Assessing the impact of climate change on urban water demand and related uncertainties: a case study of Neyshabur, Iran

Sharafati, Ahmad; Asadollah, Seyed Babak Haji Seyed; Shahbazi, Armin

doi:10.1007/s00704-021-03638-5

Cited by 11 publications

(1 citation statement)

References 77 publications

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“…In this study, the prediction performance of standalone and hybridized SVR models was evaluated using four indices, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash-Sutcliffe Efficiency (NSE), and Correlation Coefficient (R). These four indices have been widely employed in the literature for predictions using SC algorithms (Asadollah et al, 2022;Ehteram et al, 2021;Mokhtari et al, 2022;Sharafati et al, 2021). The prediction performance based on these metrics for both training and testing phases is shown in Tables 5 and 6, respectively.…”

Section: Model Prediction Performancementioning

confidence: 99%

An intelligent approach for estimating aeration efficiency in stepped cascades: optimized support vector regression models and mutual information theory

Asadollah

Sharafati²,

Haghbin³

et al. 2022

Soft Comput

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Section: Model Prediction Performancementioning

confidence: 99%

An intelligent approach for estimating aeration efficiency in stepped cascades: optimized support vector regression models and mutual information theory

Asadollah

Sharafati²,

Haghbin³

et al. 2022

Soft Comput

Self Cite

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Comparison of precipitation projections of CMIP5 and CMIP6 global climate models over Yulin, China

Shiru

Chung

Shahid

et al. 2021

Theor Appl Climatol

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Assessment of climate change impact on probable maximum floods in a tropical catchment

Sammen

Mohammed

Ghazali

et al. 2022

Theor Appl Climatol

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Increased extreme rainfall due to climate change will increase the probable maximum ood (PMF) and pose a severe threat the critical hydraulic infrastructure like hydroelectric and ood protection dams. As the rainfall extremes in tropical regions are highly sensitive to global warming, increase PMF can be much higher in the tropics. A study has been conducted to assess the impact of climate change on PMF in a tropical catchment located in peninsular Malaysia. A lumped hydrological model, Mike NAM, is calibrated and validated with observed climate and in ow data of Tenmengor reservoir, located in the state of Perak of Peninsular Malaysia. Regional climate model projected rainfall is used to generate probable maximum precipitation (PMP) for future periods. The hydrological model is used to simulate PMF from PMP estimated for the historical and two future periods, early (2031−2045) and late (2060−2075). The results revealed the NAM model could simulate the river ow with a Nash-Sutcliffe e ciency of 0.74 and root mean square error of 0.51. The application of the model with projected rainfall revealed an increase in PMP by 162 to 507% and 259 to 487% during early and late periods for different return periods ranging from 5 to 1000 years. This would cause an increase in PMF by 48.9% and 122.6% during early and late periods. A large increase in PMF indicates the possibility of devastating oods in the study area due to climate change.

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Assessing the impact of climate change on urban water demand and related uncertainties: a case study of Neyshabur, Iran

Cited by 11 publications

References 77 publications

An intelligent approach for estimating aeration efficiency in stepped cascades: optimized support vector regression models and mutual information theory

An intelligent approach for estimating aeration efficiency in stepped cascades: optimized support vector regression models and mutual information theory

Comparison of precipitation projections of CMIP5 and CMIP6 global climate models over Yulin, China

Assessment of climate change impact on probable maximum floods in a tropical catchment

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