A Novel Interannual Rainfall Runoff Equation Derived from Ol'Dekop's Model Using Artificial Neural Networks.

In water resources management, modeling water balance factors is necessary to control dams, agriculture, irrigation, and also to provide water supply for drinking and industries. Generally, conceptual and physical models present challenges to find more hydro-climatic parameters, which show good performance in the assessment of runoff in different climatic regions. Accordingly, a dynamic and reliable model is proposed to estimate inter-annual rainfall-runoff in five climatic regions of northern Algeria. This is a new improvement of Ol'Dekop's equation, which models the residual values obtained between real and predicted data using artificial neuron networks (ANNs), namely by ANN1 and ANN2 sub-models. In this work, a set of climatic and geographical variables, obtained from 16 basins, which are inter-annual rainfall (IAR), watershed area (S), and watercourse (WC), were used as input data in the first model. Further, the ANN1 output results and De Martonne index (I) were classified, and were then processed by ANN2 to further increase reliability, and make the model more dynamic and unaffected by the climatic characteristic of the area. The final model proved the best performance in the entire region compared to a set of parametric and non-parametric water balance models used in this study, where the R2Adj obtained from each test gave values between 0.9103 and 0.9923.

A Hybrid Water Balance Machine Learning Model to Estimate Inter-Annual Rainfall-Runoff.

Watershed climatic diversity poses a hard problem when it comes to finding suitable models to estimate inter-annual rainfall runoff (IARR). In this work, a hybrid model (dubbed MR-CART) is proposed, based on a combination of MR (multiple regression) and CART (classification and regression tree) machine-learning methods, applied to an IARR predicted data series obtained from a set of non-parametric and empirical water balance models in five climatic floors of northern Algeria between 1960 and 2020. A comparative analysis showed that the Yang, Sharif, and Zhang's models were reliable for estimating input data of the hybrid model in all climatic classes. In addition, Schreiber's model was more efficient in very humid, humid, and semi-humid areas. A set of performance and distribution statistical tests were applied to the estimated IARR data series to show the reliability and dynamicity of each model in all study areas. The results showed that our hybrid model provided the best performance and data distribution, where the R2Adj and p-values obtained in each case were between (0.793, 0.989), and (0.773, 0.939), respectively. The MR model showed good data distribution compared to the CART method, where p-values obtained by signtest and WSR test were (0.773, 0.705), and (0.326, 0.335), respectively.

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

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.