دوماه نامه

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه یزد

2 مرکز ملی شوری

چکیده

در این تحقیق تأثیر روش‌های پیش پردازش داده‌های شوری درافزایش دقت شبیه‌سازی‌های صورت گرفته توسط الگوریتم درخت تصمیم در منطقه مروست مورد بررسی قرار گرفته است. به این منظور شبیه‌سازی‌ها در سه حالت (استفاده از داده‌های اصلی، استفاده از لگاریتم داده‌ها و استفاده از داده‌های استاندارد شده) صورت گرفت. نتایج نشان داد علیرغم معنی دار بودن ضریب همبستگی در هر سه حالت، میزان خطا در حالت استفاده از لگاریتم داده‌ها نسبت به دو حالت دیگر کمتر بوده و نتایج به واقعیت نزدیکتر می‌باشد. به طوری که این حالت (استفاده از لگاریتم داده ها)، درخت ایجاد شده قادر است با ترکیب "باند 7، ارتفاع" و استفاده از 5 قانون، میزان شوری خاک سطحی را برآورد نماید. با توجه به اینکه توزیع احتمالاتی حاکم بر داده‌های شوری منطقه، یکی از توزیع های خانواده لگاریتم(Log-Pearson 3) می‌باشد می‌توان اظهار داشت، کاهش خطا در حالت استفاده از لگاریتم داده‌ها در ارتباط نزدیک با توزیع احتمالاتی حاکم بر داده‌های شوری منطقه مورد بررسی در این تحقیق می‌باشد. لذا شبیه سازی با استفاده از لگاریتم داده‌ها به دلیل خطای کمتر و نیازمندی به داده‌های ورودی کمتر به عنوان مدل برتر شناخته شد.آماره های خطای R، Rmse، %Rmse، MAE وBias در این حالت 76/0، 49/0، 58/38، 37/0 و 14/0- بدست آمد.

کلیدواژه‌ها

عنوان مقاله [English]

Investigation of the Optimal Method of Data Processing to Increase Accuracy of Simulation of Surface Soil Salinity (Case study: MARVAST)

نویسندگان [English]

  • azam habibipoor 1
  • Ali Talebi 1
  • Ali Akbar Karimian 1
  • Farhad Dehghani 2
  • Mohammad Hosain Mokhtari 1

1 Yazd University

2 Faculty of national salinity research center, Yazd

چکیده [English]

Introduction: Salinity is one of the problems of arid and semi-arid soils. Identification and classification of saline/alkaline soils is necessity for dealing with difficult situations and correct management. Considering the nature of salinity data and selection of befitting methods to process data before use artificial neural network, can result in better simulations. The aim of this study was to investigate the optimal method for data processing to enhance the accuracy of surface soil salinity simulation and improve the efficiency of decision tree algorithm.
Materials and Methods: The study area was 88940.4 hectares of Marvast plain located in central Iran (54° 5´to 54° 18´ east longitude and 30° 10´to 30° 35´north latitude). This region faces with problems of soil and water resources salinity. In this study, the effect of data processing on increasing accuracy of simulation of soil surface salinity was assessed in Marvast region using decision tree algorithm. For this purpose, the decision tree algorithm was applied and simulation was performed using three approaches i.e. original data, logarithmic data and standardized data. Finally, five statistics including R، Rmse، %Rmse، MAE and Bias were calculated to evaluate the performance of used simulation methods.
Results and Discussion: In this study, when the logarithmic data was used, the composition of band 7 – elevation was identified as the most appropriate condition. The created tree can estimate the soil salinity by five laws:
If elevation is less than 1519, then the average of surface soil salinity will be 147.9 ds/m.
If elevation is between 1519 to 1569.9, then the average of surface soil salinity will be 43.6 ds/m.
If elevation is between 1569.9 to 1609.8, then the average of surface soil salinity will be 17.5 ds/m.
If elevation is more or equal to 1609.8 and pixel value of band 7 (ETM+ sensor) in selected point is less than 0.295, then the average of surface soil salinity will be 4.7 ds/m.
If elevation is higher or equal to 1609.8 and pixel value of band 7 (ETM+ sensor) in selected point is more than or equal to 0.295, then the average of surface soil salinity will be 1.4 ds/m.
For the approach of using the logarithmic data, decision tree algorithm used two parameters out of 46 independent variables introduced into the model. R، Rmse، %Rmse، MAE and Bias for this method was computed to be 0.76, 0.49, 38.57, 0.37 and -0.14, respectively. The application of logarithmic data was recognized as the best method considering the lower calculated error and its less input requirement. Using Easy fit software, the distribution of salinity data was found to be Log Pearson 3. Thus, the use of logarithmic data improved model performance. Our findings were in agreement with those of Afkhami et al (2015) who increased the simulation accuracy of suspended sediment with artificial intelligence methods (Artificial neural networks and ANFIS) using logarithmic data.
Conclusions: As effective factors for soil salinity simulation vary in different regions, application of a unique method and indicator to estimate soil salinity in deferent region may not be possible.. The application of semi intelligent algorithm which limits user intervention and selects effective parameters for simulation would increase the simulation accuracy. Furthermore, considering the nature of salinity data and selection of befitting methods to process before using decision tree algorithm can effectively improve model performance. The current study was conducted to select an appropriate approach to enhance the simulation accuracy of surface soil salinity. The results demonstrate that the performance of decision tree algorithm as one of the artificial intelligence models can be affected by input data. In this study, Log-Pearson3 distribution was defined as the distribution of salinity data. Moreover, despite existence of significant correlation coefficients for three simulation methods, the error was lower when logarithmic data was used. Since the probability distribution of salinity data in the studied area was logarithmic (Log-Pearson 3), the reduction in error rate can be attributed to the probability distribution of salinity data.

کلیدواژه‌ها [English]

  • Normalization
  • Probability distribution
  • Regression decision tree
  • Electrical conductivity
1- Abdelfattah M., Shahid Sh., and Othman Y. 2009. Soil salinity mapping model developed using RS and GIS: A case study from Abu Dhabi, United Arab Emirates. European Journal of Scientific Research 26 (3): 342-351.
2- Abdinam A. 2005. Investigation of soil salinity mapping using the correlation between satellite data and numerical values of soil salinity in Ghazvin plain. Journal of research and building. (In Persian).
3- Afkhami H., Dastoorani M, T., and Fotouhi F. 2015. The impact probability distribution to increase accuracy of prediction of suspended sediment using artificial neural networks and neuro-fuzzy inference system (Case Study: Watershed Dez). Iranian Journal of Watershed Management Science and Engineering. (21), 21-35. (In Persian).
4- Ahmadian M., and Pakparvar V. 2006. Evaluation of Soil Salinity using RS & GIS in Ghahavand plain. Agriculture and Natural Resources Research Center of Hamadan. (In Persian).
5- Amini M. 1999. Investigation of geostatistics of soils salinity and alkalinity in selected soils in the Rodasht region. M.Sc Thesis of Soil Sciences. College of Agriculture. Isfahan University of Technology. 119p, (In Persian).
6- Breiman L., Friedman J., Olshen R., and Stone C. 1984. Classification and Regression Trees. Chapman & Hall/CRC Press, Boca Raton, FL.
7- Buces F.N., Siebe C., Cram S., and Palacio, J.L. 2006. Mapping soil salinity using a combined spectral response index for bare soil and vegetation: (A case study in the former lake Texcoco, Mexico). Journal of Arid Environments, 65:644-667.
8- Chitsaz V. 1999. Possibility investigation of mapping soil salinity and alkalinity in eastern region of Isfahan using TM Digital data. M.Sc Thesis. Isfahan University of Technology. 129p, (In Persian).
9- Dashtakian K., Pakparvar M., and Abdallai J. 2008. Investigation of mapping methods using Landsat data in Marvast region. Iranian Journal of Range and Desert Research.15 (2): 139-157. (In Persian).
10- Dwivedi R. S., and Sreenivas K. 1998. Image transforms as a tool for the study of soil salinity and alkalinity dynamics. International Journal of Remote Sensing, 19: 605-619.
11- Ebrahimian H., Liaghat A., and Bazrafshan M. 2011. Estimation of Some Climatic Parameters by Using Pedo-Transfer. Iranian Journal of Watershed Management Science and Engineering. (14), 77-85. (In Persian).
12- Eldiery A., Garcia L., and Reich R. 2005. Estimating soil salinity from remote sensing data in Corn fields. Hydrology days, 2005. Colorado State University fort Collins, co 80523-1372.
13- Jafari Gorzin B. 2002. Study of landsat ETM+ capability in detecting salt affected lands (a case study in Gorgan Plain), a thesis of presented for M.Sc. Gorgan university of Agriculture and Natural Resource Science, college of Range and Watershed Management, 127p.
14- Khajaldin S, J. 1996. Using data of Landsat MSS 5 for investigation of Plant communities and identify soil lands in Jazmoorian region. 02nd National Conference on desertification and desertification control methods. Kerman city. (In Persian).
15- Mohammadi Takami S, M. 2005. The methods of data processing and pattern recognition. K.N. Toosi University of Technology. (In Persian).
16- Naeijnoori R. 2001. Investigation on possibility of Separation salinity and gypsum land using TM data. M.Sc Thesis of desertification, collage of natural resource, Isfahan University of Technology. (In Persian).
17- Rivero R. G., Grunwald S., and Bruland G. L. 2007. Incorporation of spectral data into multivariate geostatistical models to map soil phosphorus variability in a Florida wetland, Geoderma, 140: 428-443.
18- Taghizadeh-Mehrjardi R., Minasny B., Sarmadian F., and Malone B, P. 2014. Digital mapping of soil salinity in Ardekan region, central Iran. Geoderma. 213: 15-28.
19- Tajgardan T., Ayoubei sh., Shetaei Sh., and Khormali F. 2009. Mapping of surface soil salinity using ETM+ data (case study: Northern Aq Qala , Gulistan Province. (In Persian).
20- Wilding L.P. 1985. Spatial variability: Its documentation, accommodation, and implication to soil survey. In: Nielsen, D.R., and J. Bouma, (eds.), Soil Spatial Variability, Pudoc, Wagenigen, the Netherlands. 166-194.
21- Wu J.,Vincent B., Yang, ., Bouarfa S., and Vidal A. 2008. Remote sensing monitoring of changes in soil salinity: A case study in Inner Mongolia, China. Journal of Sensors, 8: 7035-7049.
CAPTCHA Image