کاربرد الگوریتم ژنتیک در برآورد توزیع مکانی پ‌هاش خاک با ترکیب زمین‌آمار و برنامه‌ریزی بیان ژن

پرویز, لاله; احدی, رضا; سید میلانی, فاطمه

نوع مقاله : مقاله پژوهشی

نویسندگان

¹ دانشگاه شهید مدنی آذربایجان

² دانشگاه شهید مدنی اذربایجان

چکیده

آگاهی در مورد توزیع مکانی ویژگیهای خاک نقش مهمی در سیستم رشد و عملکرد محصول در بخش کشاورزی دارد. در این راستا جهت افزایش دقت در توزیع مکانی pHخاک، الگوریتم ژنتیک در ترکیب برآوردهای رگرسیون خطی، برنامهریزی بیان ژن (GEP) و زمینآمار (درونیابی کریجینگ) در مناطقی از استان آذربایجانشرقی (شهرستانهای بناب، عجبشیر و مراغه) مورد استفاده قرار گرفت. مدل نیمتغییرنمایخطی در درونیابی کریجینگ دارای کمترین مقدار خطااست. برنامهریزی بیان ژن و رگرسیون خطی بهترتیب در برآورد توزیع مکانیpHخاک، کمترین و بیشترین مقدارخطا را دارند،میزان کاهشمیانگین مربع خطا، خطای جذر میانگین مربعات، خطای جذر میانگین مربعات نسبیاز رگرسیون خطی به زمینآمار بهترتیب 67/47،58/27 و 47/26 درصد و میزان کاهششاخص پراکندگی و میانگین درصد خطای مطلق تعدیل شده از رگرسیون خطی به برنامهریزی بیان ژن بهترتیب 8/23 و 03/37 درصد است.روش ترکیبیبا استفاده از الگوریتم ژنتیک نسبت به سه نوع مدلسازی، خطای برآورد توزیع مکانی pHخاک را کاهش میدهد،بهعنوان نمونه میزان کاهشمیانگین مربع خطا، خطای جذر میانگین مربعات، خطای جذر میانگین مربعات نسبیاز مدلبرنامهریزی بیان ژن به روش ترکیبی بهترتیب برابر با 33/23،76/11 و 10 درصد است. برآوردهای pHبا روش ترکیبی در محدوده قلیایی میباشد که با مقادیر اندازهگیری شدههمخوانی دارد. کمینه و بیشینه مقدار قدر مطلق اختلاف بین مقادیر اندازهگیری و تخمینی در نقاط موردبررسی بهترتیب در بناب(09/0) و عجبشیر (25/0)میباشد.معیار میانگین درصد خطای مطلق در روش ترکیبی در محدوده قابلقبول از نظر عملکرد است و این مسئله کارایی روش مورداستفاده را در برآورد توزیع مکانی پهاشخاک نشان میدهد.

کلیدواژه‌ها

عنوان مقاله [English]

Genetic Algorithm Application for Soil pHSpatial Distribution Estimation with Geostatistics and Gene Expression ProgrammingCombination

نویسندگان [English]

Reza Ahadi ²
Fatemeh Seyyed Milani ¹

² Azarbaijan Shahid Madani University

چکیده [English]

Awareness about spatial distribution of soil properties plays an important role in the system of crop growth and yield in the agricultural field. In this regard, genetic algorithm was used to increase the accuracy in spatial distribution of soil pHwhich combines the estimates of linear regression, gene expression programming (GEP) and geostatistics (kriging interpolation) with data related to some parts of East Azerbaijan province (Bonab,Maragheh and Ajabshir). The linear model of kriging interpolation had the minimum error. Gene expression programming and linear regression had the lowest and highest error for soil pH spatial distribution estimation, respectively, for example the mean square error, root mean square error, relative root mean square errordecreasing from linear regression to geostatistics was 47.67%,27.58%, 26.47% and scatter index and adopted mean absolute percentage error decreasing from linear regression to GEP was 23.8% and 37.03%, respectively. The use of genetic algorithm in combination method reduced the error of spatial distribution compared to the tree types of models, for example mean square error, root mean square error, relative root mean square errordecreasing from GEP to combination method was 23.33%, 11.76%,10%, respectively. The estimates of soil pH with combination method are in the alkaline range, which is consistent with the obtained data. The minimum and maximum value of the absolute difference between measured and estimated data were at point in Bonab (0.09) and Ajabshir(0.25), respectively. The mean absolute percentage error of combination method was in acceptable range and this shows the efficiency of combination method for soil pH spatial distribution estimation.

کلیدواژه‌ها [English]

Spatial distribution
Genetic algorithm
Gene expression programming
Geostatstic

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تحقیقات کاربردی خاک

کاربرد الگوریتم ژنتیک در برآورد توزیع مکانی پ‌هاش خاک با ترکیب زمین‌آمار و برنامه‌ریزی بیان ژن

مراجع

مراجع

Afzali A., Varvani J., and Jafania R. 2018. Application of Geostatistical analysis for prediction of Spatial Variations in Soil Texture (Case study: Farahan Plain, Markazi province). Sarzamin, 15(58):1-16 .(In Persian)

Arbab M., Pahlavanravi A., Piri Sahragard H., and Amiri M. 2018. Accuracy Assessment of Geo-Statistics and Artificial Neural Networks Methods to Estimate Threshold Wind Velocity: A Case of Jazinak Region, Sistan Plain. Quarterly Journal of Environmental Erosion Research, 31(3):87-105. (In Persian)

Bayat Varkeshi M.,and Fasihi R. 2018. Comparison of Numerical Model, Neural intelligent and geostatistical in Estimating Groundwater Table. Journal of Geographical Sciences, 18(48):165-182. (In Persian)

Dehghani R., and Noorali A.2016. Comparison of geo-statistical methods and artificial neural network in estimating groundwater level (Case Study: Nourabad Plain,Lorestan). Journal of Environmental Science and Technology, 18(1):31-43. (In Persian)

Ebadi Y., Javdan J., and Rezaei Moghaddam M.H.2019.Estimating accuracy of Artificial Neural Networks (ANN) and Geo Statistical Methods in interpolating Groundwater Levels Case study: Shabestar-Sufian Plain.Scintific-Research Quarterly of Geographical Data,28(110):133-145. (In Persian)

Gia Pham T., Kappas M., Van Huynh C., and Nguyen L. 2019. Application of ordinary kriging and regression kriging method for soil properties mapping in hilly region of Central Vietnam. ISPRS International Journal of Geo-Information,8(3):147.

John K., Afu S.M., Isong I.A., Aki E.E., Kebonye N.M., Ayito E.O., Chapman P.A., Eyong M.O., and Penížek V. 2021. Mapping soil properties with soil-environmental covariates using geostatistics and multivariate statistics. International Journal of Environmental Science and Technology, 4:1-6.

Luan F., Si H.Z., Liu H.T., Wen Y.Y., and Zhang X.Y. 2008.Prediction of atmospheric degradation data for POPs by gene expression programming. SAR and QSAR in Environmental Research, 19(5-6):465-79.

Nadiri A., Naderi K., Asghari Moghaddam A.,and Habibi M.H.2017. Spatiotemporal Predicting of Groundwater Level using Artificial Intelligence Models and Geostatistics Model (Case Study: Duzduzan Plain). Journal of Geography and Planning, 20(58):218-301. (In Persian)

Nadiri A., SHakor S., Asghari Moghaddam A., and Vadie M. 2014.Investigation of Groundwater Nitrate Pollution with Different Interpolation Methods (Case Study: East Azarbayjan, Bilverdy Plain).HydroGemorphology, 1(1):75-92. (In Persian)

Shi W., Liu Y., Kong W., and Shen Q. 2015. Tea classification by near infrared spectroscopy with projection discriminant analysis and gene expression programming. Analytical Letters, 48(18):2833-42.

دوره 10، شماره 3 - شماره پیاپی 3
آذر 1401
صفحه 117-129

کاربرد الگوریتم ژنتیک در برآورد توزیع مکانی پ‌هاش خاک با ترکیب زمین‌آمار و برنامه‌ریزی بیان ژن

مراجع

مراجع

Afzali A., Varvani J., and Jafania R. 2018. Application of Geostatistical analysis for prediction of Spatial Variations in Soil Texture (Case study: Farahan Plain, Markazi province). Sarzamin, 15(58):1-16 .(In Persian)

Arbab M., Pahlavanravi A., Piri Sahragard H., and Amiri M. 2018. Accuracy Assessment of Geo-Statistics and Artificial Neural Networks Methods to Estimate Threshold Wind Velocity: A Case of Jazinak Region, Sistan Plain. Quarterly Journal of Environmental Erosion Research, 31(3):87-105. (In Persian)

Bayat Varkeshi M.,and Fasihi R. 2018. Comparison of Numerical Model, Neural intelligent and geostatistical in Estimating Groundwater Table. Journal of Geographical Sciences, 18(48):165-182. (In Persian)

Dehghani R., and Noorali A.2016. Comparison of geo-statistical methods and artificial neural network in estimating groundwater level (Case Study: Nourabad Plain,Lorestan). Journal of Environmental Science and Technology, 18(1):31-43. (In Persian)

Ebadi Y., Javdan J., and Rezaei Moghaddam M.H.2019.Estimating accuracy of Artificial Neural Networks (ANN) and Geo Statistical Methods in interpolating Groundwater Levels Case study: Shabestar-Sufian Plain.Scintific-Research Quarterly of Geographical Data,28(110):133-145. (In Persian)

Gia Pham T., Kappas M., Van Huynh C., and Nguyen L. 2019. Application of ordinary kriging and regression kriging method for soil properties mapping in hilly region of Central Vietnam. ISPRS International Journal of Geo-Information,8(3):147.

John K., Afu S.M., Isong I.A., Aki E.E., Kebonye N.M., Ayito E.O., Chapman P.A., Eyong M.O., and Penížek V. 2021. Mapping soil properties with soil-environmental covariates using geostatistics and multivariate statistics. International Journal of Environmental Science and Technology, 4:1-6.

Luan F., Si H.Z., Liu H.T., Wen Y.Y., and Zhang X.Y. 2008.Prediction of atmospheric degradation data for POPs by gene expression programming. SAR and QSAR in Environmental Research, 19(5-6):465-79.

Nadiri A., Naderi K., Asghari Moghaddam A.,and Habibi M.H.2017. Spatiotemporal Predicting of Groundwater Level using Artificial Intelligence Models and Geostatistics Model (Case Study: Duzduzan Plain). Journal of Geography and Planning, 20(58):218-301. (In Persian)

Nadiri A., SHakor S., Asghari Moghaddam A., and Vadie M. 2014.Investigation of Groundwater Nitrate Pollution with Different Interpolation Methods (Case Study: East Azarbayjan, Bilverdy Plain).HydroGemorphology, 1(1):75-92. (In Persian)

Shi W., Liu Y., Kong W., and Shen Q. 2015. Tea classification by near infrared spectroscopy with projection discriminant analysis and gene expression programming. Analytical Letters, 48(18):2833-42.

دوره 10، شماره 3 - شماره پیاپی 3آذر 1401صفحه 117-129

دوره 10، شماره 3 - شماره پیاپی 3
آذر 1401
صفحه 117-129