Transfer learning augmented enhanced memory network models for reference evapotranspiration estimation

Bedi, Jatin

doi:10.1016/j.knosys.2021.107717

Cited by 16 publications

(5 citation statements)

References 43 publications

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“…The WCA is a population-based optimization algorithm that simulates the natural water cycle stages such as precipitation, evaporation and infiltration, to optimize a given problem (Naseri et al , 2020). Transfer learning can help improve the performance of models, particularly when the new data set is small or when data is limited (Bedi, 2022). This ML technique applies knowledge gained from solving one problem to a different but related problem, wherein a pre-trained model is used as a starting point and then fine-tuned on the new task (Bedi, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Abdella et al (2020) recommended the use of K-means clustering and logistic regression to investigate the impact of food consumption in the USA. Bedi (2022) introduced a novel approach, referred to as the transfer learning augmented enhanced memory network, to enhance the accuracy of estimating reference evapotranspiration, which is a crucial parameter in hydrological and agricultural studies as it quantifies the amount of water that evaporates from a reference crop surface. El Hathat et al ( 2023) conducted a study on greenhouse gas emissions in tomato production in Morocco.…”

Section: Thematic Areasmentioning

confidence: 99%

“…Transfer learning can help improve the performance of models, particularly when the new data set is small or when data is limited (Bedi, 2022). This ML technique applies knowledge gained from solving one problem to a different but related problem, wherein a pre-trained model is used as a starting point and then fine-tuned on the new task (Bedi, 2022).…”

Section: Leveraging Technology For a Greener Futurementioning

confidence: 99%

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Machine learning for sustainable development: leveraging technology for a greener future

Kagzi,

Khanra,

Paul

2023

JSIT

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Purpose From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries. Design/methodology/approach This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development. Findings ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals. Originality/value This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

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Section: Discussionmentioning

confidence: 99%

Section: Thematic Areasmentioning

confidence: 99%

Section: Leveraging Technology For a Greener Futurementioning

confidence: 99%

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Machine learning for sustainable development: leveraging technology for a greener future

Kagzi,

Khanra,

Paul

2023

JSIT

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“…All meteorological data from the automatic weather station were degraded (Raj et al, 2022;Lu et al, 2015;Fang et al, 2021;Alavi et al, 2006;Bedi, 2022;Yu et al, 2022) to mimic possible errors inherent to low-cost equipment. This degradation comprised the following: 1.…”

Section: Simulationsmentioning

confidence: 99%

Low-Cost Irrigation Management System: Improving Data Confidence Through Artificial Intelligence

Cruz

Marques

2023

Eng. Agríc.

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A common scenario in the developing countries is the low income and less education of producers. Thus, the tools used for irrigation management must be cheap and easy to handle. In this work, an autonomous and low-cost network of micro-weather stations has been developed for irrigation management. Simulations were performed to evaluate the ability of intelligent systems to compute evapotranspiration with noisy and insufficient data. The network of micro-weather stations was then applied to autonomous irrigation management of a crop of bell peppers. Statistical analysis was performed on data from the developed system and a standard weather station. The results show no statistical difference between the values of evapotranspiration calculated with data from these two sources. The developed system performed with a coefficient of determination of 0.968, mean absolute error of 0.055 mm day −1 , and root mean square error of 0.063 mm day −1 . The study shows that low-cost intelligent systems can be used as viable tools for efficient irrigation management.

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“…As a branch of data-driven models, deep learning models can better address the insufficient ability of classical data-driven models to deal with nonlinear relationships in difficult situations. Recently, with the rapid development of deep learning models, it has been increasingly studied in the simulation and prediction of hydrological elements such as runoff, evapotranspiration, and soil moisture [26][27][28]. For instance, Castangia et al [29] explored the applicability of the transformer model to flood forecasting and found that the model has higher prediction accuracy than recurrent neural networks.…”

Section: Introductionmentioning

confidence: 99%

Runoff Prediction in Different Forecast Periods via a Hybrid Machine Learning Model for Ganjiang River Basin, China

Wang,

Tang,

Zou

et al. 2024

Water

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Accurate forecasting of monthly runoff is essential for efficient management, allocation, and utilization of water resources. To improve the prediction accuracy of monthly runoff, the long and short memory neural networks (LSTM) coupled with variational mode decomposition (VMD) and principal component analysis (PCA), namely VMD-PCA-LSTM, was developed and applied at the Waizhou station in the Ganjiang River Basin. The process begins with identifying the main forecasting factors from 130 atmospheric circulation indexes using the PCA method and extracting the stationary components from the original monthly runoff series using the VMD method. Then, the correlation coefficient method is used to determine the lag of the above factors. Lastly, the monthly runoff is simulated by combining the stationary components and key forecasting factors via the LSTM model. Results show that the VMD-PCA-LSTM model effectively addresses the issue of low prediction accuracy at high flows caused by a limited number of samples. Compared to the single LSTM and VMD-LSTM models, this comprehensive approach significantly enhances the model’s predictive accuracy, particularly during the flood season.

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Transfer learning augmented enhanced memory network models for reference evapotranspiration estimation

Cited by 16 publications

References 43 publications

Machine learning for sustainable development: leveraging technology for a greener future

Machine learning for sustainable development: leveraging technology for a greener future

Low-Cost Irrigation Management System: Improving Data Confidence Through Artificial Intelligence

Runoff Prediction in Different Forecast Periods via a Hybrid Machine Learning Model for Ganjiang River Basin, China

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