Prediction model for daily reference crop evapotranspiration based on hybrid algorithm and principal components analysis in Southwest China

Zhao, Long; Zhao, Xinbo; Zhou, Hanmi; Wang, Xianlong; Xing, Xuguang

doi:10.1016/j.compag.2021.106424

Cited by 31 publications

(7 citation statements)

References 40 publications

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“…Based on the above calculation results, the abnormal data of Sanya tourist attractions in different seasons will be removed, and then the principal component analysis method [9][10][11] will be used to denoise the data in Sanya tourist attractions in different seasons. Principal component analysis (PCA) is a commonly used data denoising method, which transforms the original data into a new coordinate system by linear transformation, so that the largest variance appears on the first coordinate axis, the second largest variance appears on the second coordinate axis, and so on.…”

Section: A Sanya Tourist Attractions Data Noise Reduction In Differen...mentioning

confidence: 99%

Research on Spatial Accessibility Measurement Algorithm for Sanya Tourist Attractions Based on Seasonal Factor Adjustment Analysis

Mao,

Zhuang

2024

IJACSA

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Seasonal factors will lead to changes in tourists' demand for scenic spots in different seasons, which will affect the traffic network and road conditions, and then affect the convenience and efficiency of tourists arriving at scenic spots. Based on the adjustment and analysis of seasonal factors, this study puts forward an algorithm for measuring the spatial accessibility of Sanya tourist attractions. Principal component analysis is used to denoise the data of Sanya tourist attractions in different seasons, and independent component analysis is used to extract the data characteristics of Sanya tourist attractions in different seasons after denoising. On this basis, the spatial accessibility index of Sanya tourist attractions is calculated by combining the spatial information of Sanya tourist attractions and GIS technology, and the spatial accessibility of Sanya tourist attractions is analyzed, and the spatial accessibility measurement model of Sanya tourist attractions is constructed to realize the spatial accessibility measurement of Sanya tourist attractions. The experimental results show that the spatial accessibility measurement method of Sanya tourist attractions is effective, which can effectively improve the accuracy of accessibility measurement and shorten the accessibility measurement time. It aims to help decision makers plan and optimize tourist routes and improve the efficiency and convenience of tourists arriving at their destinations.

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Section: A Sanya Tourist Attractions Data Noise Reduction In Differen...mentioning

confidence: 99%

Research on Spatial Accessibility Measurement Algorithm for Sanya Tourist Attractions Based on Seasonal Factor Adjustment Analysis

Mao,

Zhuang

2024

IJACSA

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“…Collaborative efforts between researchers, policymakers, and local farmers are essential to tailor machine learning solutions to the specific challenges faced by Assam's agriculture sector [16] [25]. This may involve developing customized models that account for regional variations in climate, soil types, and crop preferences [27][28][29][30][31]. In summary, harnessing machine learning techniques in Assam's agriculture sector has the potential to boost economic growth by enhancing productivity, optimizing resource utilization, and fostering sustainable farming practices.…”

Section: Machine Learning In Agricultural Growthmentioning

confidence: 99%

Recent Trends of Machine Learning Techniques on The Growth of Agricultural Sector of Assam

Borthakur,

Sahoo,

Mahanta

et al. 2023

ijmst

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The agricultural sector in Assam, India, holds immense potential for economic growth and rural development. However, harnessing this potential requires tackling challenges like low productivity, resource scarcity, and climate change. Machine learning (ML) emerges as a promising tool to address these hurdles and transform Assam’s agricultural sector. This review investigates the potential of machine learning (ML) techniques in driving agricultural growth within the specific context of the Assam’s economy. Utilizing comprehensive search within Scopus and Web of Science databases from 2015 to 2023, and following the PRISMA guidelines, we analyzed 37 relevant articles. Our examination focuses on the multifaceted applications of ML across various agricultural domains in Assam, encompassing crop yield prediction, soil health analysis and economic growth. The review highlights successful ML- driven interventions in Assam’s agricultural sector, showcasing their ability to improve resource efficiency, optimize crop management, and enhance market access. This review provides valuable insights for policymakers, researchers, and farmers seeking to leverage the power of ML for a more sustainable and prosperous Assam’s agricultural landscape.

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“…Although empirical models have been widely employed for estimating ET O , they are challenging to handle intricacy and nonlinear relationships between independent and dependent variables. With the development of arti cial intelligence technology, machine learning (ML) for managing nonlinear problems has improved model performance considerably (Zhao et al 2021). Zhang et al (2018) tested the adaptability of an ET O model constructed using three ML algorithms, namely the support vector machine (SVM), back-propagation neural network (BP) and adaptive neuro-fuzzy inference system (ANFIS), and the results demonstrated that the models have good applicability for predicting ET O .…”

Section: Introductionmentioning

confidence: 99%

Prediction Model for Reference Crop Evapotranspiration Based on the Back-propagation Algorithm with Limited Factors

Zhao

Xing

Wang

et al. 2023

Water Resour Manage

Self Cite

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The accurate estimation of reference crop evapotranspiration (ET O ) is vital for regional water and irrigation water resource management and is bene cial to the rational allocation of regional water resources, and alleviates the disparity between water supply and demand. This study accurately estimates the ET O of 14 meteorological stations in southern China. Five neural network models (backpropagation neural network [BP], extreme learning machine [ELM], ant colony optimization [ACO]-BP, bird swarm algorithm [BSA]-BP, cat swarm optimization [CSO]-BP) were introduced to predict ET O with limited factors using different methods. The results demonstrated that models inputting T (maximum, minimum and average air temperature), sunshine duration (n) and relative humidity (RH) exhibited the highest accuracy of all studied combinations; the role of T, n, RH, wind speed (U 2 ) and average atmospheric pressure (AP) in relation to ET O gradually decreased. All the three biological heuristic algorithms effectively improved the performance of the BP model. The accuracy and computational cost of the CSO-BP model are better than those built by other algorithms. Therefore, it is strongly recommended to use the CSO-BP model for ET O prediction in southern China. This result provided a reference for the more accurate prediction of ET O for future irrigation decision-making and water resource management in southern China.

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Prediction model for daily reference crop evapotranspiration based on hybrid algorithm and principal components analysis in Southwest China

Cited by 31 publications

References 40 publications

Research on Spatial Accessibility Measurement Algorithm for Sanya Tourist Attractions Based on Seasonal Factor Adjustment Analysis

Research on Spatial Accessibility Measurement Algorithm for Sanya Tourist Attractions Based on Seasonal Factor Adjustment Analysis

Recent Trends of Machine Learning Techniques on The Growth of Agricultural Sector of Assam

Prediction Model for Reference Crop Evapotranspiration Based on the Back-propagation Algorithm with Limited Factors

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