Data-driven reference evapotranspiration (ET0) estimation: a comparative study of regression and machine learning techniques

Rajput, Jitendra; Singh, Man; Lal, K.; Khanna, Manoj; Sarangi, A.; Mukherjee, J.; Singh, Shrawan

doi:10.1007/s10668-023-03978-4

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Preprint1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Comparative analysis of machine learning models for rainfall prediction

Das,

Sahu,

Swain

2024

Journal of Atmospheric and Solar-Terrestrial Physics

View full text Add to dashboard Cite

Comparative analysis of machine learning models for rainfall prediction

Das,

Sahu,

Swain

2024

Journal of Atmospheric and Solar-Terrestrial Physics

View full text Add to dashboard Cite

Calibration and evaluation of various reference evapotranspiration estimation methods in a humid subtropical climate: A case study in Samsun Province, Türkiye

Gharehbaghi,

Afaridegan,

Kaya

et al. 2024

Physics and Chemistry of the Earth, Parts A/B/C

View full text Add to dashboard Cite

Smart irrigation for coriander plant: Saving water with AI and IoT

Paria,

Giri,

Dutta

et al. 2024

Preprint

View full text Add to dashboard Cite

Accurate forecasting of water requirements is crucial for optimizing irrigation and water preservation. However, the Food and Agriculture Organization(FAO Irrigation and Drainage paper 56) Penman-Monteith(PM) model is observed as the highest quality method for evapotranspiration (EVT0 ) forecasting. However, using the PM model is often restricted by the need for predicted climatic factors, particularly solar radiation. This research article presents a real-time intelligent watering system for coriander plants that can be monitored using smartphones. The system uses a hybrid machine-learning technique and Internet of Things (IoT) sensors to sense weather circumstances directly from the crop field. Nine distinct hybrid neural network models ((HML1, HML2 …, HML9)) are developed to predict water requirements using climate and environmental variables. These models are optimized using a genetic algorithm to achieve optimal efficiency. The EVT0 forecasts of the proposed approach are being compared against the standard FAO56 Penman-Monteith technique. An in-depth analysis of the highly successful HML4 model is conducted, and the findings are used in a developed Android application that enables real-time monitoring. In addition, the most favourable parameters are determined to achieve even more improved outcomes. This intelligent irrigation system can significantly minimize flood irrigation, water consumption, and labour expenses.

show abstract

Data-driven reference evapotranspiration (ET0) estimation: a comparative study of regression and machine learning techniques

Cited by 3 publications

References 54 publications

Comparative analysis of machine learning models for rainfall prediction

Comparative analysis of machine learning models for rainfall prediction

Calibration and evaluation of various reference evapotranspiration estimation methods in a humid subtropical climate: A case study in Samsun Province, Türkiye

Smart irrigation for coriander plant: Saving water with AI and IoT

Contact Info

Product

Resources

About