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برآورد میزان غلظت کادمیوم خاک با استفاده از مدلهای ANN و ANFIS | ||
| نشریه محیط زیست طبیعی | ||
| مقاله 3، دوره 70، شماره 3، آذر 1396، صفحه 509-523 اصل مقاله (934.43 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jne.2017.140711.1084 | ||
| نویسندگان | ||
| احمد بازوبندی1؛ صمد امامقلی زاده* 2؛ هادی قربانی2؛ تورج افشاری بدرلو1 | ||
| 1دانش آموختۀ کارشناسی ارشد خاکشناسی، گروه آب و خاک، دانشکدۀ کشاورزی، دانشگاه صنعتی شاهرود | ||
| 2دانشیار گروه آب و خاک، دانشکدۀ کشاورزی، دانشگاه صنعتی شاهرود | ||
| چکیده | ||
| بررسی سطوح آلودگی خاک به فلزات سنگین مانند میزان کادمیوم خاک برای سلامتی انسان و مدیریت محیط‌زیست انسان مهم و ضروری است. با توجه به اینکه اندازهگیری مستقیم کادمیوم خاک زمان‌بر و هزینهبر است، در این پژوهش، از دو روش هوشمند مصنوعی شامل مدل شبکۀ عصبی مصنوعی (ANN) و شبکۀ عصبی فازی تطبیقی (ANFIS) برای تخمین میزان کادمیوم خاک به‌عنوان یکی از خطرناک‌ترین فلزات سنگین استفاده شد. برای برآورد میزان غلظت کادمیوم خاک از عناصر زود یافت خاک مانند درصد سیلت، شن، کربن آلی، pH، EC، T.N و P به‌عنوان پارامترهای ورودی به مدل استفاده شد و از طریق دو مدل ANN و ANFIS ارتباط میان پارامترهای مذکور و میزان غلظت کادمیوم برقرار گردید. برای آموزش و صحت سنجی مدلهای مذکور از 250 نمونه خاک که از خاک‌های استان گیلان گرفته شد، استفاده شد. ارزیابی مدلها با استفاده از پارامترهای آماری مانند ضریب تبیین (R2)، میانگین خطای مطلق (MAE)، مجــذور میــانگین مربعــات خطــا (RMSE) انجام شد. نتایج به‌دست‌آمده نشان داد مدل شبکۀ عصبی مصنوعی با ضریب تبیین (R2) 83/0 و همچنین مجذور میانگین مربعات خطا (RMSE) 01/1 و میانگین خطای مطلق (MAE) برابر 54/0 روش مناسب‌تری نسبت به شبکۀ عصبی فازی تطبیقی است. همچنین نتایج آنالیز حساسیت پارامترهای ورودی به مدلها نشان داد درصد کربن آلی و EC خاک به ترتیب بیشترین و کمترین تأثیرگذاری را بر میزان کادمیوم دارند. مدل پیشنهادی می‌تواند برای برآورد میزان غلظت کادمیوم خاک در سایر نقاط در محدودۀ موردمطالعه که اندازهگیری غلظت کادمیوم خاک انجام نشده است و همچنین برای سایر مناطق با داشتن شرایط مشابه مورد استفاده قرار گیرد. | ||
| کلیدواژه‌ها | ||
| کادمیوم؛ فلزات سنگین؛ مدلهای هوشمند مصنوعی؛ استان گیلان | ||
| عنوان مقاله [English] | ||
| Prediction of cadmium concentration of soil using ANN and ANFIS models | ||
| نویسندگان [English] | ||
| q q1؛ q q2؛ q q2؛ q q1 | ||
| 1q | ||
| 2q | ||
| چکیده [English] | ||
| Evaluation of soil contamination by heavy metals such as cadmium in soils is essential for human health and also for environment management. As direct measurement of soil cadmium is time-consuming and costly, in this study, the two methods of artificial intelligence, artificial neural network (ANN) and adaptive fuzzy neural network (ANFIS) used to estimate the amount of cadmium in soil as one of the dangerous heavy metals. For estimating of cadmium, soil readily available properties such as clay and sand percentage, organic carbon, EC, T.N and P used as input parameters and the relationship between these parameters and the concentration of cadmium established by ANN and ANFIS models. For training and testing the models, 250 soil samples collected from soils of Guilan province. For assessment of artificial intelligence models the statistical criteria such as the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) used. The results showed that ANN model with R2 = 0.83 and RMSE= 1.01, and MAE= 0.54 is superior to ANFIS model. Also the results of the sensitivity analysis on the input variables to the model showed that organic carbon and EC have the most and the least effect on the amount of Cd. The proposed model could be used to estimate the amount of cadmium in other parts of the studied area which the concentration of cadmium has not been measured, as well as for other areas with similar conditions. | ||
| کلیدواژه‌ها [English] | ||
| Cadmium, Heavy metal, artificially intelligent models, Guilan province | ||
| مراجع | ||
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