Federated learning based reference evapotranspiration estimation for distributed crop fields
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.Federated learning enhances Reference Evapotranspiration (ETo) estimation across diverse locations. The Random Forest Regressor model achieved superior accuracy, outperforming local models for sustainable agriculture and water resource management.
Area Of Science
- Environmental Science
- Agricultural Science
- Data Science
Background
- Accurate Reference Evapotranspiration (ETo) is crucial for water resource management and sustainable agriculture.
- Existing machine learning models for ETo estimation are often limited to specific geographical areas.
- Centralized data approaches face challenges with privacy and data transfer limitations.
Purpose Of The Study
- To propose a federated learning approach for estimating ETo across multiple locations with diverse weather conditions.
- To evaluate the performance of different machine learning models (RFR, SVR, DTR) in a federated learning framework for ETo prediction.
- To identify the key weather parameters influencing ETo estimation through feature importance analysis.
Main Methods
- Implemented a federated learning approach using weather data from three distinct geographical locations in Pakistan (2012-2022).
- Trained and evaluated Random Forest Regressor (RFR), Support Vector Regressor (SVR), and Decision Tree Regressor (DTR) models locally and globally.
- Conducted feature importance analysis to understand the impact of weather parameters on model performance.
Main Results
- The federated learning model based on Random Forest Regressor (RFR) demonstrated superior performance, achieving R2 = 0.97%, RMSE = 0.44, MAE = 0.33 mm day-1, and MAPE = 8.18%.
- The RFR federated model outperformed individual local machine learning models at each selected site.
- Maximum temperature and wind speed were identified as the most influential factors for ETo prediction.
Conclusions
- Federated learning offers a generalized and privacy-preserving method for accurate ETo estimation across varied climatic conditions.
- The RFR model within a federated learning framework is highly effective for regional ETo prediction.
- Understanding feature importance aids in refining ETo estimation models for better water resource and agricultural management.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.
In land plants, the uppermost cell layer of a plant leaf, called the epidermis, is coated with a waxy substance called the cuticle. This hydrophobic layer is composed of the polymer cutin and...
Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.
Under normal conditions, water taken up by the plant evaporates from leaves and other parts in a process called transpiration. In times of drought stress, water that evaporates by transpiration far exceeds the water absorbed from the soil, causing plants to wilt. The general plant response to drought stress is the synthesis of hormone...
The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...