Norouzi, E., Behzadi, S.. (1400). The Feasibility of Machine-learning Methods to Extract the Surface Evaporation Quantity using Satellite Imagery. فناوری آموزش, 9(2), 229-238. doi: 10.22061/jecei.2021.7563.406
E. Norouzi; S. Behzadi. "The Feasibility of Machine-learning Methods to Extract the Surface Evaporation Quantity using Satellite Imagery". فناوری آموزش, 9, 2, 1400, 229-238. doi: 10.22061/jecei.2021.7563.406
Norouzi, E., Behzadi, S.. (1400). 'The Feasibility of Machine-learning Methods to Extract the Surface Evaporation Quantity using Satellite Imagery', فناوری آموزش, 9(2), pp. 229-238. doi: 10.22061/jecei.2021.7563.406
Norouzi, E., Behzadi, S.. The Feasibility of Machine-learning Methods to Extract the Surface Evaporation Quantity using Satellite Imagery. فناوری آموزش, 1400; 9(2): 229-238. doi: 10.22061/jecei.2021.7563.406
Surveying Engineering Department, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
تاریخ دریافت: 06 آبان 1399،
تاریخ بازنگری: 30 دی 1399،
تاریخ پذیرش: 01 فروردین 1400
چکیده
Background and Objectives: Climate phenomena such as quantity of surface evaporation are affected by many environmental factors and parameters, which makes modeling and data mining difficult. On the other hand, the estimation of surface evaporation for a target station can be difficult as a result of partial or complete lack of local meteorological data under many conditions. In this regard, satellite imagery can play a special role in modeling and data mining of climatic phenomena, because of their significant advantages, including availability and their potential analysis. Therefore, addressing the improvement and expansion of machine learning methods and modeling algorithms along with remote sensing data is inevitable. Methods: In this research, we intend to study the ability of 11 machine-learning modeling algorithms to model data and surface evaporation phenomena using satellite imagery. We used two methods to prepare the database: PCA and its opposite method using standard deviation and correlation. Results: The calculation of the Root Mean Squared Error (RMSE) indicated that, in general, the use of the PCA method has a better result in preparing and reducing the dimensions of large databases for all methods of machine learning. The SEGPR model was ranked first with the least error (93.49%) in the Principal Component Analysis (PCA) method, and the Artificial Neural Network (ANN) model performed well in both data preparation methods (93.42, 93.38), and the Classification-Tree-Coarse model had the highest error in both methods (92.66, 92.67). Conclusion: Consequently, it can be said that by changing the methods of database preparation in order to train models, the modeling results can be changed effectively.
