Performance and Robustness Analysis of Advanced Machine Learning Models for Predicting the Required Irrigation Water Amount
Year: 2024
Type: article
Abstract: The agricultural sector plays a pivotal role in ensuring global food security, particularly in light of significant populationgrowth. The demand for food is increasing substantially, while crop production may not sufficiently meet these rising needs. Waterscarcity is one of the main problems that poses a significant challenge to the agriculture sector, exacerbated by inefficiencies intraditional irrigation methods. Accurate prediction of plant water requirements is essential to address this issue. This paper proposesadvanced machine learning (ML) and deep learning (DL) models to accurately predict the daily water amount (quantity) needs ofgreenhouse plants using various air and soil data parameters. Various data preprocessing techniques were applied to prepare the datafor the proposed models. In addition, due to the different nature of the proposed models, two different data splitting methods were usedto split data into inputs and outputs (Simple data preparation for the ML models and time series data preparation for the time series DLmodels).Results indicate that the Multi-Layer Perceptron (MLP) model consistently outperformed other models, demonstrating superiorstability and efficiency across different data optimization phases. Additionally, both ML and Long-Short Term Memory (LSTM) modelsexhibited strong performance in different data optimization scenarios. Robustness was evaluated through parameter sensitivity analysis,which revealed that ML models were generally more robust than DL models. This robustness is attributed to the limited number ofparameters in ML models, which enhances their reliability compared to the more complex DL models. This study ensures the potential ofthe proposed models to optimize the irrigation practices, thereby addressing water scarcity issues and improving agricultural productivity.
Authors: Hamed Laouz, Soheyb Ayad, Labib Sadek Terrissa, Aicha Nabila Benharkat, Samir Merdaci
Institution: University of Eloued
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4399605147