مدل سازی Cd خاک‌های اطراف معادن نمک گرمسار براساس مدل شبکه عصبی مصنوعی (MLP)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه محیط زیست ، دانشکده مهندسی منابع طبیعی ، دانشگاه سمنا ن، سمنان، ایرا ن

2 بخش تحقیقات آبخیزداری و بهره وری آب و خاک، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان تهران، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران

چکیده

سابقه و هدف: در طی دو دهه گذشته، مدل­سازی به کمک رایانه برای شبیه­ سازی عناصر سنگین توسعه قابل توجهی کرده است. برآورد آلودگی خاک نقش مهمی در کنترل آلودگی و مدیریت زمین دارد. اما در مناطقی با وسعت بالا، جمع آوری داده ­ها به روش مستقیم به لحاظ هزینه و زمان چالش برانگیز است. در سال­ های اخیر،کاربرد روش­ های غیر مستقیم مانند شبکه ­ی عصبی مصنوعی (ANN) و مدل­ های مشابه دیگر برای برآورد عناصر سنگین مورد توجه قرار گرفته است.در شهرستان گرمسار 27 معدن نمک وجود دارد که از این تعداد 16 معدن فعال است. نمک استخراج شده از این معادن به عنوان یکی از چاشنی­ های غذا مورد استفاده قرار می­ گیرد. از آنجا که به ­دلیل فعالیت­ های معدن کاری، ممکن است خاک­ های این منطقه آلوده به عناصر سنگین گردد. لذا در این بررسی، کارآیی شاخص ­های زمینی و طیفی برای برآورد کادمیوم (Cd)کل خاک در اطراف خاک ­های معادن نمک گرمسارتوسط مدل پرسپترون چند لایه (MLP) شبکه عصبی مصنوعی مورد ارزیابی قرار گرفت.
مواد و روش ها: برای انجام این پژوهش49 نمونه خاک مرکب از عمقcm20-0 منطقه مورد مطالعه جمع ­آوری گردید. ویژگی­ های فیزیکی و شیمیایی نمونه ­های خاک مانند درصد رس، شن، سیلت، اسیدیته خاک (pH)، هدایت الکتریکی (EC) و درصد آهک تعیین گردید. اندازه ­گیری غلظت Cd کل توسط دستگاه جذب اتمی مدل واریان (Varian-220AA) صورت گرفت. برای استخراج پارامترهای زمینی منطقه مورد مطالعه از نقشه رقومی ارتفاع (DEM) و برای محاسبه شاخص­ های طیفی، تصاویر باندهای لندست-8 با وضوح m30 استفاده شدند. 25 داده کمکی مستخرج از DEM و تصاویر لندست-8 برای برآورد غلظت Cd کل خاک منطقه مورد مطالعه استفاده گردید. داده ­های جمع آوری شده به صورت تصادفی به دو دسته آموزش و صحت­یابی تقسیم شدند و از آنها برای ارزیابی مدل MLP استفاده شد. براساس داده­ های کمکی بدست آمده و ضرایب همبستگی بین این داده ­ها با مقدار Cd برآورد شده، 2 مدل مورد ارزیابی قرار گرفت.
نتایج و بحث: نتایج این بررسی نشان داد که داده­ های کمکی مستخرج از باندهای لندست-8 (با بیشترین میزان دقت و کمترین میزان خطا) جزء تأثیرگذارترین پارامترها در برآورد آلودگی خاک به Cd بودند. براساس نتایج بدست آمده از ارزیابی عملکرد ANN در برآورد Cd کل، مقدار ریشه میانگین مربعات خطا (RMSE) و ضریب تبیین (R2) برای مدل اول 05/0 و 95/0 و برای مدل دوم 10/0 و 80/0 بدست آمد. در مدل 1، شاخص اشباع (Sat I)، شاخص اندازه ذرات (GSI)، شاخص کربنات (CrI)، شاخص رنگ خاک (Color I) و شاخص گچ (GI) جزء ویژگی­ های مهم و اصلی در مدل­سازی Cd بودند. نتایج مطالعه حاضر کارآیی بالای شبکه­ ی ANN را در برآورد Cd کل خاک نشان داد.
نتیجه ­گیری: با توجه به توسعه مدل ­های یادگیری ماشین در رشته مهندسی محیط­زیست بویژه در شبیه ­سازی عناصر سنگین، داشتن یک نقطه عطف برای پیشرفت آنها بسیار مهم است. نتایج این پژوهش نشان داد که مدل MLP برای برآوردCd  کل خاک مناسب است و می ­توان با کمک این روش در هزینه ­های نمونه ­برداری و تجزیه خاک صرفه جویی نمود. بنابراین توصیه می­ شود روش بکار رفته در این بررسی، برای تهیه نقشه Cd کل خاک در مناطق مشابه صحت سنجی شود.

کلیدواژه‌ها


عنوان مقاله [English]

Cd modeling of soils around Garmsar salt mines based on artifical neural network (MLP) model

نویسندگان [English]

  • Somayeh Moharami 1
  • Mahdi Sadeghi Pour Marvi 2
  • Rahman Sharifi 2
1 Department of Environment, Faculty of Natural Resources, Semnan University, Semnan, Iran
2 Department of Watershed Research and Water and Soil Productivity, Agricultural and Natural Resources Research and Education Center of Tehran Province, Agricultural Research, Education and Extension Organization, Tehran, Iran
چکیده [English]

Introduction: During the past two decades, computer aid models for simulation of heavy metals have been remarkably developed. Prediction of soil pollution plays an important role in pollution control and land management. But in large areas, collecting data in a direct way is challenging in terms of cost and time. In recent years, the use of indirect methods such as artificial neural network (ANN) and other similar models to estimate heavy metals has been considered. There are 27 salt mines in Garmsar city. Of these, 16 mines are active. Salt extracted from these mines are used as one of the food spices. On the other hand, due to mining activities, the soils of this region may be contaminated with heavy metals. Therefore,in this study, the effectiveness of terrain and spectral indices for predicting total soil Cadmium (Cd) around the soils of Garmsar salt mines was evaluated by ANN – multilayer perceptron (MLP) model.
Material and methods: For this research, 49 soil samples were collected from the 0-20 cm Physicochemical properties of soil samples such as percentage of clay, sand, silt, soil acidity (pH), electrical conductivity (EC) and lime percentage were determined. Total Cd concentration was measured by atomic absorption spectroscopy (AAS) (Varian, Spectra 220). All terrain attributes used in this study were derived from a digital elevation map (DEM) and to calculate the spectral indices, Landsat-8 OLI/TIRS bands image with a resolution of 30 meters were used. Twenty-five auxiliary data variables derived from a DEM and Landsat-8 were used to predict total soil Cd in the study area. Based on the auxiliary data obtained and the correlation coefficients between these data and the predicted total Cd value, 2 models were evaluated. The collected data were randomly divided into categories training and validation and were used to evaluate the MLP model.
Results and discussion: The results of this study show that the auxiliary data extracted from landsat-8 bands (with the highest accuracy and lowest error rate) were the effective parameters in predicting soil contamination with Cd. Based on the results obtained from the evaluation of ANN performance in estimating total Cd, the value of root mean square error (RMSE) and coefficient of explanation (R2) were 0.05 and 0.95 for the first model and 0.10 and 0.80 for the second model. In model 1, saturation index (Sat I), grain size index (GSI), carbonate index (CrI), soil color index (color I) and gypsum index (GI) were important and main parameters in total Cd modeling. The results of the present study showed the high efficiency of the ANN model in predicting total soil Cd.
Conclusion: Due to the development of machine learning models in the field of environmental engineering especially in simulation of heavy metals, having a turning point for their advancement is very important. The results of this research show that the MLP model is suitable for total soil Cd prediction and this method can save the cost of soil sampling and analysis. Therefore, it is recommended to validate the method applied in this study to prepare total soil Cd map in similar areas.

کلیدواژه‌ها [English]

  • Artificial neural network
  • Cadmium
  • Landsat-8
  • Soil pollution
Barati, S., Rayegani, B., Saati, M., Sharifi, A. and Nasri, M., 2011. Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. The Egyption Journal of Remote Sensing and Space Science. 14, 49–56.
Bazoobandi, A., Emamgholizadeh, S. and Ghorbani, H., 2019. Estimating the amount of cadmium and lead in the polluted soil using artificial intelligence models. European Journal of Environmental and Civil Engineering. 1-19.
Behrens, T., Förster, H., Scholten, T., Steinrüken, U., Spies, E. and Goldschmitt, M., 2005. Digital soil mapping using artificial neural networks. Journal of Plant Nutrition and Soil Science. 168, 21–33.
Boettinger, J.L., Ramsey, R.D., Bodily, J.M., Cole, N.J., Kienast-Brown, S., Nield, S.J., Saunders, A.M.and Stum, A.K., 2008. Landsat spectral data for digital soil mapping. In: Hartemink, A.E., A.B. McBratney, and M.L. Mendonca-Santos (Eds.), Digital Soil Mapping with Limited Data. Springer Science, Australia, pp. 193–203.
Bogaert, P. and D’Or, D., 2002. Estimating soil properties from thematic soil maps. Soil Science Society of America Journal. 66, 1492–1500.
Boudaghpour, S. and Malekmohammadi, S., 2020. Modeling prediction of dispersal of heavy metals in plain using neural network. Journal of Applied Water Engineering and Research. 8, 28-43.
Chu, Y., Liu, S., Cai, G. and Bian, H., 2019. Artificial neural network prediction models of heavy metal polluted soil resistivity. European Journal of Environmental and Civil Engineering. 1-21.
Cui, Z., Wang, Y., Zhao, N., Yu, R., Xu, G. and Yu, Y., 2018. Spatial distribution and risk assess- ment of heavy metals in Paddy soils of Yongshuyu irrigation area from Songhua River Basin, Northeast China. Chinese Geographical Science. 28, 797–809.

Dubrovin, T.A., Losev, A.A., Karpukhin, M.M., Vorobeichik, E.L., Dovletyarova, E.A., Vasyl, A., Brykov, V.A., Brykova, R.A., Ginocchio, R., Yáñez, C. and Neaman, A., 2021. Gypsum soil amendment in metal-polluted soils—an added environmental hazard. Chemosphere. 281, 130889.

Fei, X., Christakos, G., Xiao, R., Ren, Z.Q., Liu, Y. and Lv, X.N., 2019. Improved heavy metal mapping and contamination source apportionment in Shanghai City soils using auxiliary information. Science of the Total Environment. 661, 168–177.
Gholizadeh, A., Saberioon, M., Ben-Dor, E. and Boruvka, L., 2018. Monitoring of selected soil contaminants using proximal and remote sensing techniques: background, state-ofthe- art and future perspectives. Critical Review of Environmental Science and Technology. 48, 243–278.
Hang, X.S., Wang, H.Y., Zhou, J.M., Ma, C.L., Du, C.W. and Chen, X.Q., 2009. Risk assessment of potentially toxic element pollution in soils and rice (Oryza sativa) in a typical area of the Yangtze River Delta. Environmental Pollution. 157, 2542–2549.
Jaskulak, M., Grobelak, A. and Vandenbulcke, F., 2020. Modeling and optimizing the removal of cadmium by Sinapis alba L. from contaminated soil via response surface methodology and artificial neural networks during assisted phytoremediation with sewage sludge. International Journal of Phytoremediation. 22, 1321-1330.
Jia, Z., Li, S. and Wang, L., 2018. Assessment of soil heavy metals for eco-environment and human health in a rapidly urbanization area of the upper Yangtze Basin. Scientific Reports. 8, 3256.
Liao, K., Xu, S., Wu, J. andZhu, Q., 2013. Spatial estimation of surface soil texture using remote sensing data. Soil Science Plant Nutrition. 59(4), 488-500.
Lv, J.S., 2019. Multivariate receptor models and robust geostatistics to estimate source apportionment of heavy metals in soils. Environmental Pollution. 244, 72–83.
Lv, J.S.and Wang, Y., 2018. Multi-scale analysis of heavy metals sources in soils of Jiangsu Coast, Eastern China. Chemosphere. 212, 964–973.
Mazou, E., Alvertos, N. and Tsiros, I.X., 2013. Soil temperature prediction using time-delay neural networks. In: CG, Helmis and PT Nastos (eds.), Advances in Meteorology, Climatology and Atmospheric Physics, Springer Atmospheric Sciences, pp. 611-615.
Melendez-Pastor, I., Navarro-Pedre˜no, J., G´omez, I. and Almendro-Candel, M.B., 2011. The use of remote sensing to locate heavy metal as source of pollution. Advances in Environmental Research. 7, 217–233.
Metternicht, G.I. and Zinck, J.A., 2003. Remote sensing of soil salinity: potentials and constraints. Remote Sensing of Environment. 85, 1-20.
Minasny, B., Hopmans , J.W., Harter, T., Eching, S.O., Tuli, A. and Denton, M.A., 2004. Neural networks prediction of soil hydraulic functions for alluvia l soils using multi step out flow data. Soil Science Society of America Journal. 68, 417– 429.
Nield, S.J., Boettinger, J.L. and Ramsey, R.D., 2007. Digitally mapping gypsic and natric soil areas using landsat ETM data. Soil Science Society of America Journal. 71, 245-252.
Pan, L.B., Ma, J., Wang, X.L. and Hou, H., 2016. Heavy metals in soils from a typical county in Shanxi Province, China: levels, sources and spatial distribution. Chemosphere. 148, 248–254.
Pais, I.J. and Jones. B., 1997. The Handbook of Trace Elements. Publishing by: st. Lucie Press Boca Raton Florida.
Rosenblatt, F., 1958. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review. 65(6), 386–408.
Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D.W., 1973. Monitoring Vegetation Systems in the Great Plains with ERTS. 3rd ERTS Symposium, 10th-14th December, Washington DC. P. 309.
Rowell, D.L., 1994. Soil Science: Methods and Applications. Lingman Group, Harlow.
Sergeev, A.P., Buevich, A.G., Baglaeva, E.M. and Shichkin, A.V., 2019. Combining spatial autocorrelation with machine learning increases prediction accuracy of soil heavy metals. Catena. 174, 425-435.
Shaker R.R. and Ehlinger, T.J., 2014. Exploring non-linear relationships between landscape and aquatic ecological condition in southern Wisconsin: A GWR and ANN approach. International Journal of Applied Geospatial Research. 5(4), 1-20.
Song, W., Mu, X., Ruan, G., Gao, Z., Li, L. and Yan, G., 2017. Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method. International Journal of Applied Earth Observation and Geoinformation. 58, 168–176.
Sposito, G., Lund, L.J and Chang, A.C., 1982. Trace metal chemistry in arid zone field soils amended with sewage sludge: I. Fractionation of Ni, Cu, Zn, Cd, and Pb in soild phases. Soil Science Society of America Journal. 46, 260-264.
Srisomkiew, S., Kawahigashi, M. and Limtong, P., 2021. Digital mapping of soil chemical properties with limited data in the Thung Kula Ronghai region, Thailand. Geoderma. 389, 114942.
Taghizadeh-Mehrjardi, R., Nabiollahi, K. and Kerry, R., 2016. Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran. Geoderma. 266, 98–110.
Tan, K., Ma, W., Chen, L., Wang, H., Du, Q., Duf, P., Yan, B., Liu, R. and Li, H., 2021. Estimating the distribution trend of soil heavy metals in mining area from HyMap airborne hyperspectral imagery based on ensemble learning. Journal of Hazardous Materials. 401, 123288.
Taylor, S. 1964. Abundance of chemical elements in the continental crust: a new table. Geochimica et Cosmochimca Acta. 28, 1273-1285.
Wang, K., Zhang, C.R. and Li, W.D., 2013. Predictive mapping of soil total nitrogen at a regional scale: a comparison between geographically weighted regression and cokriging. Applied Geography. 42, 73–85.
Xiao, J., Shen, Y., Tateishi, R. and Bayaer, W., 2006. Development of topsoil grain size index for monitoring desertification in arid land using remote sensing. Remote Sensing of Environment. 27(12), 2411–2422.
Yang, Y., Christakos, G., Guo, M., Xiao, L. and Huang, W., 2017. Space-time quantitative source apportionment of soil heavy metal concentration increments. Environmental Pollution. 223, 560–566.
Yaseen, Z.M., 2021. An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions. Chemosphere. 277, 130126.
Zeissler, K.O. and Hertwig, T., 2011. Artificial Neural Network Instead of Kriging? A Case Study With Soil Contamination of Complex Sources. Landwirtschaft und Geologie, Dresden. Access 10.03.2016. http://www.beak.de/beak/sites/default/files/content/ 7_News/111_10_Oct_2011/Pribram2011_3.pdf.
Zhuo, L., Liu, Y., Wu, J. and Wang, J., 2008. Quantitative mapping of soil organic material using field spectrometer and hyperspectral remote sensing. The International Archives Photogrammetry Remote Sensing Spatial Information Science, 3th-11th July, Beijing, China. P. 901.