Comparative Study of SVR, Regression and ANN Water Surface Forecasting for Smart Agriculture


  • Arief Andy Soebroto Universitas Brawijaya
  • Imam Cholissodin Universitas Brawijaya
  • Destyana Ellingga Pratiwi Universitas Brawijaya
  • Guruh Prayogi Willis Putra Universitas Brawijaya



forecasting, smart, agriculture, system, water


In the smart agriculture system based on green-based technology of artificial intelligence (AI), flooding can be predicted early by forecasting the water surface and good agricultural irrigation. The process of rising and falling of the water surface in a water basin area can be explained theoretically, but since there are many related variables and the complexity of dependencies between variables, the mathematical model is difficult to construct. Forecasting water surface in the field of irrigation needs too many variable parameters, such as cross-sectional area, depth, volume of rivers and so on. Based on patterns in each period, forecasting can be done using a statistical method and AI. This study uses the support vector regression (SVR) method, regression, multiple linear regression, and algorithm backpropagation, all compared to one another. The results of tests carried out between SVR and multiple linear regression show that SVR is superior. This can be seen from the result of the mean square error (MSE) obtained for each method. SVR 0.03 and for multiple linear regression, 0.05. The result is also supported by the best MSE result in the regression method, which is 0.338, and the best MSE value in artificial neural network (ANN), which is 0.428.


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2022-04-19 — Updated on 2022-04-21


How to Cite

Soebroto, A. A., Cholissodin, I., Pratiwi, D. E., & Willis Putra, G. P. (2022). Comparative Study of SVR, Regression and ANN Water Surface Forecasting for Smart Agriculture. HABITAT, 33(1), 86–92. (Original work published April 19, 2022)