Machine learning and regression-based techniques for predicting sprinkler irrigation's wind drift and evaporation losses

Mohamed A. Mattar, Dilip Kumar Roy, Hussein M. Al-Ghobari and Ahmed Z. Dewidar

Agricultural Water Management, 2022, vol. 265, issue C

Abstract: Wind drift and evaporation losses (WDEL), which can occur as a result of operational and meteorological factors, are two of the most significant sprinkler-irrigation losses that can occur even in a well-managed irrigation system. A proper understanding of factors that influence WDEL in sprinkle irrigation is critical for developing water conservation strategies that significantly impact the quality and return on investment of irrigation projects. The specific objective of this research was to determine the predictive ability of five soft computing approaches (artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), multivariate adaptive regression spline (MARS), probabilistic linear regression (PLR), and support vector regression (SVR)) for predicting WDEL on a sprinkler irrigation system under design, operational, and meteorological conditions. Datasets were collected from previously published studies conducted under a variety of conditions. The results showed that the five approaches yielded statistically different WDEL predictions. The ANN model produced the most accurate WDEL predictions compared to the other models with the training and testing dataset. The ANFIS, MARS, PLR, and SVR models' performance ranks were found to be inconsistent across a variety of statistical performance criteria. Hence, Shannon's entropy-based decision theory was used to rank these models. The MARS model was ranked second (0.896), followed by the ANFIS model (0.865), the PLR model (0.833), and the SVR model (0.794). The design variable “auxiliary nozzle diameter” and climate variable “wind speed” both had high contribution ratios (17.5% and 12.19%, respectively) in WDEL modeling to produce a robust predictive model. In general, the developed models, particularly the ANN model, demonstrated a high degree of accuracy in estimating the WDEL of sprinkler irrigation systems.

Keywords: Sprinkler irrigation; Artificial neural network; Adaptive neuro-fuzzy Inference system; Multivariate adaptive regression spline; Probabilistic linear regression; Support vector regression; Evaporation and drift losses (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378377422000762
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:265:y:2022:i:c:s0378377422000762

DOI: 10.1016/j.agwat.2022.107529

Access Statistics for this article

Agricultural Water Management is currently edited by B.E. Clothier, W. Dierickx, J. Oster and D. Wichelns

More articles in Agricultural Water Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().