آمار
| تعداد نشریات | 13 |
| تعداد شمارهها | 660 |
| تعداد مقالات | 6,888 |
| تعداد مشاهده مقاله | 10,067,747 |
| تعداد دریافت فایل اصل مقاله | 9,346,229 |
An Estimation of Basin Sediment using Regression Analysis and Artificial Neural Network- A Case Study in Kordan Basin | ||
| Environmental Resources Research | ||
| دوره 9، شماره 2، مهر 2021، صفحه 291-304 اصل مقاله (968.82 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22069/ijerr.2021.18716.1329 | ||
| نویسندگان | ||
| Hassan Rashidi* 1؛ Navid Raisi2 | ||
| 1Allameh Tabataba'i University | ||
| 2Qazvin Branch, Islamic Azad University | ||
| چکیده | ||
| Soil is an essential natural resource for life that provides the required substrate on which plants grow and flourish. One of the challenges for environmental specialists is to accurately estimate and control soil erosion. MPSIAC (Modified model of Pacific Southwest Inter-Agency Committee) is a common model for estimating erosion and sedimentation rate. In this study, we used MPSIAC, regression and artificial neural networks (ANN) to estimate sediment yield in Kordan Basin, a region in Alborz Province of Iran. The erosion and sedimentation data of the region were collated using the opinions of sedimentation experts. A linear regression was performed in Weka software to determine the factors influencing the sedimentation rate. Based on the results and the opinion of the experts, the factors with less impact on the sedimentation were removed. ANN was implemented using NeuroSolutions and Matlab software. The neural network was a Multi-Layer Perceptron (MLP) with one hidden layer and five neurons. The hidden layer consisted of tan-sigmoid activation function, and the output layer had a linear-sigmoid activation function. The algorithm used for training the neural network was Levenberg-Marquardt. The ANN results were superior to that of regression and the Matlab's output was more accurate than that of NeuroSolutions, with a mean square error of 0.009 for sediment yield. Finally, Matlab's neural network was extracted in the form of a function for later applications without the need to further training. | ||
| کلیدواژهها | ||
| Soil Erosion؛ Artificial Neural Networks؛ MPSIAC Model؛ MLP Neural Network | ||
| مراجع | ||
|
Noori, H., SiadatMousavi, S.M., and Mojaradi, B. 2016.Assessment of sediment yield using RS and GIS at two sub-basins of Dez Watershed, Iran. International Soil and Water Conservation Research. 4(3), 199-206.
Khalilmoghadam, B., Afyuni, M., Abbaspour, K.C., Jalalian A., Dehghani, A. A., and Schulin, R.2009. Estimation of surface shear strength in Zagros region of Iran. A comparison of artificial neural networks and multiple linear regression models. Geoderma. 153 (1–2), 29-36.
Taborda Silveira, C., Oka-Fiori, L., Jose Cordeiro Santos, C, Evaristo Sirtoli, A., Ribeiro Silva, C., and Faria Botelho, M. 2013. Soil prediction using artificial neural networks and topographic attributes, Geoderma. 165-172.
Vente, J.D., and Poesen, J. 2005. Predicting soil erosion and sediment yield at the basin scale: Scale issues and semi-quantitative models. Earth-Science Reviews. 71(1–2), 95-125.
Mansouri Daneshvar, M.R., and Bagherzadeh A. 2012. Evaluation of sediment yield in PSIAC and MPSIAC models by using GIS at Toroq Watershed, Northeast of Iran, Frontiers of Earth Science. 6 (1), 83-94.
Hassoun, M. 2003. Fundamentals of Artificial Neural Networks, A Bradford Book.
Fausett, L.V. 1994. Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, Prentice-Hall.
Collobert, R., Bengio, and S. 2004. Links Between Perceptrons, MLPs and SVMs, Proceedings of the 21st International Conference on Machine Learning, Banff, Alberta, Canada
Kim, M. and Gilley, J.E. 2008. Artificial Neural Network estimation of soil erosion and nutrient concentrations in runoff from land application areas. Computers and Electronics in Agriculture. 64 (2), 268 -275.
Alp, M., and Kerem Cigizoglu H. 2007. Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environmental Modelling and Software. 22 (1), 2–13.
Nourani, V., Kalantari, O., and Baghanam, A.H. 2012. Two semi-distributed ANN-based models for estimation of suspended sediment load, Journal of Hydrologic Engineering. 17 (12), 1368-1380.
Palani, S., Yui Liong, Sh., and Tkalic, P. 2008. An ANN application for water quality forecasting. Marine Pollution Bulletin. 56 (9), 1586-1597.
Melesse, A.M., Ahmad, S., McClain, M.E., Wang, X., Lim, Y.H. 2011. Suspended sediment load prediction of river systems: An artificial neural network approach, Agricultural Water Management. 98 (5), 855-866.
Ardiclioglo, M., Kis, and O., Haktanir, T. 2007. Suspended sediment prediction using two different feed-forward backpropagation algorithms. Canadian Journal of Civil Engineering. 34 (1), 120-125.
Kisi, O., Ozkan, C., and Akay, B. 2012. Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm, Journal of Hydrology. 94-103.
Trenouth, W.R., and Gharabaghi, B. 2015. Event-based soil loss models for construction sites, Journal of Hydrology. 524, 780–788.
Draper, N.R., and Smith, H.1998. Applied Regression Analysis, 3rd Edition, Wiley-Interscience
Marvin, L.2016. Neural Networks with MATLAB, Create Space Independent Publishing Platform.
Khan, M.Y.A., Tian, F., Hasan, F., Chakrapani, G.J. 2019. Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga, Ganges Basin, India. International Journal of Sediment Research. 34(2), 95-107.
Afrakhteh, S., Mosavi, M. R., Khishe, M., and Ayatollahi, A. 2020. Accurate Classification of EEG Signals Using Neural Networks Trained by Hybrid Population-physic-based Algorithm. International Journal of Automation and Computing. 17(1), 108-122.
Jafarzadeh, A., Mahdavi, A. Shamsi, S.R.F, and Yousefpour, R. 2021. Assessing synergies and trade-offs between ecosystem services in forest landscape management, Article in Press.
Layeghi, N., Javadi, S.A. Jafari, M. Arzani H, 2020. Measuring the land use based risk of soil erosion in a mining-dominated landscape in Northern Iran, Journal of Ecological Engineering. 21(7), 271-282. | ||
|
آمار تعداد مشاهده مقاله: 469 تعداد دریافت فایل اصل مقاله: 335 |
||
سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب