Prediction of Irrigation Water Requirements for Green Beans-Based Machine Learning Algorithm Models in Arid Region

• Published in: Water resources management Vol. 37; no. 4; pp. 1557 - 1580
• Main Authors: Mokhtar, Ali, Al-Ansari, Nadhir, El-Ssawy, Wessam, Graf, Renata, Aghelpour, Pouya, He, Hongming, Hafez, Salma M., Abuarab, Mohamed
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.03.2023; Springer Nature B.V
• Subjects: Algorithms; Arid regions; Arid zones; Artificial neural networks; Atmospheric models; Atmospheric Sciences; Beans; Civil Engineering; Climate change; Coefficients; Crops; Earth and Environmental Science; Earth Sciences; Environment; Evaporation; Evapotranspiration; Forecasting; French beans; Geotechnical Engineering & Applied Earth Sciences; Geoteknik; Humidity; Hybrid models; Hydrogeology; Hydrology/Water Resources; Irrigation; Irrigation water; Learning algorithms; Long short-term memory; Machine learning; Mathematical models; Meteorological data; Neural networks; Parameters; Precipitation; Relative humidity; Soil; Soil Mechanics; Soil temperature; Soils; Sustainable use; Temperature; Water requirements; Water resources; Water resources management; Water scarcity; Water shortages; Water use; Wind; Wind speed; Long short-term memory; Climate change; Water resources management; Hybrid models; Evapotranspiration
• ISSN: 0920-4741; 1573-1650; 1573-1650
• DOI: 10.1007/s11269-023-03443-x

Water scarcity is the most obstacle faced by irrigation water requirements, likewise, limited available meteorological data to calculate reference evapotranspiration. Consequently, the focal aims of the investigation are to assess the potential of machine learning models in forecasting irrigation water requirements (IWR) of snap beans by evolving multi-scenarios of inputs parameters to figure out the impact of meteorological, crop, and soil parameters on IWR. Six models were applied, support vector regressor (SVR), random forest (RF), deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and Hybrid CNN-LSTM. Ten variables including maximum and minimum temperature, Relative humidity, wind speed, precipitation, root depth, basal crop coefficient, soil evaporation, a fraction of surface wetted and, exposed and soil wetted fraction were used as the input data for models with their combination, 8 input scenarios were designed. Overall models, the best scenario was scenario 4 (relative humidity, wind speed, basal crop coefficient, soil evaporation), however, the best scenario for DNN and RF model was scenario 7 (root depth, basal crop coefficient, soil evaporation, fraction of surface wetted, exposed and soil wetted fraction). While the weakest one was the group of climatic factors in scenario 6 (maximum temperature, minimum temperature, relative humidity, wind speed, and precipitation). Among the models, the hybrid LTSM & CNN was the most accurate and the SVR model had the lowest estimation accuracy. The outcomes of this research work could set up a modeling strategy that would set in motion the improvement of efforts to identify the shortages in IWR forecasting, which sequentially may support alleviation strategies such as policies for sustainable water use and water resources management. The current approach was promising and has research value for other similar regions.