Multi-objective Optimization of water resources in real time based on integration of NSGA-II and support vector machines.

One of the management strategies of water resources systems is the combination of simulation and optimization models to achieve the optimal policies of reservoir operation in the form of specific optimization. This study utilizes an integration of the NSGA-II multi-objective algorithm and WEAP simulator model so that the first objective is to maximize the reliability of providing the needs in front of the second goal, i.e., to minimize the drawdown the water table at the end of the operation time. The dam rule curve or the amount of released volume from the reservoir is optimized to supply downstream uses in these conditions. However, in certain optimizations, the optimal solutions cannot be generalized to other possible inputs to the reservoir, and if the inflow to the reservoirs changes, the obtained optimal solutions are no longer efficient and the system must be re-optimized in the form of an optimizer algorithm. Therefore, to solve this problem, a new method is extended on the basis of the combination of the support vector machine and NSGA-II algorithm for optimal real-time operation of the system. The results demonstrate that the average error rate of optimal rules derived from support vector machines is less than 2.5% compared to the output of the NSGA-II algorithm in the verification step, which indicates the efficiency of this method in predicting the optimal pattern of the dam rule curve in real time. In this structure, based on the inflow to the reservoir, the volume of water storage in the reservoir and changes in the reservoir storage (at the beginning of the month) and the downstream demands of the current month, the optimal release amount can be achieved in real time. Therefore, the developed support vector machine has the ability to provide optimal operation policies based on new data of the inflow to the dam in a way that allows us optimally manage the system in real time.

Simulation of time-series groundwater parameters using a hybrid metaheuristic neuro-fuzzy model.

The estimation of qualitative and quantitative groundwater parameters is an essential task. In this regard, artificial intelligence (AI) techniques are extensively utilized as accurate, trustworthy, and cost-effective tools. In the present paper, two hybrid neuro-fuzzy models are implemented for the prediction of groundwater level (GWL) fluctuations, as well as variations of Cl - and HCO3 - in the Karnachi well, Kermanshah, Iran in monthly intervals within a 13-year period from 2005 to 2018. In order to develop AI models, the adaptive neuro-fuzzy inference system (ANFIS), firefly algorithm (FA), and wavelet transform (WT) are used. In other words, two hybrid models including ANFIS-FA (adaptive neuro-fuzzy inference system-firefly algorithm) and WANFIS-FA (wavelet-adaptive neuro-fuzzy inference system-firefly algorithm) are utilized for the estimation of the quantitative and qualitative parameters. Firstly, influencing lags of the time-series of the qualitative and quantitative parameters are identified using the autocorrelation function. Then, four and eight separate models are developed for the approximation of GWLs and qualitative parameters (i.e. Cl - and HCO3 -), respectively. It is worth to mention that about 75% of observed values are assigned to train the hybrid AI models, while the rest (i.e. 25%) to test them. Sensitivity analysis results reveal that the WANFIS-FA models display more acceptable performance than the ANFIS-FA ones. Also, the estimations of MAE, NSC, and SI for the simulation of HCO3 - by the superior model of the WANFIS-FA are obtained to be 0.040, 0.988, and 0.022, respectively. In addition, the lags (t-1), (t-2), (t-3), and (t-4) are ascertained as the most effective time-series lags for the estimation of Cl - .

Multi-objective optimization of quantitative-qualitative operation of water resources systems with approach of supplying environmental demands of Shadegan Wetland.

Irregular withdrawals from water resources followed by the increase of the cultivation lands and the construction of Marun and Jarahi Dams on upstream rivers of the Shadegan Wetland have led to severe hydrological changes as well as increased salinity of the wetland inflow in some periods. The aim of this study is to develop a simulator-optimizer coupling model for proper planning and management of resource allocation to the upstream of Shadegan Wetland. In addition to maximizing the supply of basin demands during the operation period, this model aims to reduce the salinity of the inflow to Shadegan Wetland. Due to the importance of the wetland as a seasonal habitat for birds and the importance of protecting its ecosystem, the development of a quantitative-qualitative optimization model for optimal use of available water resources is the aim of this study. First, based on current conditions, the prepared model is developed as a reference scenario for a future 30-year period(2021-2050). To achieve the best system efficiency in terms of quality and quantity, the optimization is performed by means of the NSGA-II algorithm. The results indicate that the optimizer model performs appropriately in supplying various demands and also decreasing the salinity of the inflow to Shadegan Wetland compared to the reference scenario so that in addition to supplying the demands with more than92% reliability in the whole system, it is expected that the salinity of the river at the entrance to Shadegan Wetland to be reduced by about50%., especially in low water months. The coupling model proposed in this research is applicable for other study areas with quantitative-qualitative operation approach and is able to detect critical points of rivers in terms of quantity and quality. This model has also the capability of providing optimal solutions for improving river conditions as well as downstream ecosystems.

Evaluation of shape factor impact on discharge coefficient of side orifices using boost simulation model with extreme learning machine data-driven.

In this paper, for the first time, the impact of the shape factor on the discharge coefficient of side orifices is evaluated using the novel Extreme Learning Machine (ELM) model. In addition, the Monte Carlo simulations (MCs) are applied to assess the accuracy of the modelling. Furthermore, the validation is conducted by means of the k-fold cross-validation approach (with k = 5). In other words, the most optimized number of hidden neurons is chosen to be equal to 30 and the results of all activation functions of the extreme learning machine are examined and the sigmoid activation function is selected for simulating the discharge coefficient. Subsequently, two modelling combinati0ons are introduced using the input parameters and five different extreme learning machine models are also developed. The analysis of the modelling results exhibits that the model with the shape factor is more accurate. The superior model is a function of all input parameters reasonably estimating the discharge coefficient. For example, the values of R and MAPE for this model are estimated to be 0.990 and 0.223, respectively. The results of the superior model are also compared with the empirical equations and it was shown that this model has higher accuracy.