Investigation of the land potential of Kermanshah province for rainfed wheat cultivation using artificial neural network

Subject Areas : Geospatial systems development

Milad Bagheri ¹ , Mohammadreza Jelokhani Noaryki ² , Kayvan Bagheri ³

1 - MSc. Student of Remote Sensing and GIS, University of Tehran
2 - Assis. Prof. College of Geography, University of Tehran
3 - Ph.D. Student of Remote Sensing and GIS, University of Tehran

Received: 2017-09-05 Accepted : 2017-11-23 Published : 2018-02-20

Keywords: zoning, Wheat, Multilayer Perceptron (MLP), Neural Networks,

Abstract :

With increasing population growth and the need for food, wheat as the crop with the largest cultivated area and annual production on a global scale has been especially important. Therefore, identifying and recommending suitable areas for cultivation in each area is essential.  Kermanshah province as the study area is one of the areas that most wheat crops are from among. Therefore, in this study Multilayer Perceptron Neural Network (MLP) with Levenberg-Marquardt algorithm was used to identify the potential of rainfed wheat cultivation. The input layer network consists of 12 layers: land use, average annual rainfall, average rainfall in the autumn, the average spring rainfall, the average annual temperature, average temperatures in spring, average temperatures in autumn, slope, aspect, elevation, humidity the relative and degree of days. The rainfall and temperature layers were prepared using the data from the stations of adventurous and synoptic and the interpolation operation in the ArcGIS environment, respectively. The altitude-related layer was extracted using with a DEM 30×30 meter IRS. To determine the search space of the neural network algorithm, the uncultivated areas are determined and removed from the entire input layers. 210 points of The right place to cultivate were prepared as network training points. Finally, the class of uncultivated areas which 15% and The results of the model consists of five classes: very suitable, suitable, somewhat suitable, poor or very poor, respectively, 5.4, 14.8, 24, 22.5 and 18.3 percent of the total area of the province is allocated. Regression analysis of all data on the network is 91% of the network of the company, effective for the MLP neural network is in these zoning.

References:

1. عینی، ح.، س. صادقی و س. ر. حسین­زاده. 1391. پهنه‌بندی پتانسیل‌های توپوکلیمایی کشت گندم دیم در استان کرمانشاه. جغرافیا و توسعه ناحیه­ای، 10(19): 21-45.

2. کمالی، غ. ع.، ع. صدقیانی­پور، ع. صداقت­کردار و ا. عسکری. 1387. بررسی پتانسیل اقلیمی کشت گندم دیم در استان آذربایجان شرقی. آب و خاک، 22(2): 467-483.

3. Brink R, Young A. 1977. A framework for land evaluation. FAO Soils Bulletin No. 32. International Institute for Land Reclamation and Improvement/ILRI. 94 pp.

4. Fischer G, Van Velthuizen H, Shah M, Nachtergaele FO. 2002. Global agro-ecological assessment for agriculture in the 21st century: methodology and results. Food and Agriculture Organization of the United Nations. International Institute for Applied Systems Analysis Laxenburg, Austria. 155 pp.

5. Ghaffari A. 2000. Application of GIS and crop simulation modelling to assess crop suitability and production potential under current and climate change scenarios in the Stour Catchment, Kent, UK. PhD thesis, Wye College, University of London. 233 pp.

6. Huang H-C, Hwang R-C, Hsieh J-G. 2002. A new artificial intelligent peak power load forecaster based on non-fixed neural networks. International Journal of Electrical Power & Energy Systems, 24(3): 245-250.

7. Jalili Ghazi Zade M, Noori R. 2008. Prediction of municipal solid waste generation by use of artificial neural network: A case study of Mashhad. International Journal of Environmental Research, 2(1): 13-22.

8. Jarvis C, Stuart N, Hims M. 2002. Towards a British framework for enhancing the availability and value of agro-meteorological data. Applied Geography, 22(2): 157-174.

9. Khan M, De Bie C, Van Keulen H, Smaling E, Real R. 2010. Disaggregating and mapping crop statistics using hypertemporal remote sensing. International Journal of Applied Earth Observation and Geoinformation, 12(1): 36-46.

10. Khush GS. 2005. What it will take to feed 5.0 billion rice consumers in 2030. Plant Molecular Biology, 59(1): 1-6.

11. Kiartzis S, Bakirtzis A, Petridis V. 1995. Short-term load forecasting using neural networks. Electric Power Systems Research, 33(1): 1-6.

12. Molden D. 2007. Water for food, water for life: a comprehensive assessment of water management in agriculture. Earthscan. International Water Management Institute. 645 pp.

13. Noori R, Karbassi A, Mehdizadeh H, Vesali‐Naseh M, Sabahi M. 2011. A framework development for predicting the longitudinal dispersion coefficient in natural streams using an artificial neural network. Environmental Progress & Sustainable Energy, 30(3): 439-449.

14. Noori R, Khakpour A, Omidvar B, Farokhnia A. 2010. Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic. Expert Systems with Applications, 37(8): 5856-5862.

15. Reilly DL, Cooper LN. 1995. An overview of neural networks: early models to real world systems. In:  How We Learn; How We Remember: Toward An Understanding Of Brain And Neural Systems: Selected Papers of Leon N Cooper. World Scientific, pp 300-321.

16. Sadras VO, McDonald G. 2012. Water use efficiency of grain crops in Australia: principles, benchmarks and management. Change, 11(19): 24-55.

17. Sharda R. 1994. Neural networks for the MS/OR analyst: An application bibliography. Interfaces, 24(2): 116-130.

18. Shen S, Yang S, Li B, Tan B, Li Z, Le Toan T. 2009. A scheme for regional rice yield estimation using ENVISAT ASAR data. Science in China Series D: Earth Sciences, 52(8): 1183-1194.

19. Subasi A, Ercelebi E. 2005. Classification of EEG signals using neural network and logistic regression. Computer Methods and Programs in Biomedicine, 78(2): 87-99.

20. Tucker CJ. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8(2): 127-150

21. Yilmaz I. 2009. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Computers & Geosciences, 35(6): 1125-1138.