed data of neighboring stations and that of reference station (Aieb et al, 2019). In this study, the recorded data had missed values randomly and the percentage of the missing data were less than 4%.…”

The assessment of annual rainfall field by applying different interpolation methods in the state of Rio Grande do Sul, Brazil

“…It is possible to verify that the models used managed, in a certain (2021) 3:687 | https://doi.org/10.1007/s42452-021-04679-1 way, to capture the behavior of the series, since the margin of error was low and variable depending on the year (Table 4). This study may have ramifications such as those carried out by Barrios,Trincado,and Garreaud [74] in monthly precipitation records and Aieb et al [75] to daily rainfall considering data imputation methods such as an artificial neural network (ANN), multiple linear regression (MLR), among others.…”

Statistical modeling of monthly rainfall variability in Soummam watershed of Algeria, between 1967 and 2018

“…The Table 1 showed that the missing rainfall data had an interval range of (1.1%, 14.2%), whereas the temperature values are less frequent, which had an interval range of (0.2%, 4.1%). Hence, the missing data have been processed by using the new approach proposed in our former work to get a continuous and reliable time series (Aieb et al, 2019).…”