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مقایسه عملکرد شبکههای عصبی مصنوعی و برنامهریزی بیان ژن در برآورد منحنی مشخصه آب در خاکهای جنگلی | ||
تحقیقات آب و خاک ایران | ||
دوره 52، شماره 8، آبان 1400، صفحه 2093-2109 اصل مقاله (1.46 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2021.322879.668952 | ||
نویسندگان | ||
محمد مهدی جعفری1؛ حسن اوجاقلو* 2؛ مسعود کرباسی3 | ||
1گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران | ||
2استادیار-گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران | ||
3دانشیار -گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران | ||
چکیده | ||
منحنی مشخصه آب خاک یکی از پارامترهای فیزیکی مهم و کاربردی در مطالعات مرتبط با جریان آب در خاک شناخته میشود. روش مستقیم اندازهگیری منحنی مشخصه آب خاک مستلزم صرف زمان و هزینه بالایی است. به همین دلیل روشهای غیرمستقیم متنوعی از جمله مدلهای هوشمند توسعه پیدا نمودهاند. در این تحقیق عملکرد سه روش شبکههای عصبی پرسپترون چندلایه (MLP)، شبکههای عصبی آبشاری (Cascade-NN) و برنامهریزی بیان ژن (GEP) در برآورد منحنی مشخصه آب خاک مورد ارزیابی و مقایسه قرار گرفت. در این پژوهش اطلاعات اندازهگیری شده مربوط به تعداد ۱۰۸ نمونه خاک مناطق جنگلی شامل درصد توزیع اندازه ذرات خاک، مقادیر رطوبت در هفت مکش مختلف و جرم مخصوص ظاهری مورد استفاده قرار گرفت. سه سناریو شامل ترکیبهای مختلف از دادههای ورودی تعیین و مدلهای مذکور برای هر کدام اجرا شد. مقایسه مقادیر پیشبینی شده و مشاهداتی رطوبت خاک نشان دهنده عملکرد قابل قبول هر سه مدل بود؛ برای مرحله آزمون مقادیر R2 برای بهترین ساختار در سه روش شبکههای عصبی MLP، Cascade-NN و GEP به ترتیب ۹۵/۰، ۹۶/۰ و ۹۳/۰ و مقادیر RMSE نیز به ترتیب ۷۴/۳، ۲۵/۳ و ۱۰/۴ درصد بود. مقایسه نتایج سناریوهای مختلف داده ورودی نیز نشان داد، دقت و اختلاف بین نتایج مدلها در سناریوی اول کم بود ولی در سناریوی دوم و سوم به ترتیب با اضافه شدن پارامترهای تخلخل و رطوبت نقطه ظرفیت زراعی به دادههای ورودی، دقت و از سوی دیگر اختلاف بین نتایج مدلها بیشتر شد. در نهایت شبکههای عصبی آبشاری با استفاده از تمام دادههای فیزیکی اشاره شده به عنوان گزینه مطلوب شناخته شد. | ||
کلیدواژهها | ||
پیش بینی؛ رطوبت خاک؛ مدل های هوشمند؛ مکش | ||
عنوان مقاله [English] | ||
Comparison of the Performance of Artificial Neural Networks and Gene Expression Programming in Estimating the Forest Soil Water Characteristic Curve | ||
نویسندگان [English] | ||
Mohammad Mahdi Jafari1؛ Hassan Ojaghlou2؛ Masoud Karbasi3 | ||
1Department of Water Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran | ||
2Assistant professor-Department of Water Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran | ||
3Associate professor - Department of Water Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran | ||
چکیده [English] | ||
One of the most important and practical physical parameters in studies of soil water flow is Soil Water Characteristic Curve (SWCC). Measuring the soil moisture characteristic curve through the direct method is expensive and time-consuming. For this reason, a variety of indirect methods including intelligent models have been developed. In this study, the performance of three models included multilayer perceptron neural networks (MLP), cascade neural network (Cascade-NN) and gene expression programming (GEP) were evaluated and compared to estimate of SWCC. The measured data from 108 soil samples, including soil particle size distribution, soil moisture in different suctions and the bulk density were used. In all models, three different input data combinations were used. Comparison of predicted and observed values of soil moisture showed acceptable performance of all three models, however, the Cascade-NN neural network model was relatively superior. The R2 values of test phase for the best structure of the neural networks (MLP), neural networks (Cascade-NN) and gene expression programming (GEP) were 0.95, 0.96 and 0.93, respectively, and the RMSE values were 3.74, 3.25 and 4.10 %, respectively. Comparison of the results of different input data scenarios indicated the low accuracy and difference between the results of the models in the first scenario, but adding the parameters of porosity and moisture at field capacity point to the input data in the second and third scenarios, increased the accuracy and difference between the results achieved by the models. Finally, it can be emphasized that the cascade-NN model was introduced as the superior option, using all the mentioned physical data. | ||
کلیدواژهها [English] | ||
Intelligent Models, Prediction, Soil Moisture, Suction | ||
مراجع | ||
Abbasi, A., Khalili, K., Behmanesh, J. and Shirzad, A. (2020). Comparison of artificial neural networks, bayesian network and gene expression programming in drought prediction (Case Study: Maragheh Synoptic Station). Journal of Watershed Management Research, 11(21), 59-71. (In Farsi)
Akbarzadeh, A., Taghizadeh Mehrjardi, R., Rahimi Lake, H. and Ramezanpour, H. (2009). Application of artificial intelligence in modeling of soil properties (Case study: Roodbar Region, North of Iran). Environmental Research Journal, 3(2), 19-24.
Alidadi, N. and Mahdavian, A. (2018). Modeling the amplification ratio of sandy soils using two methods of neural network and gene. Scientific Quarterly Journal, Geosciences, 107, 87-98. (In Farsi)
Bayat, H., Neyshaburi, M. R., Mohammadi, K., Nariman-Zadeh, N., Irannejad, M. and Gregory, A. S. (2013). Combination of artificial neural networks and fractal theory to predict soil water retention curve. Computers and Electronics in Agriculture, 92, 92–103.
Davari, M., Zalvaee, Z. and Mahmoodi, M. A. (2019). A comparison between empirical and fractal models fitted to the measured soil moisture characteristic curve data. Iranian Journal of Soil and Water Research, 50(4), 847-862. (In Farsi)
Dehghani, R., Younesi, H. and 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). Journal of Water and Soil Conservation, 24(4), 161-177. (In Farsi)
Diamantopoulou, M. J., Antonopoulos, V. Z. and Papamichail, D. M. (2007). Cascade correlation artificial neural networks for estimating missing monthly values of water quality parameters in rivers. Water Resour Manage, 21, 649–662.
Emamgolizadeh, S., Bateni, S. M., Shahsavani, D., Ashrafi, T. and Ghorbani, H. (2015). Estimation of soil cation exchange capacity using genetic expression programming (GEP) and multivariate adaptive regression splines (MARS). Journal of Hydrology, 529(3), 1590-1600.
Fahlman, S. E. and Lebiere, C. (1990). The cascade-correlation learning architecture. Advances in Neural Information Processing Systems, 524–532.
Ferreira, C. (2001). Gene expression programming a new adaptive algorithm for solving problems. Complex Systems, 13(2), 87-129.
Ferreira, C. (2006). Gene expression programming: mathematical modeling by an artificial intelligence 2nd ed. Springer-Verlag, Germany.
Garg, A., Garg, A. and Tai, K. (2014). A multi-gene genetic programming model for estimating stress-dependent soil water retention curves. Comput Geosci, 18, 45–56.
Ghorbani Dashtaki, SH. and Homaei, M. (2002). Derivation of the retention curve parameters using pedotransfer function. Journal of Agricultural Engineering Research, 3(12), 1-16. (In Farsi)
Ghorbani, M. A., Deo, R. C., Kim, S., Kashani, M. H., Karimi, V. and Izadkhah, M. (2020). Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia. Soft Computing, 24, 12079–12090.
Haghverdi, A., Ghahraman, B., Joleini, M., Khoshnud Yazdi, A. A. and Arabi, Z. (2011). Comparison of different artificial intelligence methods in modeling water retention curve (Case study: North and Northeast of Iran). Journal of Water and Soil Conservation, 18(2), 65-84. (In Farsi)
Hosseini, S. A., golabi, M. R., marofi, S., khalediyan, N. and solatani, M. (2020). Evaluation of extended kalman filter-based neural network (EKFNN) model and gene expression planning in rainfall-runoff modelin. Journal of Watershed Engineering and Management, 12(3), 771-784. (In Farsi)
Hsu, K. L., Gupta, H. V. and Sorooshian, S. (1995). Artificial neural network modeling of the rainfall-runoff process. Water Resources Research, 31(10), 2517-2530.
Jauhiainen, M. (2004). Relationships of particle size distribution curve, Soil water retention curve and unsaturated hydraulic conductivity and their implications on water balance of forested and agricultural hillslopes, Ph. D thesis, Helsinki University of Technology, Helsinki/Finland.
Jenadeleh, N., Nadian, H. A., Khalilimoghadam, B. and Ghorbani dashtaki, S. (2017). Point estimation of soil moisture characteristic curve using artificial neural networks and its optimizing by genetic algorithm in Agro-Industries of Khouzestan. Watershed Management Research (Research and Construction), 113, 40-50. (In Farsi)
Johari, A., Habibagahi, G. and Ghahramani, A. (2006). Prediction of Soil–Water Characteristic Curve Using Genetic Programming. Journal of Geotechnical and Geoenvironmental Engineering, 132:661-665.
Johari, A., Javadi, A. A. and habibagahi, G. (2011). modeling the mecanical behavior of unsaturated soils using a genetic algorithm-based neural network. Computer and Geotecnic, 38, 2-13.
Johari, A. and Hooshmand Nejad, A. (2015). Prediction of soil-water characteristic curve using gene expression programming. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 39(1), 143-165.
Kia, M. (2009). Neural networks in matlab. Kian Rayan Sabz Publication, Tehran, pp, 408. (In Farsi)
Kianpoor Kalkhajeh, Y., Rezaie Arshad, R., Amerikhah, H. and Sami, M. (2012). Multiple linear regression, artificial neural network (MLP, RBF) and ANFIS models for modeling the saturated hydraulic conductivity of tropical region soils (A case study: Khuzestan Province: Southwest Iran). International Journal of Agriculture: Research and Review, 2(3), 255-265.
Kisi, O., Shiri, J. and Tombul, M. (2013). Modeling rain fall-runoff process using soft computing techniques. Computers Geosciences, 51, 108-117.
Lentzsch, P., Wieland, R. and Wirth, S. (2005). Application of multiple regression and neural network approaches for landscape-scale assessment of soil microbial biomass. Soil Biology and Biochemistry, 37, 1577-1580.
Mahmoudabadi, E., Karaimi, A. R., Haghnia, Gh. H. and Sepehr, A. (2017). Assessing performance of multivariate linear regression (MLR), artificial neural network (ANN) and gene expression programming (GEP) in estimating soil properties. Journal of Water and Soil Conservation, 24(2), 23-44. (In Farsi)
Mehdipour, V., Memarianfard, M. and Homayounfar, F. (2017). Application of gene expression programming to water dissolved oxygen concentration prediction. International Journal of Human Capital in Urban Management, 2(1), 1-10.
Merdun, H., Cinar, O., Meral, R. and Apan, M. (2006). Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil and Tillage Research, 90, 108-116.
Mermoud, A. and Xu, D. (2006). Comparative analysis of three methods to generate soil hydraulic functions. Soil and Tillage Research, 87, 89-100.
Minasny, B. and McBratney, A. B. (2002). The neuro-m method for fitting neural network parametric pedotransfer functions. Soil Science Society of America Journal, 66, 352-361.
Mohammadi, J. (2002). Testing an artificial neural network for predicting soil water retention characteristics from soil physical and chemical properties. 17TH WORLD CONGRESS OF SOIL SCIENCE, Thailand, 378-943.
Moosavizadeh-Mojarrad, R. and Sepaskhah, A. R. (2011). Predicting soil water retention curve by artificial neural networks. Archives of Agronomy and Soil Science, 57(1), 3-13.
Nikooee, E., Mirghafari, R., Habibagahi, G., Ghadamgahi Khorassani, A. and Nouri, A. M. (2020). Determination of soil-water retention curve: an artificial intelligence-based approach. E3S Web of Conferences, 195, 1-6.
Oliaei, M. S., Barikloo, A. and Servati, M. (2019). Performance evaluation of artificial neural networks conjunct with genetic algorithm for estimation of soil infiltration rate (Case Study: Khoda afarin Region of East Azerbaijan Province). Iranian Journal of Soil and Water Research, 50(5), 1127-1139. (In Farsi)
Rahimi Lake, H., Akbarzadeh, A. and Taghizadeh Mehrjardi, R. (2009). Development of pedotransfer functions (PTFs) to predict soil physico-chemical and hydrological characteristics in southern coastal zones of the Caspian Sea. Ecology and The Natural Environment, 1(7), 160-172.
Rostamlou, M., Ojaghlou, H. and Karbasi, M. (2018). Compartion performance of adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) to estimate water distribution uniformity coefficient in classic sprinkle irrigation systems. Iranian Water Research Journal, 12(4), 85-94. (In Farsi)
Ryan, M., Müller, C., Di, H. J. and Cameron, K. C. (2005). The use of artificial neural networks (ANNs) to simulate N2O emissions from a temperate grassland ecosystem. Ecological Modelling, 175, 189–194.
Sarmadian, F., Taghizadeh Mehrjui, R. A. and Akbarzadeh, A. (2009). Optimization of pedotransfer functions using an artificial neural network. Australian Journal of Basic and Applied Sciences, 3, 323-329.
Schaap, M. G., Leij, F. J. and Van Genuchten, M. T. (1998). Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil Science Society of America Journal, 62, 847– 855.
Shahinejad, B. and Dehaghani, R. (2018). Comparison of wavelet neural network models, support vector machine, and gene expression programming in estimating the amount of oxygen dissolved in rivers. Iran-Water Resources Research, 14(3), 265-277. (In Farsi)
sheikhesmaeili, O., Moazed, H. and Naseri, A. A. (2016). Evaluation of estimation methods for water field capacity in soils of Khuzestan Province. Iranian Journal of Soil and Water Research, 47(1), 55-63. (In Farsi)
Shirani, H. and Rafienejad, N. (2012). Prediction of some difficult-to-measure soil characteristics using regression pedotransfer functions and artificial neural network in kerman province. Iranian Journal of Soil Research (Soil and Water Science), 25(4), 349-359.
Shiri, J., Sadraddini, A. A., Nazemi, A. H., Kisi, O., Landeras, G., Fakheri Fard, A. and Marti, P. (2014). Generalizability of gene expression programming-based approaches for estimating daily reference evapotranspiration in coastal stations of Iran. Journal of Hydrology, 508, 1–11.
Solgi, A., Zarei, H. and Golabi, M. R. (2017). Performance assessment of gene expression programming model using data preprocessing methods to modeling river flow. Journal of Water and Soil Conservation, 24(2), 185-201. (In Farsi)
Traore, S. and Guven, A. (2012). Regional specific numerical models of evapotranspiration using gene-expression programming interface in Sahel. Water Resources Management, 26, 4367–4380.
Wosten, J. H. M., Pachepsky, Y. A. and Rawls, W. J. (2001). Pedotransfer functions bridging the gap between available basic soil data and missing soil hydraulic characteristics. Hydrology, 251, 123-150.
Yosefi, M. and Poorshariaty, R. (2014). Suspended sediment estimation using neural network and algorithms assessment (Case Study: Lorestan Province). Journal of Watershed Management Research, 5(10), 85-97. (In Farsi)
Zarei, M. M., Dastorani, M. T., Mesdaghi, M. and Eshghizadeh, M. (2017). Evaluation of the efficiency of different artificial intelligence and statistical methods in estimating the amount of runoff (Case Study: Shahid Noori Watershed of Kakhk, Gonabad). Journal of Watershed Management Research, 8(16), 11-21. (In Farsi)
Zare zade Mehrizi, M. and Bozorg Hadad, A. (2008). Optimization of number of layers and neurons in artificial neural network with genetic algorithm method in flow prediction. 3rd Conference of Water Resources Management. Tabriz University. Faculty of Civil Engineering. Available in http://www.civilica.com. (In Farsi)
Zhu, A. X. and Julian, J. (2011). Unsaturated hydraulic properties of anisotropic soils. State Water Resources Research Institute Program. | ||
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