Yapay sinir ağı ile deprem şiddeti tahmini: Farklı ağ tasarımlarının ve eğitim algoritmalarının incelenmesi

Yıl 2022, , 2133 - 2146, 28.02.2022

Aslı Sebatlı Sağlam , Fatih Çavdur

https://doi.org/10.17341/gazimmfd.791337

Cited By: 2

Öz

Bu çalışmada, ileri beslemeli geri yayılımlı bir yapay sinir ağı ile depremin büyüklüğü, derinliği ve afetzedelerin merkez üssüne olan uzaklıklarına bağlı olarak deprem şiddeti tahmini yapılmıştır. Bu kapsamda, Amerika Birleşik Devletleri Jeoloji Araştırmaları Kurumu’nun veri tabanında yer alan ve önemli depremler olarak adlandırılan depremlere ilişkin bilgiler yapay sinir ağının girdisi olarak kullanılmıştır. Farklı yapay sinir ağı tasarımları için deprem şiddeti tahmin edilerek uygun bir ağ tasarımı elde edilmiştir. Ardından söz konusu uygun ağ tasarımı için farklı eğitim algoritmaları kullanılarak ağ eğitilmiş ve bu algoritmalar arasından en uygun eğitim yöntemi belirlenmiştir. Farklı ağ tasarımlarının ve eğitim algoritmalarının performansları, ortalama karesel hata ve korelasyon katsayısı cinsinden analiz edilmiştir. Performans parametrelerinin ortalaması açısından, iki gizli katman ve her bir katmanda sırasıyla beş ve on gizli nöronun bulunduğu ağ yapısı en uygun tasarım olarak belirlenmiştir. Söz konusu ağ yapısı için Bayes Düzenlemesi ile Levenberg-Marquardt eğitim algoritmasının kullanıldığı durumda performans parametreleri açısından en iyi sonuçlar gözlenmiştir.

Anahtar Kelimeler

Afet operasyonları yönetimi, Afet yardım operasyonları, Makine öğrenmesi, Yapay sinir ağları, Deprem şiddeti tahmini

Destekleyen Kurum

TÜBİTAK

Proje Numarası

115M020

Teşekkür

Bu çalışma, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından 115M020 numaralı proje kapsamında desteklenmiştir. Desteklerinden dolayı TÜBİTAK’a teşekkürlerimizi sunarız.

Earthquake intensity estimation via an artificial neural network: Examination of different network designs and training algorithms

Öz

In this study, using a multi-layer feed-forward artificial neural network, we estimate earthquake intensity based on the magnitude and the depth of an earthquake and the distance of the disaster victims from the epicenter of the earthquake. In this context, we use significant earthquakes database of the United States Geological Survey as the inputs of the artificial neural network. We first determine an appropriate network design by estimating earthquake intensity with different artificial neural network designs and then the best training algorithm for the appropriate network design by evaluating different algorithms for the corresponding network design. These analyses are performed in terms of the mean square error and correlation coefficient. In terms of the average performance parameters, the network structure with two hidden layers and five and ten hidden neurons in each respective layer is determined as the most appropriate design. We observe the best results in terms of performance parameters by using the Levenberg-Marquardt training algorithm with Bayesian Regularization for the corresponding network structure.

Anahtar Kelimeler

Disaster operations management, Disaster relief operations, Machine learning, Artificial neural networks, Earthquake intensity estimation

Proje Numarası

115M020

Kaynakça

• Van Wassenhove, L.N., Humanitarian aid logistics: supply chain management in high gear, Journal of the Operational Research Society, 57(5), 475-489, 2006.
• Erdik, M., Earthquake vulnerability of buildings and a mitigation strategy: Case of Istanbul, Washington DC: World Bank, 79-92, 2003.
• Kumar, A., Latif, Y.L., Daver, F., Developing forecasting tool for humanitarian relief organizations in emergency logistics planning, International Journal of Economics and Management Engineering, 6(11), 3194-3200, 2012.
• European Commission. Action plan on the Sendai framework for disaster risk reduction 2015–2030, European Union. http://ec.europa.eu. Yayın tarihi Haziran 17, 2016. Erişim tarihi Mayıs 13, 2020.
• Xu, X., Qi, Y., Hua, Z., Forecasting demand of commodities after natural disasters, Expert systems with applications, 37(6), 4313-4317, 2010.
• Sheu, J. B., Challenges of emergency logistics management, Transportation Research Part E: Logistics and Transportation Review, 43(6), 655-659, 2007.
• United States Geological Survey. Earthquake Hazards Program, Significant Earthquakes Archive. https://earthquake.usgs.gov/earthquakes/browse/significant.php. Yayın tarihi 2011. Erişim tarihi Mart 8, 2017.
• Reyes, J., Morales-Esteban, A., Martínez-Álvarez, F., Neural networks to predict earthquakes in Chile, Applied Soft Computing, 13(2), 1314-1328, 2013.
• Corbi, F., Sandri, L., Bedford, J., Funiciello, F., Brizzi, S., Rosenau, M., Lallemand, S, Machine learning can predict the timing and size of analog earthquakes, Geophysical Research Letters, 46(3), 1303-1311, 2019.
• Sankaranarayanan, S., Prabhakar, M., Satish, S., Jain, P., Ramprasad, A., Krishnan, A., Flood prediction based on weather parameters using deep learning, Journal of Water and Climate Change, 11(4), 1766-1783, 2020.
• Kuradusenge, M., Kumaran, S., Zennaro, M, Rainfall-induced landslide prediction using machine learning models: The case of Ngororero District, Rwanda, International journal of environmental research and public health, 17(11), 4147, 2020.
• Battarra, M., Balcik, B., Xu, H., Disaster preparedness using risk-assessment methods from earthquake engineering, European Journal of Operational Research, 269(2), 423-435, 2018.
• Adeli, H., Panakkat, A., A probabilistic neural network for earthquake magnitude prediction, Neural networks, 22(7), 1018-1024, 2009.
• Külahcı, F., İnceöz, M., Doğru, M., Aksoy, E., Baykara, O., Artificial neural network model for earthquake prediction with radon monitoring, Applied Radiation and Isotopes, 67(1), 212-219, 2009.
• Alarifi, A.S., Alarifi, N.S., Al-Humidan, S., Earthquakes magnitude predication using artificial neural network in northern Red Sea area, Journal of King Saud University-Science, 24(4), 301-313, 2012.
• Amit, Z., Arjun, S., Quantification of recent seismicity and a back propagation Neural Network for forecasting of earthquake magnitude in Northeast Region of India, Disaster Advances, 10(6). 17-34, 2017.
• Mousavi, S.M., Beroza, G.C, A machine‐learning approach for earthquake magnitude estimation, Geophysical Research Letters, 47(1), e2019GL085976, 2020.
• Panakkat, A., Adeli, H. Neural network models for earthquake magnitude prediction using multiple seismicity indicators, International journal of neural systems, 17(01), 13-33, 2007.
• Asim, K.M., Martínez-Álvarez, F., Basit, A., Iqbal, T., Earthquake magnitude prediction in Hindukush region using machine learning techniques, Natural Hazards, 85(1), 471-486, 2017.
• Moustra, M., Avraamides, M., Christodoulou, C., Artificial neural networks for earthquake prediction using time series magnitude data or seismic electric signals, Expert systems with applications, 38(12), 15032-15039, 2011.
• Juang, C. H., Elton, D. J., Fuzzy logic for estimation of earthquake intensity based on building damage records, Civil Engineering Systems, 3(4), 187-191, 1986.
• Kubo, H., Kunugi, T., Suzuki, W., Suzuki, S., Aoi, S., Hybrid predictor for ground-motion intensity with machine learning and conventional ground motion prediction equation, Scientific reports, 10(1), 1-12, 2020.
• Bradley, B.A., Site-specific and spatially-distributed ground-motion intensity estimation in the 2010–2011 Canterbury earthquakes, Soil Dynamics and Earthquake Engineering, 61, 83-91, 2014.
• Jozinovic, D., Lomax, A., Stajduhar, I., Michelini, A., Rapid prediction of earthquake ground shaking intensity using raw waveform data and a convolutional neural network, Geophysical Journal International, 222(2), 1379-1389, 2020.
• Nicolis, O., Plaza, F., Salas, R., Prediction of intensity and location of seismic events using deep learning, Spatial Statistics, 42, 100442, 2021.
• Asif, A., Dawood, M., Jan, B., Khurshid, J., DeMaria, M., PHURIE: hurricane intensity estimation from infrared satellite imagery using machine learning, Neural Computing and Applications, 32(9), 4821-4834, 2020.
• Burks, L., Miller, M., Zadeh, R., Rapid estimate of ground shaking intensity by combining simple earthquake characteristics with tweets, 10th US National conference on earthquake engineering, Anchorage, Alaska, USA, 21-25 July, 2014.
• Kropivnitskaya, Y., Tiampo, K.F., Qin, J., Bauer, M.A., Real-Time Earthquake Intensity Estimation Using Streaming Data Analysis of Social and Physical Sensors, Pure and Applied Geophysics, 174(6), 2331-2349, 2017.
• Zahera, H.M., Sherif, M.A., Ngonga Ngomo, A.C., Jointly learning from social media and environmental data for typhoon intensity prediction, 10th International Conference on Knowledge Capture, Marina Del Rey, CA, USA, 231-234, 19-21 November, 2019.
• Günaydın, K., Günaydın, A., Peak ground acceleration prediction by artificial neural networks for northwestern Turkey, Mathematical Problems in Engineering, 2008, 1-20, 2008.
• Wang, Z., Zentner, I., Pedroni, N., Zio, E., Adaptive artificial neural networks for seismic fragility analysis, 2nd International Conference on System Reliability and Safety (ICSRS), Milan, Italy, 414-420, 20-22 December, 2017.
• Asim, K.M., Moustafa, S.S., Niaz, I.A., Elawadi, E.A., Iqbal, T., Martínez-Álvarez, F, Seismicity analysis and machine learning models for short-term low magnitude seismic activity predictions in Cyprus, Soil Dynamics and Earthquake Engineering, 130, 105932, 2020.
• Erdik, M., Şeşetyan, K., Demircioğlu, M. B., Hancılar, U., Zülfikar, C., Rapid earthquake loss assessment after damaging earthquakes, Soil Dynamics and Earthquake Engineering, 31(2), 247-266, 2011.
• Sebatli, A., Cavdur, F., Analysis of relief supplies distribution operations via simulation, Journal of the Faculty of Engineering and Architecture of Gazi University, 34(4), 2079-2096, 2019.
• Samardjieva, E., Badal, J., Estimation of the expected number of casualties caused by strong earthquakes, Bulletin of the Seismological Society of America, 92(6), 2310-2322, 2002.
• Aghamohammadi, H., Mesgari, M.S., Mansourian, A., Molaei, D., Seismic human loss estimation for an earthquake disaster using neural network, International Journal of Environmental Science and Technology, 10(5), 931-939, 2013.
• Gul, M., Guneri, A. F., An artificial neural network-based earthquake casualty estimation model for Istanbul city, Natural hazards, 84(3), 2163-2178, 2016.
• Amirifar, L., Shafiee, H., Estimating of Loss Human Life Caused Through Earthquake Employing Neural Network, Journal of Advances in Computer Research, 9(2), 71-89, 2018.
• Xing, H., Junyi, S., Jin, H., The casualty prediction of earthquake disaster based on Extreme Learning Machine method, Natural Hazards, 102(3), 873-886, 2020.
• Cui, S., Yin, Y., Wang, D., Li, Z., Wang, Y., A stacking-based ensemble learning method for earthquake casualty prediction, Applied Soft Computing, 101, 107038, 2021.
• Ganguly, K.K., Nahar, N., Hossain, B.M., A machine learning-based prediction and analysis of flood affected households: A case study of floods in Bangladesh, International journal of disaster risk reduction, 34, 283-294, 2019.
• Hashemi, M., Alesheikh, A.A., A GIS-based earthquake damage assessment and settlement methodology, Soil dynamics and earthquake engineering, 31(11), 1607-1617, 2011.
• So, E., Spence, R., Estimating shaking-induced casualties and building damage for global earthquake events: a proposed modelling approach, Bulletin of Earthquake Engineering, 11(1), 347-363, 2013.
• Musson, R. M. W., Intensity-based seismic risk assessment, Soil Dynamics and Earthquake Engineering, 20(5-8), 353-360, 2000.
• Molas, G. L., Yamazaki, F., Neural networks for quick earthquake damage estimation, Earthquake engineering & structural dynamics, 24(4), 505-516, 1995.
• Barbosa, A. R., Ribeiro, F. L., Neves, L. A., Influence of earthquake ground‐motion duration on damage estimation: application to steel moment resisting frames, Earthquake Engineering & Structural Dynamics, 46(1), 27-49, 2017.
• Chaurasia, K., Kanse, S., Yewale, A., Singh, V.K., Sharma, B., Dattu, B.R., Predicting Damage to Buildings Caused by Earthquakes Using Machine Learning Techniques, 2019 IEEE 9th International Conference on Advanced Computing (IACC), Tiruchirappalli, India, 81-86, 13-14 December, 2019.
• Mangalathu, S., Sun, H., Nweke, C.C., Yi, Z., Burton, H.V., Classifying earthquake damage to buildings using machine learning, Earthquake Spectra, 36(1), 183-208, 2020.
• Cavallo, E., Powell, A., Becerra, O., Estimating the direct economic damages of the earthquake in Haiti, The Economic Journal, 120(546), F298-F312, 2010.
• Kim, J.M., Bae, J., Son, S., Son, K., Yum, S.G., Development of Model to Predict Natural Disaster-Induced Financial Losses for Construction Projects Using Deep Learning Techniques, Sustainability, 13(9), 5304, 2021.
• Bi, C., Fu, B., Chen, J., Zhao, Y., Yang, L., Duan, Y., Shi, Y., Machine learning based fast multi-layer liquefaction disaster assessment, World Wide Web, 22(5), 1935-1950, 2019.
• Xu, Y., Lu, X., Tian, Y., Huang, Y., Real-time seismic damage prediction and comparison of various ground motion intensity measures based on machine learning, Journal of Earthquake Engineering, 1-21, 2020.
• Richter, C. F., An instrumental earthquake magnitude scale, Bulletin of the Seismological Society of America, 25 (1), 1-32, 1935.
• Wood, H. O., Neumann, F. Modified Mercalli intensity scale of 1931, Bulletin of the Seismological Society of America, 21(4), 277-283, 1931.
• United States Geological Survey. The Modified Mercalli Intensity Scale. https://www.usgs.gov/natural-hazards/earthquake-hazards/science/modified-mercalli-intensity-scale. Yayın tarihi 1989. Erişim tarihi Nisan 11, 2017.
• MathWorks, Help Center, Deep Learning Toolbox, 2020, https://www.mathworks.com/help/deeplearning. Yayın tarihi 2020. Erişim tarihi Mayıs 4, 2020.