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Dam and reservoir systems (DRSs) are crucial aspects of the infrastructure necessary for reliable water resource management. Nowadays, DRSs are being increasingly affected by numerous natural and anthropogenic impacts (aging and outdated infrastructure, climate change, natural hazards, global crises, etc.). Hence, additional pressure on DRS management is being applied as DRSs must be operated in adverse operating conditions, outside of their design envelopes. Since there is no practical way to redesign DRSs to meet all possible adverse conditions, efficient simulation tools are necessary for various "what-if" analyses. A system dynamics (SD) approach can be used, as it has shown the capacity to comprehend the intrinsic system complexity. In this paper, an 11-step framework for the dynamic modelling of reduced functionality in a DRS and the emulation of the system operation in adverse conditions is proposed. The framework covers the system model design, input scenario generation, system simulation, and performance evaluation steps. A focus is placed on the steps related to system decomposition, the identification of failure-indicative parameters, the definition and implementation of failure functions in the subsystem dynamic models, and dynamic failure modelling. Through these steps, a novel procedure is proposed for the dynamic modelling of the DRS subsystems' failures (reduced functionality), common in the operation of DRSs under adverse conditions. For each subsystem prone to failure, failure-indicative parameters are identified. Failure functions employing generic functionality indicators, with values spanning from 0 to 1, are suggested to modify the values of the failure-indicative parameters in simulations and emulate the component failure impacts on DRS operation. Possibilities for modelling failure modes for different subsystems, varying in nature, duration, and magnitude are discussed. Potential physical damage to the system components, increases in measurement uncertainty, and the lack of the spare parts during periods of global crisis are applied as disturbances to the Pirot DRS case study to illustrate the possibilities of the suggested framework's application for DRS failure modelling. It was concluded that the proposed framework allowed for the detection of severe impacts on system performance, emphasizing the need for DRS dynamic failure modelling in system analysis. [ABSTRACT FROM AUTHOR]

Modeling seepage through geological formations located near the dam site or dam bodies is a challenging task in dam engineering. In order to monitor the seepage, piezometric devices are installed on sections of the dam. The objective of this study is to develop a neuro-fuzzy model to predict the water level in piezometers of the Iron Gate 2 dam. The neuro-fuzzy model was developed using experimental data which was collected during a period of nine years. The measurements of tailwater level taken on the same day, one day before, and two days before the measurements of piezometer were input variables, and the water level in the examined piezometer was the target output in the neuro-fuzzy model. The measured data has been compared with the results of neuro-fuzzy model on the basis of correlation coefficient (r), coefficient of determination (R²), mean square error (MSE) and mean absolute error (MAE). Comparing the experimental data with the values modeled by the neuro-fuzzy system indicates that the computational intelligence models provide very accurate results. [ABSTRACT FROM AUTHOR]

Highlights: [•] We construct the SVR models for the prediction of the nonlinear structural behaviour. [•] The SVR-NARX models were developed and tested using experimental data. [•] The parameters of the SVR models are specified with the trial-and-error method. [•] Measured values were compared with predicted values. [•] The SVR models for the prediction of the dam displacement have excellent performance. [ABSTRACT FROM AUTHOR]