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In this study, the performance of two popular conceptual models, a classic artificial intelligence (AI) model, and a deep learning model is compared using simple and differential split-sample tests (SSST and DSST). Finally, the two conceptual models and the classic AI model are combined employing a model ensemble technique. The SSST and DSST schemes confirmed the ability of both types of models (conceptual and AI) to capture the hydrological response to rainfall events, although the conceptual models showed a better performance in the DSST scheme. The ensemble technique enhanced the modelling performance up to 22% and 35% in the calibration and verification steps. This study highlights the importance of applying DSST using existing data to build further confidence in model projections, and suggests that the use of model ensemble techniques combining conceptual and AI models can enhance the performance of rainfall-runoff modelling in the context of climate change. [ABSTRACT FROM AUTHOR]

This study aimed at time-space estimations of monthly precipitation via a two-stage modeling framework. In temporal modeling as the first stage, three different Artificial Intelligence (AI) models were applied to observed precipitation data from 7 gauges located at Northern Cyprus. In this way 2 different input scenarios proposed, by employing different input combinations. Afterwards, the outputs of single AI models were used to generate ensemble techniques to enhance the precision of modeling by the single AI models. For this purpose, 2 linear and 1 non-linear methods of ensembling were designed and afterwards, the results were evaluated. In the second stage, for estimation of the spatial distribution of precipitation over whole region, the results of temporal modeling were used as inputs for the Inverse Distance Weighting (IDW) spatial interpolator. The cross-validation was finally applied to evaluate the overall accuracy of the proposed hybrid spatiotemporal modeling approach. The obtained results in temporal modeling stage demonstrated that the non-linear ensemble technique provided more accurate results. Results of spatial modeling stage indicated that IDW scheme is a good choice for spatial estimation of the precipitation. The overall results show that the combination of temporal and spatial modeling tools could simulate the precipitation appropriately by serving unique features of both tools. [ABSTRACT FROM AUTHOR]

In recent times, significant research has been carried out into developing and applying soft computing techniques for modeling hydro-climatic processes such as seepage modeling. It is necessary to properly model seepage, which creates groundwater sources, to ensure adequate management of scarce water resources. On the other hand, excessive seepage can threaten the stability of earthfill dams and infrastructures. Furthermore, it could result in severe soil erosion and consequently cause environmental damage. Considering the complex and nonlinear nature of the seepage process, employing soft computing techniques, especially applying pre-post processing techniques as hybrid methods, such as wavelet analysis, could be appropriate to enhance modeling efficiency. This review paper summarizes standard soft computing techniques and reviews their seepage modeling and simulation applications in the last two decades. Accordingly, 48 research papers from 2002 to 2021 were reviewed. According to the reviewed papers, it could be understood that regardless of some limitations, soft computing techniques could simulate the seepage successfully either through groundwater or earthfill dam and hydraulic structures. Moreover, some suggestions for future research are presented. This review was conducted employing preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. [ABSTRACT FROM AUTHOR]

The aim of ensemble precipitation prediction in this paper was to achieve the best performance via artificial intelligence (AI) based modeling. In this way, ensemble AI based modeling was proposed for prediction of monthly precipitation with three different AI models (feed forward neural network-FFNN, adaptive neural fuzzy inference system-ANFIS and least square support vector machine-LSSVM) for the seven stations located in the Turkish Republic of Northern Cyprus (TRNC). Two scenarios were examined each having specific inputs set. The scenario 1 was developed for predicting each station's precipitation through its own data at previous time steps while in scenario 2, the central station's data were imposed into the models, in addition to each station's data, as exogenous input. Afterwards, the ensemble modeling was generated to improve the performance of the precipitation predictions. To end this aim, two linear and one non-linear ensemble techniques were used and then the obtained outcomes were compared. In terms of efficiency measures, the averaging methods employing scenario 2 and non-linear ensemble method revealed higher prediction efficiency. Also, in terms of Skill score, non-linear neural ensemble method could enhance predicting efficiency up to 44% in the verification step. [ABSTRACT FROM AUTHOR]