Reservoir Evaporation Forecasting Based on Climate Change Scenarios Using Artificial Neural Network Model

Ahi, Yeşim; Dilcan, Çiğdem Coşkun; Köksal, Daniyal Durmuş; Gültaş, Hüseyin Tevfik

doi:10.1007/s11269-022-03365-0

Cited by 9 publications

(1 citation statement)

References 27 publications

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“…Furthermore, the FGD process is fully enclosed, and climate factors only exert minimal influence on it. The combination of these factors has resulted in research findings that are different from the result of Ahi et al [39] and Kayhomayoon et al [40]; both of their results indicate scenarios of increased evaporation.…”

Section: Discussionmentioning

confidence: 69%

Assessing the Impact of Climate Change on Water Usage in Typical Industrial Enterprises

et al. 2023

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Climate change and water scarcity are significant global issues both currently and in the future. Investigating the changes in industrial water usage under this backdrop holds crucial guidance for enabling adaptive changes within industries. It offers methods and case support for assessing the impact of climate on the water usage processes of industrial enterprises as well. This study employed a physical model to examine the impact of climate factors on the primary water consumption processes of a selected case of a coal-fired power plant. Additionally, by utilizing the ScenarioMIP experimental outcomes from CMIP6 models, the future water consumption processes were predicted under four different SSPs-RCPs scenarios. A relevant LSTM neural network was constructed based on the primary water consumption calculation to establish connections between the power generation process, meteorological process, water consumption process, and water intake process. Findings from the study reveal that the annual average primary water consumption showed a decreasing trend in different SSPs-RCPs scenarios. Specifically, there were reductions of 1600, 5300, 9000, and 11,400 t/year in each respective scenario from SSP1–2.6 to SSP5–8.5. Conversely, the water intake exhibited a gradual increase, with increments of 2000, 5600, 9200, and 10,000 t/year, respectively. Moreover, the impact of climate change on evaporation showed an annual decrease of less than 0.056%, while the increase in water intake was below 0.044%. Under the SSPs-RCPs scenarios, both water intake and consumption exhibited a gradually increasing trend with fluctuations ranging from 1–2.6 to 5–8.5. However, the annual trends remained relatively stable. It is crucial to acknowledge that climate change has amplified the uncertainty surrounding water intake and consumption. Industrial enterprises should proactively ensure the stability of their production processes in response to climate change.

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Section: Discussionmentioning

confidence: 69%

Assessing the Impact of Climate Change on Water Usage in Typical Industrial Enterprises

et al. 2023

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Gaussian mutation-alpine skiing optimization algorithm-recurrent attention unit-gated recurrent unit-extreme learning machine model: an advanced predictive model for predicting evaporation

Ehteram,

Barzegari Banadkooki,

Afshari Nia

2024

Stoch Environ Res Risk Assess

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A novel modeling approach on the water–electricity–climate nexus in the context of resource sustainability

Coskun Dilcan,

Aydinalp Koksal

2024

Clean Techn Environ Policy

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Water and energy have become essential resources and must be wisely managed for a sustainable future. This paper explores the relationship between water consumption and electricity generation in hydropower plants with dams under different climate change scenarios to contribute to the policy perspectives with a new tool and method to sustain the future. Still, as a reliable forecasting tool, the evaluation of the adaptive neuro-fuzzy inference system model has not been tested for forecasting water consumption during electricity generation. Thus, this study uses this modeling approach to generate reliable water consumption estimates based on electricity generation. The operational data of 78 hydroelectric power plants with dams and meteorological parameters were used as input variables, while water consumption was the output parameter in the model. The dataset was randomly divided into training and testing sets, and 85–15% data splitting presented the best-fitted model. The lowest mean average percent error of the hydroelectric power plants' model resulted in 9.59%, and the coefficient of determination of the model was 0.97, which showed that the developed model presented acceptable prediction performance. Various climate change scenarios are applied to analyze the effects of climate parameters on the water consumption of hydropower plants. The annual hydroelectric power plant water consumption and water intensity were estimated between 2,609 million m3 and 4,393 million m3, and 50,768 m3/GWh and 85,487 m3/GWh, respectively, based on climate change scenarios. The study concludes with significant policy suggestions to endorse this approach. Graphical abstract

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Reservoir Evaporation Forecasting Based on Climate Change Scenarios Using Artificial Neural Network Model

Cited by 9 publications

References 27 publications

Assessing the Impact of Climate Change on Water Usage in Typical Industrial Enterprises

Assessing the Impact of Climate Change on Water Usage in Typical Industrial Enterprises

Gaussian mutation-alpine skiing optimization algorithm-recurrent attention unit-gated recurrent unit-extreme learning machine model: an advanced predictive model for predicting evaporation

A novel modeling approach on the water–electricity–climate nexus in the context of resource sustainability

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