Optimal operation of dams/reservoirs emphasizing potential environmental and climate change impacts

Mahdi studied the potential ecological and climate change impacts on management of dams. He developed several new optimization frameworks in which benefits of dams are maximized, while above impacts are mitigated. Governments and consulting engineers can use the proposed frameworks for managing dams considering environmental challenges in river basins

An Ecological Expert System Optimization for Assessing Environmental Water Requirements of Hypersaline Lakes

The present study proposes an applicable methodology to optimize environmental water requirement of hypersaline lakes with a focus on Urmia lake as the case study in which remote sensing analysis, machine learning model and fuzzy expert system are linked. A machine learning model was developed to simulate effective abiotic parameters in which bands of operational land imager (Landsat 8) were inputs and depth and total dissolved solids were the outputs of the model. Moreover, an ecological expert system using Mamadani fuzzy inference system was developed to generate the habitat suitability map for the selected target species. Then, a multivariate linear model was developed to assess unit habitat suitability in which water level and total inflow of the lake were the variables of the model. An optimization model was developed to assess environmental water requirement in which habitat suitability between natural and regulated flows and water supply loss was minimized. The multivariate linear model was applied to assess habitat suitability in the optimization model. Based on the results in the case study, the proposed combined model is able to balance the ecological requirements and water demand by allocating 60% and 40% of total inflow to environmental water requirement and water demand respectively. Average habitat loss proposed by the optimal environmental water requirement was less than 20% which implies the robustness of the model. Generating habitat suitability maps of the lake by a reliable method which is used in the environmental flow optimization might be the significance of the proposed method

Balancing environmental impacts and economic benefits of agriculture under the climate change through an integrated optimization system

The present study proposes a framework to mitigate impact of climate change on the rice production by maximizing the yield while the energy use and ecological impacts on the river ecosystem as the irrigation source are mitigated. Coupled general circulation model- soil and water assessment tool (SWAT) was utilized to project the impact of climate change on the stream flow. Fuzzy physical habitat simulation was applied to develop the ecological impact function of the river. Moreover, a data-driven model was developed to predict the rice yield through changing water and energy consumption. Finally, all the simulations were utilized in the structure of the optimization model in which minimizing loss of the production, greenhouse gas emission by reducing energy use and physical habitat loss were considered as the objectives. Based on the results, the Nash–Sutcliffe model efficiency coefficient of the SWAT is 0.7 that demonstrates its reliability for simulating the impact of climate change on river flow. The optimization model is able to reduce the impact of climate change on yield of production by balancing water and energy use. In the most pessimistic scenario, water use should approximately be reduced 25% for protecting river ecosystem. However, the optimization model approximately increased energy use 16% for preserving the yield of the rice. Conversely, model decreased the energy use 40% compared with the current condition due to increasing water supply. Moreover, physical habitat loss is less than 50% that means the combined optimization model is able to protect river habitats properly

A hydro-environmental optimization for assessing sustainable carrying capacity

The present study proposes an applicable method to determine the population carrying capacity of urban areas in which ecological impacts of river ecosystem as the source of water supply and sustainable population growth are linked. A multiobejctive optimization method was developed in which two objectives were considered: 1) minimizing the fish population loss as the environmental index of the river ecosystem and 2) minimizing the difference between initial population carrying capacity and the sustainable population carrying capacity. The ecological impacts of the river ecosystem were assessed through the potential fish population as an environmental index using several artificial intelligence and regression models. Based on case study results, the initial plan of development is not reliable because ecological impacts on the river ecosystem are remarkable. The proposed method is able to reduce the ecological impacts. However, the sustainable population carrying capacity is considerably lower than the initial planned population. It is needed to reduce the planned population more than 45% in the case study. Habitat loss is less than 35% which means the optimization model is able to find an optimal solution for balancing environmental requirements and humans’ needs. In other words, the optimization model balances the needs of environment and water supply by reducing 45% of population and decreasing habitat loss to 35%

Detecting land use changes using hybrid machine learning methods in the Australian tropical regions

The present study evaluates the application of the hybrid machine learning methods to detect changes of land use with a focus on agricultural lands through remote sensing data processing. Two spectral images by Landsat 8 were applied to train and test the machine learning model. Feed forward neural network classifier was utilized as the machine learning model in which two evolutionary algorithms including particle swarm optimization and invasive weed optimization were applied for the training process. Moreover, three conventional training methods including Levenberg–Marquardt back propagation (LM), Scaled conjugate gradient backpropagation (SCG) and BFGS quasi-Newton backpropagation (BFG) were used for comparing the robustness and reliability of the evolutionary algorithms. Based on the results in the case study, evolutionary algorithms are not a reliable method for detecting changes through the remote sensing analysis in terms of accuracy and computational complexities. Either BFG or LM is the best method to detect the agricultural lands in the present study. BFG is slightly more robust than the LM method. However, LM might be preferred for applying in the projects due to low computational complexities

Efficiency of coupled invasive weed optimization-adaptive neuro fuzzy inference system method to assess physical habitats in streams

This study presents a coupled invasive weed optimization-adaptive neuro fuzzy inference system method to simulate physical habitat in streams. We implement proposed method in Lar national park in Iran as one of the habitats of Brown trout in southern Caspian Sea basin. Five indices consisting of root mean square error (RMSE), mean absolute error (MAE), reliability index, vulnerability index and Nash–Sutcliffe model efficiency coefficient (NSE) are utilized to compare observed fish habitats and simulated fish habitats. Based on results, measurement indices demonstrate model is robust to assess physical habitats in rivers. RMSE and MAE are 0.09 and 0.08 respectively. Besides, NSE is 0.78 that indicates robustness of model. Moreover, it is necessary to apply developed habitat model in a practical habitat simulation. We utilize two-dimensional hydraulic model in steady state to simulate depth and velocity distribution. Based on qualitative comparison between results of model and observation, coupled invasive weed optimization-adaptive neuro fuzzy inference system method is robust and reliable to simulate physical habitats. We recommend utilizing proposed model for physical habitat simulation in streams for future studies

Reducing impacts of rice fields nitrate contamination on the river ecosystem by a coupled SWAT reservoir operation optimization model

The present study proposes a multipurpose reservoir operation optimization for mitigating impact of rice fields’ contamination on the downstream river ecosystem. The developed model was applied in the Tajan River basin in Mazandaran Province, Iran, in which the rice is the main crop. We used soil and water assessment tool (SWAT) to simulate inflow of the reservoir and nitrate load at downstream river reach. Nash–Sutcliffe model efficiency coefficient was used to measure the robustness of SWAT. NSE indicated that SWAT is acceptable to simulate nitrate load of the rice fields. The results of SWAT was applied in the structure of a multipurpose reservoir operation optimization in which three metaheuristic algorithms including differential evolution algorithm, particle swarm optimization and biogeography-based algorithm were utilized in the optimization process. Reliability index, mean absolute error and failure index were used to measure the robustness of the optimization algorithms. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution was utilized to select the best algorithm. Based on results, particle swarm optimization is the best method to optimize reservoir operation in the case study. The reliability index and mean absolute error for water supply are 0.6 and 5 million cubic meters, respectively. Furthermore, the failure index of contamination is 0.027. Hence, it could be concluded that the proposed optimization system is reliable and robust to mitigate losses and nitrate contamination simultaneously. However, its performance is not perfect for minimizing impact of contamination in all the simulated months

Linking ecohydraulic simulation and optimization system for mitigating economic and environmental losses of reservoirs

Balancing the benefits and environmental degradations of the reservoirs is a challenging issue in the reservoir management. The present study proposes and evaluates an integrated framework to optimize reservoir operation in which hydropower loss and economic loss of irrigation supply are minimized while ecological degradations at downstream river are alleviated. The ecohydraulic simulation was utilized in the structure of the reservoir operation optimization. Reservoir operation losses and environmental degradations were minimized in three hydrological conditions including dry years, normal years and wet years. Moreover, the cropping pattern optimization was applied to mitigate the economic loss of irrigation supply as the main responsibility of the reservoir in the study area. Particle swarm optimization was applied in the reservoir operation optimization. Based on the results in the case study, reliability indices of hydropower production and farmers’ revenue are 15–25 and 30–60%, respectively. Moreover, the physical habitat loss is considerably reduced in all hydrologic conditions by proposing optimal environmental flow. The proposed method is able to provide a fair balance between downstream environmental degradations and economic benefits of the reservoir including farmers’ revenue and hydropower production. Low computational complexities are the most important strength point for the developed model

Reducing the conflict of interest in the optimal operation of reservoirs by linking mesohabitat hydraulic modeling and metaheuristic optimization

The present study proposes a novel framework to optimize the reservoir operation through linking mesohabitat hydraulic modeling and metaheuristic optimization to mitigate environmental impact downstream of the reservoir. Environmental impact function was developed by mesohabitat hydraulic simulation. Then, the developed function was utilized in the structure of the reservoir operation optimization. Different metaheuristic algorithms including practice swarm optimization, invasive weed optimization, differential evolution and biogeography-based algorithm were used to optimize reservoir operation. Root mean square error (RMSE) and reliability index were utilized to measure the performance of algorithms. Based on the results in the case study, the proposed method is robust for mitigating downstream environmental impacts and sustaining water supply by the reservoir. RMSE for mesohabitats is 8%, which indicates the robustness of proposed method to mitigate environmental impacts at downstream. It seems that providing environmental requirements might reduce the reliability of water supply considerably. Differential evolution algorithm is the best method to optimize reservoir operation in the case study

A simulation-optimization framework for reducing thermal pollution downstream of reservoirs

Thermal pollution is an environmental impact of large dams altering the natural temperature regime of downstream river ecosystems. The present study proposes a simulation-optimization framework to reduce thermal pollution downstream from reservoirs and tests it on a real-world case study. This framework attempts to simultaneously minimize the environmental impacts as well as losses to reservoir objectives for water supply. A hybrid machine-learning model is applied to simulate water temperature downstream of the reservoir under various operation scenarios. This model is shown to be robust and achieves acceptable predictive accuracy. The results of simulation-optimization indicate that the reservoir could be operated in such a way that the natural temperature regime is reasonably preserved to protect downstream habitats. Doing so, however, would result in significant trade-offs for reservoir storage and water supply objectives. Such trade-offs can undermine the benefits of reservoirs and need to be carefully considered in reservoir design and operation