برآورد دمای خاک از داده‌های هواشناسی با استفاده از مدل‌های یادگیری ماشین سریع، شبکه عصبی مصنوعی و رگرسیون خطی چندگانه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، اهواز ، ایران

2 دانشیار، گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، ایران

چکیده

دمای خاک عامل کلیدی است که فرآیندها و خصوصیات فیزیکی، شیمیایی و بیولوژیکی خاک را کنترل می­کند؛ لذا بر کمیت و کیفیت تولید محصولات کشاورزی تأثیر می­گذارد. هدف از انجام این پژوهش برآورد دمای خاک با استفاده از پارامترهای هواشناسی به روش­های مختلف ماشین یادگیری بوده است. بدین منظور داده‌های هواشناسی و دمای خاک در عمق‌های 5، 10، 20، 30، 50 و 100 سانتی‌متری از 17 ایستگاه‌ سینوپتیک استان خوزستان مربوط به 25 سال (1994 تا 2018) گردآوری شد. مدل‌های شبکه عصبی پرسپترون چندلایه (MLPNN)، ماشین یادگیری سریع (ELM) و رگرسیون خطی چندگانه (MLR) برای برآورد دمای خاک استفاده شدند. داده‌های دمای هوا، سرعت باد، رطوبت نسبی هوا، فشار بخار، تبخیر و بارندگی به­عنوان ورودی برای آموزش مدل­ها استفاده شدند. نتایج نشان داد که تمام مدل­ها برآوردی بهتری از دمای لایه سطحی خاک (عمق 30-0 سانتی­متری) داشته و با افزایش عمق دقت­آن­ها کاهش می­یابد، به­طوری­که بهترین دقت برآورد دمای خاک مربوط به عمق 5 و کمترین آن مربوط به عمق 100 سانتی‌متری بود. نتایج نشان داد مدل­های MLR، MLPNN و ELM عملکردی مطلوب در مدل­سازی دمای خاک در تمام عمق­ها داشتند. برای مدل­های MLR، MLPNN، ELM به ترتیب مقادیر R2 از 864/0-700/0، 997/0-967/0 و 996/0-967/0، مقادیر RMSE از 823/2-557/2، 072/0-034/0 و 078/0-028/0 درجه سلسیوس و مقادیر MAE از 529/1-398/1، 063/0-023/0 و 065/0-023/0 درجه سلسیوس بود. نتایج نشان داد که دو مدل MLPNN و ELM دارای عملکردی تقریبا مشابه و بهتر از مدل رگرسیون خطی چندگانه بودند؛ اما به دلیل سرعت محاسبات بسیار بالای مدل ELM، پیشنهاد می­گردد از مدل MLPNN برای تخمین دمای نیمرخ خاک استفاده شود.

کلیدواژه‌ها


عنوان مقاله [English]

Estimating Soil Temperature from Metrological Data Using Extreme Learning Machine, Artificial Neural Network and Multiple Linear Regression Models

نویسندگان [English]

  • Hossein Zadmehr 1
  • Ahmad Farrokhian Firouzi 2
1 Soil Science and Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 Associate Professor, Department of soil science, Faculty of Agriculture , Shahid Chamran University of Ahvaz, Iran
چکیده [English]

Soil temperature (Ts) is a key factor controlling the soil physical, chemical and biological properties and processes and consequently affects agricultural crop productions. The objective of this study was to estimate Ts from meteorological data using different machine learning methods. For this purpose, meteorological data and soil temperature at different depths (5, 10, 20, 30, 50 and 100 cm from the soil surface) for 25 years (1994-2018) were collected from 17 synoptic stations in Khuzestan province, Iran. Air temperature, wind speed, relative humidity, evaporation, precipitation, and vapor pressure were used as inputs to train the models. Multiple Linear Regression (MLR), Multilayer Perceptron Neural Network (MLPNN) and Extreme Learning Machine (ELM) models were used to predict soil temperature from metrological data. The results indicated that all models predicted temperature of the top layer (0-30 cm) better than the ones in sublayers. on the other hand, by increasing soil depth the accuracy of the models diminished; so that, the best and worst Ts predictions were belong to 5 cm and 100 cm depth, respectively. The results revealed that MLR, MLPNN and ELM models provided desirable performance in modeling Ts at all depths, with R2 values ranging 0.700-0.864, 0.967-0.997, and 0.967-99, RMSE values ranging 2.557–2.873, 0.034–0.072, and 0.028–0.078 °C, and MAE values ranging 1.398–1.529, 0.023–0.063, and 0.023–0.065 °C, respectively. Overall, the results showed that MLPNN and ELM models had approximately similar performance and better accuracy than MLR model. However, because of the high computational speed of the ELM model, it is recommended to use MLPNN model for estimation of soil profile Ts.

کلیدواژه‌ها [English]

  • Khuzestan Province
  • meteorological parameters
  • Multiple Linear Regression
  • Multilayer Perceptron Neural Network
  • Extreme learning machine
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