Predicting of daily Khazir basin flow using SWAT and hybrid SWAT-ANN models

Kassem, Abdulwahd; Raheem, Adil M.; Khidir, Khalid M.; Alkattan, Mohammad

doi:10.1016/j.asej.2019.10.011

Cited by 29 publications

(11 citation statements)

References 13 publications

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“…Also, ANN model performance in this study shows that it can be used in watershed with no gauging stations at the outlet when the hydrological response does not change much throughout the watershed. This is comparable to the studies by Meresa (2019) and Kassem et al (2020) which revealed that ANN approach is suitable to predict runoff in ungauged catchment with a reasonable accuracy. We have assumed that the value of peak discharge at any day t is a function of the maximum rainfall for consecutive 3 days ( R t , R t −1 , R t −2 ) and temperature ( T max and T min ) of the flood day t .…”

Section: Discussionsupporting

confidence: 90%

An artificial neural network‐hydrodynamic coupled modeling approach to assess the impacts of floods under changing climate in the East Rapti Watershed, Nepal

Bhattarai

Pandey

et al. 2022

J Flood Risk Management

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Recurring floods have devastating consequences on the East Rapti Watershed (ERW), but effective mitigation/adaptation measures are lacking. This article aims at establishing a rainfall-runoff (RR) relationship; estimating depth and extent of inundation under climate change scenarios; assessing impacts on the socio-economy; and identifying and evaluating adaptation strategies in the ERW. Artificial Neural Network (ANN) was used to generate peak flows which were then entered into a hydraulic model to simulate inundation. Results were validated with field survey. The calibrated and validated RR and hydraulic models were fed with projected future climate (2021-2050) derived from multiple regional-climate-models to assess the changes in inundation. Results showed the peak discharge likely exceeds 10,500 m 3 /s at the ERW outlet in the extreme future flood scenario with corresponding inundation of 80 km 2 and up to a depth of 11 m sweeping away over 1000 houses and 19 km 2 of agricultural land in the critical areas. Constructing a 17 km long embankment in the critical areas along the right bank of the East Rapti River could reduce the flood spread by 35%, safeguarding 78% of the houses and saving 51% agricultural land compared with the scenarios without the embankment. K E Y W O R D S adaptation strategies, ANN, climate change, East Rapti Watershed, flood modelling, HEC-RAS | INTRODUCTIONFlood, a major natural disaster across the globe, accounted for 44% of all disaster events during 2000-2019 worldwide (UNDRR, 2020). About 41% of the total flood events during the period was observed in Asia alone impacting 1.5 billion people. Climate change (CC) is further exacerbating extreme events such as floods and exposing more population and property at risk (Fahad & Wang, 2019). Nepal ranks 30th in terms of vulnerability to flood risk, the problem being more prominent in the flat southern plains (UNDP, 2021).Flood modelling and hazard mapping are wellestablished methods for effectively assessing associated

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Section: Discussionsupporting

confidence: 90%

An artificial neural network‐hydrodynamic coupled modeling approach to assess the impacts of floods under changing climate in the East Rapti Watershed, Nepal

Bhattarai

Pandey

et al. 2022

J Flood Risk Management

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“…The results of this research have been compared with the results of Bazzi et al 62 and Kassem et al 25 Table 7 shows the comparison of the obtained results.…”

Section: Forecasting the River Flowmentioning

confidence: 87%

“…22,23 There has been a lot of research on the combined models between neural networks with hydrological models, that most research has shown the successful performance of this hybrid model. 24,25 Therefore, using a hydrological model along with the neural network can provide managers with a more accurate forecast of river flow.…”

Section: Introductionmentioning

confidence: 99%

Forecasting hydropower generation byGFDL‐CM3climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search Algorithm (ISSA)

Wang

Gholinia

2021

Concurrency and Computation

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One of the most sensitive factors affecting hydropower generation is climate change.The objective of this study is to forecast the hydropower generation under the influence of climate change for the next 50 years. For this purpose, the GFDL-CM3 model is used under three scenarios: RCP2.6, RCP4.5, and RCP8.5 to predict precipitation and temperature. Any change in the inlet flow to the turbine will cause changes in the hydropower output. Therefore, the more accurately the flow is estimated, the hydropower generation is forecasted more accurately. In this research, a hydrological-neural network hybrid model for flow prediction is presented in which the Improved Sparrow Search Algorithm (ISSA) has been used. The results forecasting climate parameters showed that the average annual rainfall, runoff, and evaporation have a downward trend compared to the control period. The minimum and maximum annual temperatures have an increasing trend compared to the control period. To evaluate the hydropower generation Aras dam, the HEC-Ressim reservoir model is utilized. By comparing the average annual hydropower with the control period, it is known that the power generation will decrease in future years. The average annual electricity generated by hydropower under the scenario RCP2.6 and RCP4.5 and RCP8.

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“…For example, Y. Wu and Chen (2012) incorporated the R. Zhang (1963) equation into SWAT and conducted a comparative analysis with the original model, revealing that the revised version exhibited superior performance overall, particularly in accurately tracking peak and low sediment concentration. Kassem et al (2019) coupled SWAT and Artificial Neural Network model to obtain SWAT‐ANN to enhance the simulation performance of runoff from the Khazir River in Iran, and the results showed that the simulation results of SWAT‐ANN were all better than SWAT. In conclusion, revising the modelling framework is an efficient way to improve its performance.…”

Section: Introductionmentioning

confidence: 99%

“…Wu and Chen (2012) incorporated the R. Zhang (1963) equation into SWAT and conducted a comparative analysis with the original model, revealing that the revised version exhibited superior performance overall, particularly in accurately tracking peak and low sediment concentration. Kassem et al (2019)…”

mentioning

confidence: 99%

Developing SWAT‐S to strengthen the soil erosion forecasting performance of the SWAT model

Long,

Gao,

Shao

et al. 2023

Land Degrad Dev

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Soil erosion is an important cause of global land degradation, and accurate monitoring of it is essential. The Soil and Water Assessment Tool (SWAT), a distributed hydrological model, is an advanced technique for predicting soil erosion at watershed scale. However, as the erosion framework was established in gently sloping land, SWAT is limited in predicting soil erosion in some highland and mountainous regions. Therefore, this study suggested a method to integrate the sediment transport theoretical formula that can reflect the morphology of gully regions into SWAT to obtain SWAT‐S to enhance the calculation performance of sediment load, and the SWAT‐S was evaluated according to the coefficient of determination (R2), Nash‐Sutcliffe coefficient (NSE), Percent‐Bias (P‐BIAS) and root mean square errors (RMSE)‐observations SD ratio (RSR) in the Yanhe basin on the Chinese Loess Plateau. The results showed that SWAT‐S is more successful in reproducing the monthly sediment load, with R2, NSE, |P‐BIAS| and RSR were changed by 5.08%, 17.65%, −2.92% and −10.00% in the calibration, as well as by 1.18%, 10.39%, 45.45% and −18.75% in the validation of the SWAT‐S compared to SWAT. Meanwhile, SWAT‐S estimates 2.66 × 106 t more sediment than SWAT during the June–September flood season and better matches observed data. In total, the revised SWAT can improve the performance of sediment estimation, which is beneficial for the wider application of the model in more regions of the world.

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Predicting of daily Khazir basin flow using SWAT and hybrid SWAT-ANN models

Cited by 29 publications

References 13 publications

An artificial neural network‐hydrodynamic coupled modeling approach to assess the impacts of floods under changing climate in the East Rapti Watershed, Nepal

An artificial neural network‐hydrodynamic coupled modeling approach to assess the impacts of floods under changing climate in the East Rapti Watershed, Nepal

Forecasting hydropower generation byGFDL‐CM3climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search Algorithm (ISSA)

Developing SWAT‐S to strengthen the soil erosion forecasting performance of the SWAT model

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