پیوندهای مفید
آمار
| تعداد نشریات | 22 |
| تعداد شمارهها | 354 |
| تعداد مقالات | 3,735 |
| تعداد مشاهده مقاله | 4,939,273 |
| تعداد دریافت فایل اصل مقاله | 3,311,449 |
Estimation of the Spatial Distribution of the Groundwater Quality Using the Combined Method of Geostatistics_ Artificial Neural Networks (Case study: the Miandoab aquifer)) | ||
| مجله پژوهش های خشکسالی و تغییراقلیم | ||
| دوره 1، شماره 2، شهریور 1402، صفحه 63-76 اصل مقاله (1.75 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22077/jdcr.2023.6118.1013 | ||
| نویسندگان | ||
| Seyyed Ali Moasheri* 1؛ Forouzan Karami2؛ Bahare Baba3 | ||
| 1Ph.D. Condidate of irrigation and drainage, Campus Aboureyhan Tehran University, Tehran, Iran. | ||
| 2Master of Water Resources, Shahid Bahonar Kerman University, Kerman, Iran | ||
| 3Master of Water Resources, Shahid Bahonar Kerman University, Kerman, Iran. | ||
| چکیده | ||
| Drought crisis, although traditionally limited to central provinces, desert, and hot and dry places, but recent research has shown that in recent years, the plains of the Lake Urmia, such as the Miandoab plain, have been affected by drought, and has undergone a sharp decline in groundwater levels and subsequently reduced quality. n this research, the rate of variation in quality parameters of groundwater such as TH, TDS, EC, pH, SAR, which was collected by the regional water company of West Azarbaijan in the years 2002 and 2011, has been investigated. The statistical data of 2002 were mapped by statistical and Kriging method and stored in a regular grid of 31 * 26 in GIS software. This data is stored as a text file and used in the simulation of the artificial neural network. The results showed that the MLP model with M6 structure has a correlation coefficient of 0.92 and a mean square error of 0.562, and it can simulate groundwater quality in Mianodab Plain. Also, in predicting values of the absorption sodium ratio between 2003 and 2011, the correlation coefficient showed 0.69 satisfactory results. Finally, with sensitivity analysis, respectively, chlorine, acidity, and phosphate have the greatest effect on simulation and prediction of sodium adsorption rates. | ||
| کلیدواژهها | ||
| Prediction؛ Geostatistics؛ Artificial Neural Network؛ Sodium Absorption Ratio؛ Miandoab | ||
| مراجع | ||
|
Ahmadifar, R., Mousavi, S. M., & Rahimzadegan, M. (2017). Groundwater pollution risk zoning using GIS (Case study: Sarab Plain). Water and Soil Conservation Research Journal, 24(3), 1-20. Azari, I. (2008). Estimation of the amount of gas consumed in Tehran using neural network technology. Journal of Water and Sewage, Engineering Quarterly, 42(8), 961-968. Azimi, S., Azhdary Moghaddam, M., & Hashemi Monfared, S. A. (2019). Prediction of annual drinking water quality reduction based on Groundwater Resource Index using the artifcial neural network and fuzzy clustering. Journal of Contaminant Hydrology, 220, 6-17. Basirani, N., Moasheri, S. A., & Narouee, J. (2016). Estimation of the spatial distribution of groundwater quality of Torbat Jam-Fariman plain using a statistical compilation of geostatistics, artifcial neural network, frst international conference on water, environment and sustainable development, Civil Engineering Department, Faculty of Engineering, University Ardebil scholar. Heidarzadeh, N. (2017). A practical low-cost model for prediction of the groundwater quality using artifcial neural networks. Journal of Water Supply; Research and Technology-AQUA. Khataar, M., Mosaddeghi, M. R., Amiri Chayjan, R., & Mahboubi, A. A. (2018). Prediction of water quality effect on saturated hydraulic conductivity of soil by artifcial neural networks. The International Society of Paddy and Water Environment Engineering and Springer Japan KK. Moasheri, S. A., Tabatabai, S. M., Sarani, N., & Alai, Y. (2012). Estimation Spatial distribution of Sodium adsorption ratio. SAR. In Groundwater’s using ANN and Geostatistics Methods, the case of Birjand Plain, Iran. Paper presented at the ISEMPSR Centre Conferences, Bangkok. Moasheri, S. A., Gholam Ali Zadeh Ahangar, A., & Shahnavazi, A. (2017). The effect of soil characteristics on prediction of cation exchange capacity (CEC) using artifcial neural networks in the section of the Durban in Khash city. First National Conference on New Opportunities, University of Birjand. Nouri, R., Ashraf, Kh., & Azhdarpour, A. (2008). Comparison of Artifcial Neural Networks and Multivariate Linear Regression Based on Main Components of Carbon Monoxide Daily PreConcentration: A Case Study of Tehran. Journal of Physics of Earth and Space, 34(1), 135-152. Salehi, H., & Zaini Vand, H. (2014). Evaluation of Groundwater Quality for Drinking, Agriculture and Selection of the Most Suitable Localization Method (Case Study: West of Marivan County). Ecology, 1(3), 166-153. Tiri, A., Belkhiri, L., & Mouni, L. (2018). Evaluation of surface water quality for drinking purposes using fuzzy inference system. Groundwater for Sustainable Development, 6, 235-244. Wackernagel, H. (1992). Multivariate geostatistics: an introduction with applications. Springer, Berlin, Germany. Yazdani, M. R., & Koh Banani, H. R. (2016). Evaluation of groundwater quality indices of Mashhad Plain using geostatistical and GIS techniques. Journal of Neyshabur School of Medical Sciences, 5(3), 63-73. Zabihi, M. R., Kamali, Gh. R., & Rahimian, M. (2015). Modeling and Damaging Water Resources of Garmsar Plain (Master’s Degree). Shahid Bahonar the University of Kerman, Faculty of Engineering and Engineering, Department of Mining Engineering. Zare Abyaneh, H. (2013). Development and Application of Neural, Fuzzy, Genetic Algorithms and Geomagnetic Models for Estimating the spatial distribution of station surfaces, 20(4), 1-25. | ||
|
آمار تعداد مشاهده مقاله: 528 تعداد دریافت فایل اصل مقاله: 528 |
||
نماد الکترونیک
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب