Related Links
An evaluation of the impact of exponential downscale input parameters with artificial intelligence method for estimation of hydrological parameters, case study: Ardabil Synoptic Station | ||
| مهندسی و مدیریت آبخیز | ||
| Article 7, Volume 15, Issue 3, September 2023, Pages 438-451 PDF (509.06 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.22092/ijwmse.2022.358022.1963 | ||
| Authors | ||
| Negar Einnollahzadeh1; Atabak Feizi* 2; Farnaz Daneshvar vousoughi3 | ||
| 1Msc in Civil Engineering, Water Resource Management and Engineering, Department of Civil Engineering, University of Mohaghegh Ardabili, Ardabil, Iran | ||
| 2Associate Professor, Department of Civil Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran | ||
| 3Assistant Professor, Department of Civil Engineering, Islamic Azad University, Ardabil Branch, Ardabil, Iran | ||
| Abstract | ||
| Introduction In recent years, factors such as the growth of industrial activities and environmental destruction have led to an increase in greenhouse gases, resulting in disruption of the climate balance known as climate change. The negative impact of this phenomenon on various systems, such as water resources, agriculture and industry, has raised concerns in human society. Consequently, addressing the issue of climate change regarding water resources has become one of the primary causes of concern today. Climate change and its effects pose significant challenges to water and energy resource management, necessitating thorough investigation and developing plans to mitigate its impact on water resources. This study aims to identify the region's most suitable climate change model and assess the effectiveness of artificial intelligence methods in studying the climate change phenomenon. Materials and methods One of the most reliable approaches for studying the parameters influencing hydrological phenomena under climate change is atmospheric general circulation models. To employ these models on a regional scale, downscaling operations are necessary. Given the large number of parameters derived from Earth's General Circulation Models (GCMs), selecting the most influential parameters is essential before proceeding with the exponential downscaling process. In this study, the meteorological and hydrological parameters of the Ardabil synoptic station were determined using 25 models from the fifth series of the IPCC report. The linear correlation coefficient between monthly precipitation and observed temperature with the output of GCM was used to identify the most appropriate model among the reviewed models. Artificial Neural Network (ANN) was also utilized to downscale the GCMs output. Before employing the neural network, the linear correlation coefficient, the standard information function, and the M5 decision tree were used to identify the most suitable input parameters from the parameters of the best GCMs in the region, to obtain an ideal and optimal network. Results and discussion This research investigated 25 models from the fifth series of the IPCC report to explore the uncertainty of GCMs. The results indicated that three models-MRI-CGCM3, CMCC-CMS, and MPI-ESMMR-demonstrated the most suitable correlation coefficients at the Ardabil synoptic station. The findings related to determining the most appropriate input parameters for exponential downscaling, using three methods: linear correlation coefficient, standard information function, and M5 decision tree, revealed that the decision tree algorithm provided the most suitable parameters. Moreover, the results obtained from the downscale analysis using the neural network with the variables selected by the decision tree method exhibited the excellent performance of this approach in selecting the effective input parameters of the neural network. Specifically, using the selected parameters of the MRI-CGCM3 model as input for the neural network as a downscaling method yielded better outcomes. The results obtained using the selected parameters of the MRI-CGCM3 model indicated that for the precipitation parameter, the values of the Determination Coefficient (DC), Root Mean Square Error (RMSE), and Correlation Coefficient (CC) for the test data were 0.39, 0.04, and 0.63, respectively. For the temperature parameter, the values of DC, RMSE, and CC for the test data of the superior model were 0.9, 0.03, and 0.95, respectively. Conclusion The performance of exponential downscaling networks is determined by the climatic conditions of the region. The superiority of a particular model in one study cannot be regarded as a valid argument for selecting that model for all regions. It is advisable to utilize different models of the general earth circulation within the region to identify an optimal model. Conducting such studies can assist researchers in investigating various hydrological phenomena that may occur in the future, which may have irreparable consequences. | ||
| Keywords | ||
| Artificial neural network; Correlation coefficient; Decision tree; GCM models; Mutual information | ||
| References | ||
|
Ahmadi Baseri, N., Shirvani, A., Nazemosadat, M.J., 2014. The application of ANN for downscaling GCMs outputs for prediction of precipitation in across Southern Iran. J. Soil Water Conserv. 28(5), 1037-1047 (in Persian). Alberg, D., Last, M., Kindle, A., 2012. Knowledge discovery in data streams with regression tree methods. WIREs Data Mining Knowl. Discov. (2), 69-78. Alizadeh, M., Gorbani, M., Darbandi, S., 2020. The effect of climate change on the severity and duration of meteorological drought under the LARS-WG model, case study: Ardabil Synoptic Station. Proceedings of 9th National Conference on Rainwater Catchment Systems, University of Tabriz, Tabriz, Iran. Almazroui, M., Saeed, F., Saeed, S., Islam, M.N., Ismail, M., Klutse, N.A.B., Siddiqui, M.H., 2020. Projected change in temperature and precipitation over Africa from CMIP6. Earth Syst. Environ. 4(3), 455-475. Alvisi, S., Mascellani, G., Franchini, M., Bardossy, A., 2006. Water level forecasting through fuzzy logic and artificial neural network approaches. Hydrol. Earth Syst. Sci. 10, 1-17. Amirabadizadeh, M., Nazeri Tahroudi, M., Zeynali, M.J., 2018. Evaluation of the accuracy of artificial intelligence and regression models for the simulation of daily temperature. J. Meteoro. Atmos. Sci. 1(1), 65-76 (in Persian). Asakereh, H., Hesami, N., 2019. Assessing the application of artificial neural networks and SDSM models to simulate the minimum and maximum temperatures at Isfahan station. J. Geophys. Res. Desert Areas 7(2), 133-158 (in Persian). Dehghani, R., Younesi, H., Torabi Podeh, H., 2017. Comparing the performance of support vector machine, gene expression programming and Bayesian networks in predicting river flow, case study: Kashkan River. Water Soil Conserv. 24(4), 161-177 (in Persian). ESGF, 2020. Earth system grid federation. https://esgf-node.llnl.gov/search/cmip5/ (accessed 25 April 2020). Feizi, A., Aghajani Jomayran, R., 2021. Allocation and management of water resources in the Yamchi Dam Basin with scenario analysis approach using WEAP model. J. Environ. Sci. Technol. 23(9), 89-107 (in Persian). Goodarzi, M., Choobeh, S., 2019. Assessment of downscaling methods in predicting climatic parameters under climate change status: a case study in Ardabil Synoptic Station. Iran-Watershed Manag. Sci. Engin. 13(45), 63-69 (in Persian). Gudmundsson, L., Boulange, J.DO.X., Gosling, S.N., Grillakis, M.G., Koutroulis, A.G., Zhao, F., 2021. Globally observed trends in mean and extreme river flow attributed to climate change. Science 371(6534), 1159-1162. Hamidi, O., Poorolajal, J., Sadeghifar, M., Abbasi, H., Maryanaji, Z., Faridi, H., Tapak, L., 2015. A comparative study of support vector machines and artificial neural networks for predicting precipitation in Iran. Theor. Appl. Climatol. 119(3), 723–731. IPCC, 2014. Summary for policymakers, In: Climate Change 2014: Impacts, Adaptation, and Vulnerability, Part A: Global and Sectoral Aspects. Cambridge University Press, Cambridge, United Kingdom and New York. Javaherian, M., Ebrahimi, H., Aminnejad, B., 2021. Prediction of changes in climatic parameters using CanESM2 model based on RCP scenarios, case study: Lar Dam Basin. Ain Shams Eng. J. 12(1), 445-454. Khezri, F., Irandoust, M., Jalalkamali, N., Yazdanpanah, N., 2022. Modeling and bivariate analysis of meteorological drought using data generation with climate change approach, case study: Lake Urmia. J. Soil Water Conserv. 11(2), 49-68 (in Persian). Kia, E., Karimi, V., 2021. Investigation of temperature and rainfall parameters of Haraz River Basin affected by climate change. J. Nat. Enviro. Hazards 26(9), 145-160 (in Persian). Miao, C.Y., Duan, Q.Y., Sun Q.H., Li, G.D., 2013. Evaluation and application of Bayesian multi-model estimation in temperature simulations. Prog. Phys. Geogr. 37(6), 727-744. Mora, D.E., Campozano, L., Cisneros, F., Wyseure, G., Willems, P., 2014. Climate changes of hydrometeorological and hydrological extremes in the Paute Basin, Ecuadorean Andes. Hydrol. Earth Syst. Sci. (18), 631–648. Nourani, V., Komasi, M., Mano, A., 2009. A multivariate ANN wavelet approach for rainfall–runoff modeling. Water Resour. Manag. 23(14), 2877-2894. Nourani, V., Razzaghzadeh, Z., Baghanam, A.H., Molajou, A., 2019. ANN-based statistical downscaling of climatic parameters using decision tree predictor screening method. Theor. Appl. Climatol. 137(3), 1729-1746. Omidvar, E., Rezaei, M., Pirnia, A., 2018. Performance evaluation of artificial neural network models for downscaling and predicting of climate variables. J. Watershed Manag. Res. 9(18), 80-90 (in Persian). Pattnayak, K.C., Kar, S.C., Dalal, M., Pattnayak, R.K., 2017. Projections of annual rainfall and surface temperature from CMIP5 models over the BIMSTEC countries. Glob. Planet. Change 152, 152-166. Rajaee, T., Nourani, V., Zounemat-Kermani, M., Kisi, O., 2011. River suspended sediment load prediction: application of ANN and wavelet conjunction model. J. Hydrol. Eng. 16(8), 613-627. Razzaghzadeh, Z., Nourani, V., Hosseini baghanam, A., 2020. Application of mutual information feature extraction methods in statistical downscaling for investigation of climate change effects on Tabriz precipitation. J. Environ. Sci. Technol, in Press (in Persian). Rezaee, M., Nahtaj, M., Moghadamniya, A., Abkar, A., Rezaee, M., 2015. Comparison of artificial neural network and SDSM methods in the downscaling of annual rainfall in the HadCM3 modelling, case study: Kerman, Ravar and Rabor. Water Resour. Engin. J. 8(24), 25-40 (in Persian). Sabziparvar, A., F. Khoshhal Jahromi. 2018. Comparison of multi-layer perceptron artificial neural network and Linacre regression model performance for predicting daily minimum temperature (case study: Kerman, Shiraz, Rasht and Hamedan). Iranian Journal of Geophysics, 12(3):121-107, (in Persian) Yang, H. H., S.V. Vuuren, S. Sharma and H. Hermansky. 2000. Relevance of time-frequency features for phonetic and speaker-channel classification. Speech Communication, 31(1): 35-50. | ||
|
Statistics Article View: 728 PDF Download: 354 |
||
Journal management system. designed by sinaweb