Version 1
: Received: 13 April 2024 / Approved: 17 April 2024 / Online: 17 April 2024 (09:05:07 CEST)
How to cite:
Guevara Rodriguez, G. The Behavior of the Travel Times in Drip Irrigation, Using Supervised Machine Learning and Optimizing Methods by Python. Preprints2024, 2024040955. https://doi.org/10.20944/preprints202404.0955.v1
Guevara Rodriguez, G. The Behavior of the Travel Times in Drip Irrigation, Using Supervised Machine Learning and Optimizing Methods by Python. Preprints 2024, 2024040955. https://doi.org/10.20944/preprints202404.0955.v1
Guevara Rodriguez, G. The Behavior of the Travel Times in Drip Irrigation, Using Supervised Machine Learning and Optimizing Methods by Python. Preprints2024, 2024040955. https://doi.org/10.20944/preprints202404.0955.v1
APA Style
Guevara Rodriguez, G. (2024). The Behavior of the Travel Times in Drip Irrigation, Using Supervised Machine Learning and Optimizing Methods by Python. Preprints. https://doi.org/10.20944/preprints202404.0955.v1
Chicago/Turabian Style
Guevara Rodriguez, G. 2024 "The Behavior of the Travel Times in Drip Irrigation, Using Supervised Machine Learning and Optimizing Methods by Python" Preprints. https://doi.org/10.20944/preprints202404.0955.v1
Abstract
The importance of knowing the advance time in drip irrigation lines lies in its precision, as this influences the proper management of water and nutrients delivery. The mathematical calculation to determine the advance time is based on the general hydraulic flow equation, which relates the fluid velocity and the cross-sectional area of the pipe to the volume of water flowing through it. However, in irrigation pipes, where the flow is a mixture of dripper properties, its pressure, the approach to solving the advance time is different, as it is the sum of individual advance times for each segment. To address this issue, Python 3.11 was used along with various libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn to create a modeling environment and run algorithms that could predict outcomes. A program was developed that calculates the advance time for each segment of the drip line using partial velocities, and a dataset was generated that was used to train and test machine learning models. Several machine learning algorithms such as Linear Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, and Random Forests were implemented to predict the advance time. Additionally, SciPy optimize was used to obtain multivariable equations that describe the advance time in drip irrigation lines. Results showed that the dripper flow has the greatest influence on the advance time, followed by the diameter and distance. Decision Tree and SVM models had the best accuracy with scores above 98%. Equations were found to calculate the advance time in the complete drip line and in 95% of its length, with coefficients of determination close to 99.33%. This study demonstrated the importance of understanding the relationship between dripper parameters and travel time in drip irrigation, as well as the utility of machine learning and optimization tools for predicting and modeling this phenomenon.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
