ارزیابی تکنیک سنجش از دور و مدل های یادگیری ماشین در برآورد تبخیروتعرق گیاه نیشکر

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه آبیاری و زهکشی، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران.

2 گروه سنجش از دور محیطی و ژئوماتیک، مرکز آب، زمین و محیط زیست، INRS -کبک، کانادا.

10.22059/jwim.2023.362473.1090

چکیده

تخمین تبخیروتعرق گیاه در مناطق خشک و نیمه­خشک چالش برانگیز است زیرا این فرایند در طول زمان و مکان بسیار پویا است. هم‌چنین اندازه­گیری این متغیر به‌صورت میدانی کاری بسیار وقت­گیر و هزینه­بر است. لذا این پژوهش با هدف ایجاد چارچوبی برای برآورد بهینه تبخیروتعرق گیاه نیشکر در مقیاس مکانی- زمانی با استفاده از چهار مدل یادگیری ماشین (MLR، CART، SVR و GBRT) در ترکیب با داده­های سنجش از دور و متغیر­های هواشناسی صورت گرفت. هم‌چنین به‌منظور کاهش وابستگی به پارامترهای متعدد هواشناسی در روش­های مرسوم برآورد تبخیروتعرق، هشت مدل مختلف تجربی مبتنی بر دما و چهار مدل اصلاحی هارگریوز سامانی نسبت به مدل استاندارد فائو- پنمن- مانتیث ارزیابی شد. بدین منظور داده­های هواشناسی از ایستگاه هواشناسی کشت و صنعت نیشکر حکیم فارابی در دوره زمانی سه ساله (1400-1397) گردآوری شدند. نُه ترکیب مختلف از متغیرهای ورودی (داده­های سنجش از دور و متغیر­های هواشناسی) براساس روش Information Gain Ratio طراحی شدند و سپس توسط الگوریتم­های یادگیری ماشین ارزیابی شدند. نتایج نشان داد که بیش‌ترین دقت مدل‌های یادگیری ماشین براساس آماره‌هایR2 ، RMSE و MAE به‌ترتیب در مدل‌های CART (99/0، 41/0 و 18/0) و GBRT (99/0، 65/0 و 26/0) به‌دست آمد. هم‌چنین از بین روش­های تجربی مبتنی بر دما، روش ایوانف با R2 برابر 91/0 و روش بایر رابرتسون با R2 برابر 78/0 به‌ترتیب بهترین و ضعیف­ترین عملکرد را ثبت کردند. به‌طورکلی روش سنجش از دور در ترکیب با مدل­های یادگیری ماشین توانست مقادیر بهتر و دقیق­تری از تبخیروتعرق گیاه را در مقیاس زمان و مکان ارائه نماید.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluating Remote Sensing Technique and Machine Learning Algorithms in Estimating Sugarcane Evapotranspiration

نویسندگان [English]

  • Mohammad Alavi 1
  • Mohammad Albaji 1
  • Mona Golabi 1
  • Abd Ali Naseri 1
  • Saeid Homayouni 2
1 Department of Irrigation and Drainage, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
2 Centre Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), 490 Couronne St, Quebec, QC G1K 9A9, Canada.
چکیده [English]

Estimating crop evapotranspiration (ETc) in arid and semi-arid areas can be difficult due to the dynamic nature of this process across both time and space. In addition, obtaining on-site measurements for this variable can be very time-consuming and costly. This study aimed to develop a framework that accurately estimates the sugarcane crop evapotranspiration on a spatio-temporal scale. This was achieved using four machine learning (ML) algorithms (MLR, CART, SVR, and GBRT) combined with remote sensing (RS) data and meteorological variables. Also, to reduce the dependence on several meteorological parameters in conventional ETc equations, the performance of eight different experimental temperature-based methods and four modified Hargreaves & Samani equations was evaluated compared to the standard FAO-Penman-Monteith method. For this purpose, weather data were collected from Hakim Farabi Sugarcane Agro-Industrial meteorological station for three years (2018-2021). Nine combinations of input variables (RS data and meteorological variables) were designed based on the IGR method and then evaluated by the ML algorithms. The results showed that the highest accuracy of ML algorithms based on R2, RMSE, and MAE statistics was obtained in CART (0.99, 0.41, and 0.18) and GBRT algorithms (0.99, 0.65, and 0.26), respectively. Regarding temperature-based methods, Ivanov’s equation had the best performance with an R2 of 0.91, while Baier and Robertson’s equation had the weakest performance with an R2 of 0.78 when estimating ETc. Overall, the combination of RS and ML algorithms effectively produced more precise and reliable ETc values on both temporal and spatial scales.

کلیدواژه‌ها [English]

  • Decision tree
  • Experimental models
  • Gradient boosted regression tree
  • Spectral indices
  • Support vector machine

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مدیریت آب و آبیاری
دوره 13، شماره 4
دی 1402
صفحه 965-982

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