نوع مقاله : پژوهشی
نویسندگان
1 استاد گروه جغرافیای طبیعی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران.
2 فارغ تحصیل کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، دانشگاه محقق اردبیلی
چکیده
امروزه به‌دلیل افزایش جمعیت، توسعه صنعتی، بهره‌برداری بی‌رویه، خشکسالی‌ها بهره‌برداری از آب‌های زیرزمینی چندین برابرشده است. بنابراین تشخیص مناطق دارای آب زیرزمینی به‌عنوان یکی از منابع مهم برای تأمین آب آشامیدنی، کشاورزی، صنایع مختلف به‌خصوص از موارد مهم و ضروری در مدیریت منابع آب محسوب می‌شود. هدف از انجام این پژوهش، بررسی و پهنه‌بندی مناطق دارای آب زیرزمینی در دشت خرم‌آباد واقع در استان لرستان با استفاده از روش شبکه عصبی کانولوشن است. بدین منظور ابتدا از طریق بازدیدهای میدانی، نقشه‌های زمین‌شناسی و توپوگرافی و با مرور منابع قبلی و بررسی شرایط منطقه، نه عامل طبقات ارتفاعی، شیب، جهت شیب، فاصله از گسل، فاصله از رودخانه، بارش، لیتولوژی و کاربری‌اراضی، خاک به‌عنوان عوامل مؤثر بررسی و انتخاب شدند و نقشه آن‌ها در محیط ArcGisتهیه شدند. درروش کانولوشن تعداد نمونه‌ها به‌عنوان نسبت بین مجموعه آموزشی و مجموعه آزمایشی70:30 تعیین شد و چارچوب شبکه عصبی کانولوشن به‌عنوان 2 لایه کانولوشن و 2 لایه ادغام، 2 اتصال کامل استفاده شد. لایه‌ها و در نهایت لایه sigmoid برای در طبقه‌بندی از هسته کانولوشن 3 3، تابع Relu به‌عنوان تابع فعال‌سازی و تابع آنتروپی متقاطع به‌عنوان تابع زیان استفاده شد. نقشه‌های به‌دست آمده در 5 کلاس طبقه‌بندی شد. هم‌چنین برای اعتبارسنجی نتایج مدل از ماتریس کانفیوزن استفاده شد.30 درصد از داده‌های واقعی برای ارزیابی استفاده شد که منجر به‌دقت کلی92 درصد شد، یعنی مدل توانسته 92درصد داده‌ها را آب زیرزمینی و93درصد عدم آب زیرزمینی رو به‌درستی تشخیص دهد. تجزیه و تحلیل نقشه پتانسیل آب زیرزمینی مدل شبکه عصبی کانولوشن نشان می‌دهد که حدود 57 درصد منطقه در شرایط کم آب زیرزمینی و43درصد منطقه در شرایط خوب آب زیرزمینی قرار دارد.
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
Prediction of underground water potential in Khorramabad plain based on convolutional neural networks
نویسندگان [English]
- sayyad Asghari Saraskanrood 1
- Maryam Riahinia 2
1 Professor, Department of Physical Geography, Faculty of Social Science, University of Mohaghegh Ardabili, Ardabil, Iran
2 Master's degree in remote sensing and geographic information system, Mohaghegh Ardabili University, Iran
چکیده [English]
Today, due to population increase, industrial development, excessive exploitation, droughts, exploitation of underground water has multiplied. Therefore, identifying areas with underground water as one of the important sources for providing drinking water, agriculture, and various industries is considered to be one of the important and necessary issues in water resources management. The purpose of this research is to investigate and zonate the areas with underground water in Khorram Abad plain located in Lorestan province using convolutional neural network method. For this purpose, maps of nine factors affecting underground water were first prepared in the ArcGist environment. In the convolution method, the number of samples was determined as the ratio between the training set and the test set was 70:30, and the convolution neural network framework was used as 2 convolution layers and 2 integration layers, 2 complete connections. layers and finally the sigmoid layer was used for classification from the 3-3 convolution kernel, the Relu function as the activation function and the cross entropy function as the loss function. The obtained maps were classified into 5 classes: very good, good, average, low and very low. Confusion matrix was also used to validate the results of the model. 30% of the real data was used for evaluation, which resulted in an overall accuracy of 92%, that is, the model was able to correctly identify 92% of the data as underground water and 93% as the absence of underground water. The analysis of the groundwater potential map of the convolutional neural network model shows that about 57% of the area is in low groundwater conditions and 43% of the area is in good groundwater conditions.
کلیدواژه‌ها [English]
- Zoning
- Neural Network
- Underground Water
- Khorramabad Plain
Aliyari, H. (1376). Geomorphology of Khorramabad river basin (north of Khorramabad) with emphasis on the problem of erosion. Master's thesis of the Department of Geography, Faculty of Earth Sciences.
Alizadeh, A. (1389), Principles of applied hydrology, 31st edition, Imam Reza University Press, Mashhad.
Alshehhi.R., Reddy Marpu. P., Woona.W.L., Dalla Mura. M.(2017). Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks.ISPRS Journal of Photogrammetry and Remote Sensing 130 (2017) 139–149.
Amyar.A ., Modzelewski.R., Li.H., Ruan.S.(2020). Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Computers in Biology and Medicine 126, 104037.1-10.
Asghari, f; Belvasi, I.(2017). Comparison of artificial neural network model with fuzzy logic model in landslide risk assessment (case study of Simre Chenar watershed), Quantitative Geomorphology Research, 7th year, No. 2, pp. 158-182.
Bouaafia.S., Messaoud.S., Maraoui.A., Ammari.A.,C. Khriji.L., Machhout.M. (2021). Deep Pre-trained Models for Computer Vision Applications: Traffic sign recognition. 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD'21).23_28.
Biranvand, N., Kikhaei, M., Mamani, R. (2021). Presenting a method in the field of revealing targets in satellite images using deep learning and with remote sensing and GIS approach. Geographical Quarterly of the Land, Scientific-Research, Year 18, Number 69, Spring 140. pp. 44-60
Chen, W., Zhang, S., Li, R., Shahabi, H. (2018d). Performance evaluation of the GIS-based data mining techniques of bestfirst decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. Science of The Total Environment 644, 1006–1018
Deng, F. Deng, Z. Dan, L. Daqing, W. Duan, H. Xing, Z. (2016). Application of remote sensing and GIS analysis in groundwater potential estimation in west Liaoning Province, China. Journal of Engg Research ,4:(3), pp 1-17.
Fallah, F., M. Daneshfar, S. Ghorbani Nejad (2016). Finding the potential of underground water resources in the study area of Khorram Abad using statistical index method. Journal of Water and Sustainable Development, 4(1): 89-98.
- Qi, H. Wang, M. Haner, C. Weng, S. Chen, Z. Zhu. (2019). Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation, CAAI Trans. Intell. Technol. 4, 80–91
Haji Azizi, M; Rahmani, M; Biglari, N (2013). finite element analysis of underground dams and important points in their design and implementation, case study: Abkhori underground dam in Semnan province, Journal of Modeling in Engineering, Year 12, No. 38, pp. 153-165.
Jingxin.L., Mengchao.Z., Yuchen.L., Jinglei.C., Yutong.Z Zhong. Z.,Lihui.Z.(2021).COVID-19 lesion detection and segmentation–A deep learning method.Contents lists available at ScienceDirect Methods.1-8.
Kazemi, M. Zamani Shorabi, A. (2016). Brain lesion tumor segmentation using convolutional neural network, Seyed Masoumi Forough | Kazemi Manouchehr Once Shurabi Abbas. National conference of 1420 vision and technological developments of electricity, computer and information technology, holding period: 1
Lee, Saro. Song, K. Kim, Y. Park, I.(2012). Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model, Hydrogeology Journal, 8(20), pp1511–1527
Manap, M. Nampak, H. Pradhan, B. Lee, S. Sulaiman, W. Ramli, M. (2012). Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arabian Journal of Geosciences, 7(2), pp 711-724.
Mazzia, V., Khaliq, A., & Chiaberge, M.(2020). Improvement in land cover and crop classification based on temporal features learning from Sentinel-2 data using recurrent-convolutional neural network (R-CNN). Applied Sciences, 10(1), 238
Nohani, A., A. Marufini, Kh. Khosravi.(2017). Evaluation of the abundance ratio method in determining the potential of underground water resources in Dasht al-Shatar, Lorestan province. Iran Irrigation and Drainage Journal, 1(12): 119-129.
Sadaqat, M.(2017). Land and Water Resources, Payam Noor Publications
Salla. R., Wilhelmiinab.H., Saric.K., Mikaelad.M., Pekkaa.M., Jaakko.M. (2018).Evaluation of the confusion matrix method in the validation of an automated system for measuringfeeding behaviour of cattle.Behavioural Processes.1-19.
Seyed Masoumi, F., Kazemi, M., Zamani Shorabi, A.(2018). Brain lesion tumor segmentation using convolutional neural network, 1420 Vision National Conference and Technological Advances in Electrical Engineering, Computer, Information Technology, pp. 1-16
Suganthi, S. Elango, L. Subramanian, S K. (2013). Ground- water potential zonation by Remote Sensing and GIS techniques and its relation to the Groundwater level in the part of Coastal the Arani and Koratalai River Basin. Southern India, Earth Sci. Res. SJ, 17(2), pp 87 – 95.
Tahmasian, S., Z. Khani Temlia, c. Bahmanesh.( 2016). investigating the quality of underground water in Khorram Abad Plain. Journal of Environmental and Water Engineering, 3(4): 641-352.
Talebi, L. (2014). Investigating flood risks in Khorramabad city. Master's thesis on urban hydrology and meteorology.
Xu. H.,Yang. X., Wang.D., Hu.Y., Shi. Y., Cheng.Z., Liu.Z., Lu.Z., Shi.L., Liang.Z., Zhu.D.2022. Predicting groundwater potential assessment in water-deficient islands based on convolutional neural networks. The Egyptian Journal of Remote Sensing and Space Sciences 25 (2022) 1013–1023.
Yamani, M; Alizadeh, Sh. (2013. potential exploration of underground water resources using AHP model (case study: Abadeh-Euclid Fars basin), Hydrogeology Quarterly, year 1, number 1, pp. 131-144.
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