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برآورد عیار پتاسیم شورابه زیرزمینی با استفاده از تصاویر ماهواره‌ای سنتینل و الگوریتم جنگل تصادفی (مطالعه موردی پلایای خور و بیابانک، استان اصفهان) | ||
| تحقیقات آب و خاک ایران | ||
| دوره 55، شماره 6، شهریور 1403، صفحه 869-888 اصل مقاله (2.73 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2024.372983.669669 | ||
| نویسندگان | ||
| مریم ایرجی* 1؛ سید علیرضا موحدی نائینی1؛ چوقی بایرام کمکی2؛ سهیلا ابراهیمی1؛ بامشاد یغمایی3 | ||
| 1گروه علوم و مهندسی خاک، دانشکده مهندسی آب و خاک، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران. | ||
| 2گروه مدیریت مناطق بیابانی، دانشکده مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران. | ||
| 3گروه باستان شناسی، دانشکده علوم انسانی, موسسه آموزش عالی معماری و هنر پارس، تهران، ایران. | ||
| چکیده | ||
| یکی از عناصر پرمصرف که نقش مهمی در تولید پایدار کشاورزی دارد، پتاسیم است. پتاسیم خاک سطحی در پلایا از پتاسیم موجود در آب زیرزمینی نشات می گیرد و در نتیجه، بین پتاسیم خاک سطحی و عیار پتاسیم شورابه زیرزمینی همبستگی وجود دارد. هدف این پژوهش، استفاده ترکیبی از الگوریتم جنگل تصادفی (RF) و تصویر ماهواره‌ای برای یافتن ارتباط بین پتاسیم سطحی خاک و شاخص‌های سنجش‌ازدور تعریفی مختص این مطالعه به‌منظور پیش‌بینی عیار پتاسیم شورابه زیرزمینی در پلایای خور و بیابانک استان اصفهان است. بدین منظور تعداد 60 نمونه خاک از لایه 5-0 سانتی‌متری جهت اندازه‌گیری پتاسیم لایه سطحی (متغیر وابسته) نمونه‌برداری شد. به‌منظور تعیین مختصات نمونه‌گیری‌ها از روش ابر مکعب لاتین استفاده شد. همچنین 12 گمانه جهت استخراج و اندازه‌گیری عیار پتاسیم شورابه زیرزمینی حفر شد. از 12 باند ماهواره سنتینل 2 و چهار عمل اصلی ریاضی برای تعریف شاخص (متغیرهای مستقل) به‌منظور مدل‌سازی پتاسیم لایه سطحی و درنهایت برآورد عیار پتاسیم شورابه زیرزمینی استفاده شد. داده‌ها به دو دسته 70 درصد برای واسنجی (آموزش) و 30 درصد برای اعتبار سنجی (آزمون) دسته‌بندی شده و با الگوریتم RF در محیط Google Colab و با استفاده از زبان برنامه‌نویسی پایتون مدل‌سازی شدند. نتایج این الگوریتم با شاخص‌های آماری R2، MSE، RMSE و MAE به ترتیب 51/0، 0179/0، 1338/0 و 1130/0 به دست آمد. نتایج این پژوهش تائید کننده کارایی داده‌های سنجش‌ازدور و الگوریتم یادگیری ماشین در پیش‌بینی عیار پتاسیم شورابه زیرزمینی است. | ||
| کلیدواژه‌ها | ||
| واژه‌های کلیدی: پایتون؛ سنجش از دور؛ کفه‌های نمکی؛ مدل‌سازی؛ یادگیری ماشین | ||
| عنوان مقاله [English] | ||
| Estimating the potassium grade of saline underground water using Sentinel satellite images and random forest algorithm (case study of Khoor and Biabank playa, Isfahan province) | ||
| نویسندگان [English] | ||
| maryam iraji1؛ Seyed Alireza Movahedi naeini1؛ Chooghi Bayram Komaki2؛ Soheila Ebrahimi1؛ Bamshad Yaghmaei3 | ||
| 1Department of Soil Science and Engineering, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. | ||
| 2Department of Desert Management, Faculty of Pasture and Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. | ||
| 3Department of Archaeology, Faculty of Humanities, Higher Education Institute of Architecture and Arts, Tehran, Iran. | ||
| چکیده [English] | ||
| One of the widely used elements that plays an important role in sustainable agricultural production is potassium.The potassium in the surface soil of the playa originates from the potassium present in the underground water. As a result, there is a correlation between the surface soil potassium and the potassium grade of the groundwater. The aim of this research is to utilize a combination of the random forest (RF) algorithm and satellite imagery to establish the relationship between soil surface potassium and remote sensing indicators. This will enable the prediction of the potassium grade of the underground in Khoor and Biabank playa in Isfahan province. For this purpose, 60 soil samples were taken from the 0-5 cm layer to measure potassium in the surface layer(dependent variable). In order to determine the sampling coordinates, the Latin supercube method was used. Twelve boreholes were drilled to extract and measure the potassium grade of underground saline water. The 12 bands of the Sentinel-2 satellite and four main mathematical operations were used to define the index (independent variables) to model the potassium content of the surface soil layer and ultimately estimate the rate of potassium grade in the underground saline water. The data were categorized into two groups: 70% for calibration (training) and 30% for validation (testing). The data were modeled using the RF algorithm in the Google Colab environment and implemented with the Python programming language. The results of this algorithm were obtained with R2, MSE, RMSE and MAE statistical indices of 0.51, 0.0179, 0.1338 and 0.1130 respectively. The results of this research confirm the effectiveness of remote sensing data and machine learning algorithms in predicting the potassium grade of saline groundwater. | ||
| کلیدواژه‌ها [English] | ||
| Keywords: Python, Remote sensing, Salt pans, Modeling, Machine learning | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 152 تعداد دریافت فایل اصل مقاله: 107 |
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