A neural network method for the reconstruction of winter wheat yield series based on spatio-temporal heterogeneity

Jiang, Li; Zhang, Xiaohu; Zuo, Wenjun; Xu, Hao; Zhao, Jianqing; Qiu, Xiaolei; Tian, Yongchao; Zhu, Yan

doi:10.1016/j.compag.2018.08.047

“…This topic modelling is affected by challenges related to the heterogeneity of geographical context (Jiang et al. 2018 ), such as the locations sparsity caused by a tiny amount of posts that are tagged with geographical locations. The sparsity issue directly affects spatiotemporal social media analysis tasks such as density estimation (Jeawak et al.…”

Section: Stdm Application-related Challengesmentioning

confidence: 99%

Spatiotemporal data mining: a survey on challenges and open problems

Hamdi

¹

,

Shaban

²

,

Erradi

³

et al. 2021

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Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

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“…Spatiotemporal topic modelling in social media contents is used with time and location-tagged to discover topics. This topic modelling is affected by challenges related to the heterogeneity of geographical context [114], such as the locations sparsity caused by a tiny amount of posts that are tagged with geographical locations. The sparsity issue directly affects spatiotemporal social media analysis tasks such as density estimation [111,230], event location extraction [40] and collaborative filtering [341].…”

Section: Social Media Analysismentioning

confidence: 99%

Spatiotemporal Data Mining: A Survey on Challenges and Open Problems

Hamdi¹,

Shaban²,

Erradi³

et al. 2021

Preprint

View full text Add to dashboard Cite

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

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“…For example, Cheng and Lu (2017) employed an artificial neural network (ANN) to merge the outcomes of P-BSHADE in both the spatial and temporal dimensions. Jiang et al (2018) enhanced the framework of Cheng and Lu (2017) to establish a model for estimating winter wheat yield series. Deng et al (2018) put forward a heterogeneous ST-ANN for predicting ST series.…”

Section: Introductionmentioning

confidence: 99%

“…To facilitate the continuous monitoring of air quality in real‐time, networks of station‐based ambient air quality monitoring systems have been established in China (Hao & Liu, 2016). However, the majority of in situ stations encounter the challenge of missing values, primarily attributed to instrumental malfunction, power outages, and transmission faults (Bai et al., 2020; Jiang et al., 2018; Tan et al., 2022). These missing values have the potential to cause illogical inferences during subsequent data analysis and modeling (Bai et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal stacking method with daily‐cycle restrictions for reconstructing missing hourly PM2.5 records

Chen,

Li

2024

Transactions in GIS

0

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The reliability of hourly PM2.5 data obtained from air quality monitoring stations is compromised as a result of the missing values, thereby impeding the thorough examination of crucial information. In this paper, we present a spatiotemporal (ST) stacking machine learning (ML) method with daily‐cycle restrictions for reconstructing missing hourly PM2.5 records. First, the ST neighbors for the target station with missing values are selected at a daily scale. Subsequently, the non‐null data within the ST neighbors undergo an iterative P‐BSHADE interpolation process for re‐interpolation. Next, a stacking ML model is constructed using the re‐interpolation values and several environmental factors associated with PM2.5 as the predictors, while the observed PM2.5 is taken as the independent variable. Finally, the missing values are reconstructed by inputting the predictors into the trained stacking model. The study utilized hourly PM2.5 data in the Beijing‐Tianjin‐Hebei region as a case study to assess the effectiveness of the proposed method, using daily missing ratios of 10%, 30%, and 50%, respectively. The accuracy of the proposed method was then compared to four contemporary ST interpolation methods. The results indicate that the proposed method exhibits superior performance compared to the classical methods. Specifically, it achieves a reduction in the average root mean square error and mean absolute error by at least 40.6% and 40.1%, respectively. Additionally, the proposed method demonstrates the successful recovery of extreme values in the hourly PM2.5 records, in contrast to the classical methods which often exhibit a tendency to overestimate low values and underestimate high values. Overall, the proposed method presents a viable and efficient approach to recover missing values in the hourly PM2.5 records that demonstrate evident daily periodic patterns.

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A neural network method for the reconstruction of winter wheat yield series based on spatio-temporal heterogeneity

Cited by 12 publications

References 34 publications

Spatiotemporal data mining: a survey on challenges and open problems

Spatiotemporal data mining: a survey on challenges and open problems

Spatiotemporal Data Mining: A Survey on Challenges and Open Problems

Spatiotemporal stacking method with daily‐cycle restrictions for reconstructing missing hourly PM2.5 records

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