Irrigation water infiltration modeling using machine learning

doi:10.1016/j.compag.2020.105921

Return

Irrigation water infiltration modeling using machine learning

2021-01-01

PRE

Amin Mahdavi‐Meymand

Mohammad Zounemat‐Kermani *

DOI：10.1016/j.compag.2020.105921

Original

Original request for help

Save

Abstract

En 中文

The present study proposes five standard artificial intelligence models including Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Group Method of Data Handling (GMDH), Multivariate Linear Regression (MLR), and Support Vector Regression (SVR) as well as their integrative models combined with the nature-inspired Firefly Algorithm (FA) to forecast the infiltrated water in the furrow irrigation system. The FA functions as an optimizer tool in constructing the integrative models. Data for assessing these models was gathered from published literatures and field experiments conducted on researching farm at SBUK University, Kerman, Iran. The input parameters used in modeling were furrow length (L), the inflow rate (Q), advance time at the end of the furrow (TL), cross-sectional area of inflow (A(0)) and infiltration opportunity time (T-0). The root mean square (RMSE), the mean absolute error (MAE), the correlation coefficient (R-2), the Nash-Sutcliffe efficiency index (NSE), and the index of agreement (IA) were used to evaluate the predicting performance of the suggested models. The best values of the statistical criteria and in training and testing phase of the simulation imply that the MLPNN-FA and SVR-FA provided an excellent fit to the observed data. Comparison of the results revealed that the FA is able to boost the accuracy of the models so that the improvement in RMSE values was 5%, 1%, 4%, 47% in the ANFIS, GMDH, MLPNN, and SVR, respectively. Having the calculated value for the comprehensive synthesis index (SI) indicated that using the combination of the SVR and FA noticeably enhance the performance of the standard SVR model by 97%. Even though the FA algorithm had a trivial influence on the GMDH model; however, it can be introduced as a powerful tool to enhance the complicated modeling processes.

Keywords:

Artificial intelligence

Hybrid models

Infiltration

Furrow irrigation

Firefly algorithm

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.