Prediction of Potential Evapotranspiration via Machine Learning and Deep Learning for Sustainable Water Management in the Murat River Basin DOI Open Access

Ibrahim A. Hasan,

Mehmet İshak Yüce

Sustainability, Journal Year: 2024, Volume and Issue: 16(24), P. 11077 - 11077

Published: Dec. 17, 2024

Potential evapotranspiration (PET) is a significant factor contributing to water loss in hydrological systems, making it critical area of research. However, accurately calculating and measuring PET remains challenging due the limited availability comprehensive data. This study presents detailed sustainable model for predicting using Thornthwaite equation, which requires only mean monthly temperature (Tmean) latitude, with calculations performed R-Studio. A geographic information system (GIS) was employed interpolate meteorological data, ensuring coverage all sub-basins within Murat River basin, area. Additionally, Python libraries were utilized implement artificial intelligence-driven models, incorporating both machine learning deep techniques. The harnesses power intelligence (AI), applying through convolutional neural network (CNN) techniques, including support vector (SVM) random forest (RF). results demonstrate promising performance across models. For CNN, coefficient determination (R2) varied from 96.2 98.7%, squared error (MSE) ranged 0.287 0.408, root (RMSE) between 0.541 0.649. SVM, R2 94.5 95.6%, MSE 0.981 1.013, RMSE 0.990 1.014. RF showed best performance, achieving an 100%, values 0.326 0.640, corresponding 0.571 0.800. climate topography data used algorithms consistent, indicate that outperforms others. Consequently, model’s superior accuracy highlights its potential as reliable tool prediction, supporting informed decision-making resource planning. By leveraging GIS, AI, learning, this enhances modeling methodologies, addressing management challenges promoting practices face change limitations.

Language: Английский

Integrating deep learning algorithms for forecasting evapotranspiration and assessing crop water stress in agricultural water management DOI
Mahfuzur Rahman, Md Mehedi Hasan,

Md Anuwer Hossain

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 375, P. 124363 - 124363

Published: Jan. 31, 2025

Language: Английский

Citations

0
Comprehensive analysis of methods for estimating actual paddy evapotranspiration—A review DOI Creative Commons

Kiran Bala Behura,

S. K. Raul,

Jagadish Chandra Paul

et al.

Frontiers in Water, Journal Year: 2025, Volume and Issue: 7

Published: March 4, 2025

Evapotranspiration (ET) has considerable significance in the water cycle, especially farming areas where it determines crop needs, irrigation plans, and sustainable management of resources. This study stresses need for accurate ET estimation paddy fields rice is grown because its high-water sensitivity consumption which implications use efficiency food security. The attempts to address problem by estimating ET: Standard procedures such as Penman–Monteith equation, lysimeters, even remote sensing Surface Energy Balance Algorithm Land (SEBAL) Mapping at High Resolution with Internalized Calibration (METRIC) are all investigated. Furthermore, an attempt made combine data machine learning techniques refined estimation. Utilizing modernized technologies hybrid models, research investigation aims deepen understanding variability cropping systems promote improved resources agriculture practices future work suggest application vegetation indices incorporating high-resolution multi-spectral imagery accurately estimate appropriately differentiate between evaporation transpiration these complex agricultural systems.

Language: Английский

Citations

0
Prediction of Potential Evapotranspiration via Machine Learning and Deep Learning for Sustainable Water Management in the Murat River Basin DOI Open Access

Ibrahim A. Hasan,

Mehmet İshak Yüce

Sustainability, Journal Year: 2024, Volume and Issue: 16(24), P. 11077 - 11077

Published: Dec. 17, 2024

Potential evapotranspiration (PET) is a significant factor contributing to water loss in hydrological systems, making it critical area of research. However, accurately calculating and measuring PET remains challenging due the limited availability comprehensive data. This study presents detailed sustainable model for predicting using Thornthwaite equation, which requires only mean monthly temperature (Tmean) latitude, with calculations performed R-Studio. A geographic information system (GIS) was employed interpolate meteorological data, ensuring coverage all sub-basins within Murat River basin, area. Additionally, Python libraries were utilized implement artificial intelligence-driven models, incorporating both machine learning deep techniques. The harnesses power intelligence (AI), applying through convolutional neural network (CNN) techniques, including support vector (SVM) random forest (RF). results demonstrate promising performance across models. For CNN, coefficient determination (R2) varied from 96.2 98.7%, squared error (MSE) ranged 0.287 0.408, root (RMSE) between 0.541 0.649. SVM, R2 94.5 95.6%, MSE 0.981 1.013, RMSE 0.990 1.014. RF showed best performance, achieving an 100%, values 0.326 0.640, corresponding 0.571 0.800. climate topography data used algorithms consistent, indicate that outperforms others. Consequently, model’s superior accuracy highlights its potential as reliable tool prediction, supporting informed decision-making resource planning. By leveraging GIS, AI, learning, this enhances modeling methodologies, addressing management challenges promoting practices face change limitations.

Language: Английский

Citations

0