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

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فصلنامه علوم محیطی
دوره 21، شماره 2
1402
صفحه 153-168

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