Global Land Surface Dry/Wet Conditions Mining Based on Spatial‐Temporal Association Rules

Zheng, Kun; Qin, Wei C.; Du, Xiaoping

doi:10.1029/2020ea001501

Cited by 3 publications

(3 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The identified spatial types and dry-wet signals expand the basis for the decision-making of drought management. This could be useful for instructing local governments to take precautions to mitigate the potential impacts of severe drought or wetness [137]. From a national-scale perspective, the study results provide a spatial representation of recent meteorological droughts, which has certain implications for drought prevention and mitigation strategies in the context of global warming [127].…”

Section: Limitations and Recommendationsmentioning

confidence: 96%

“…These results may be helpful in exploring the spatial heterogeneity in dry-wet trends and for supplementing the study of the meteorological drought pattern in China in a changing environment. On the other hand, severe drought and wetness have serious impacts on human society and the natural environment [137]. This study provided the spatial pattern of the drywet status for the next year forecasted by the SARIMA model.…”

Section: Limitations and Recommendationsmentioning

confidence: 99%

See 1 more Smart Citation

Evidence for Intensification in Meteorological Drought since the 1950s and Recent Dryness–Wetness Forecasting in China

Yang

Bing

2022

Atmosphere

View full text Add to dashboard Cite

Drought is one of the major environmental stressors; drought is increasingly threatening the living environment of mankind. The standardized precipitation evapotranspiration index (SPEI) with a 12-month timescale was adopted to monitor dry–wet status over China from 1951 to 2021. The modified Mann–Kendall (MMK) and Pettitt tests were used to assess the temporal trend and nonlinear behavior of annual drought variability. The analysis focuses on the spatio-temporal structure of the dry–wet transition and its general connections with climate change processes. In addition, the seasonal autoregressive integrated moving average (SARIMA) model was applied to forecast the dry–wet behavior in the next year (2022) at 160 stations, and the hotspot areas for extreme dryness–wetness in China were identified in the near term. The results indicate that the dry–wet climate in China overall exhibits interannual variability characterized by intensified drought. The climate in the Northeast China (NEC), North China (NC), Northwest China (NWC), and Southwest China (SWC) has experienced a significant (p < 0.05) drying trend; however, the dry–wet changes in the East China (EC) and South Central China (SCC) are highly spatially heterogeneous. The significant uptrend in precipitation is mainly concentrated to the west of 100° E; the rising magnitude of precipitation is higher in Eastern China near 30° N, with a changing rate of 20–40 mm/decade. Each of the sub-regions has experienced significant (p < 0.01) warming over the past 71 years. Geographically, the increase in temperature north of 30° N is noticeably higher than that south of 30° N, with trend magnitudes of 0.30–0.50 °C/decade and 0.15–0.30 °C/decade, respectively. The response of the northern part of Eastern China to the warming trend had already emerged as early as the 1980s; these responses were earlier and more intense than those south of 40° N latitude (1990s). The drying trends are statistically significant in the northern and southern regions, bounded by 30° N, with trend magnitudes of −0.30–−0.20/decade and −0.20–−0.10/decade, respectively. The northern and southwestern parts of China have experienced a significant (p < 0.05) increase in the drought level since the 1950s, which is closely related to significant warming in recent decades. This study reveals the consistency of the spatial distribution of variations in precipitation and the SPEI along 30° N latitude. A weak uptrend in the SPEI, i.e., an increase in wetness, is shown in Eastern China surrounding 30° N, with a changing rate of 0.003–0.10/decade; this is closely associated with increasing precipitation in the area. Drought forecasting indicates that recent drying areas are located in NWC, the western part of NC, the western part of SWC, and the southern part of SCC. The climate is expected to show wetting characteristics in NEC, the southeastern part of NC, and the eastern part of EC. The dry–wet conditions spanning the area between 30–40° N and 100–110° E exhibit a greater spatial variability. The region between 20–50° N and 80–105° E will continue to face intense challenges from drought in the near future. This study provides compelling evidence for the temporal variability of meteorological drought in different sub-regions of China. The findings may contribute to understanding the spatio-temporal effect of historical climate change on dry–wet variation in the region since the 1950s, particularly in the context of global warming.

show abstract

Section: Limitations and Recommendationsmentioning

confidence: 96%

Section: Limitations and Recommendationsmentioning

confidence: 99%

Evidence for Intensification in Meteorological Drought since the 1950s and Recent Dryness–Wetness Forecasting in China

Yang

Bing

2022

Atmosphere

View full text Add to dashboard Cite

show abstract

“…Spatial association rule mining has played an important role in the fields of the ecological environment [1] , public social security [2] and economic development. Moreover, spatial association rule mining is a popular topic and has attracted the research interest of geographers.…”

Section: Introductionmentioning

confidence: 99%

Mining significant local spatial association rules for multi-category point data

Cai,

Chen,

Chen

et al. 2024

Heliyon

View full text Add to dashboard Cite

Abnormal Node Location Algorithm in Multi-source Heterogeneous Network Based on Association Rules

Ma,

Hao

2023

J. Test. Eval.

View full text Add to dashboard Cite

It is difficult to mine association rules between multi-source heterogeneous network nodes, which leads to the high false detection rate, node location error, and location overhead of multi-source heterogeneous network abnormal nodes. Therefore, this article proposes a new abnormal node location algorithm in multi-source heterogeneous network based on association rules. The algorithm of sliding windows multiple value pattern tree is used to extract the association of multi-source heterogeneous network data and realize the mining of association rules between multi-source heterogeneous network nodes. The association rule mining results are preprocessed, and the multi-source heterogeneous network abnormal node location model is built using the multidimensional scaling algorithm and the low rank matrix decomposition model. The model is solved to obtain the multi-source heterogeneous network abnormal node location results. The experimental results show that the false detection rate of abnormal nodes in the proposed algorithm is always below 4.6 %, the maximum node location error is only 2.1 mm, and the average location overhead is 55.1 ms, which can achieve the goal of fast and accurate location of abnormal nodes in multi-source heterogeneous networks.

show abstract

Global Land Surface Dry/Wet Conditions Mining Based on Spatial‐Temporal Association Rules

Cited by 3 publications

References 54 publications

Evidence for Intensification in Meteorological Drought since the 1950s and Recent Dryness–Wetness Forecasting in China

Evidence for Intensification in Meteorological Drought since the 1950s and Recent Dryness–Wetness Forecasting in China

Mining significant local spatial association rules for multi-category point data

Abnormal Node Location Algorithm in Multi-source Heterogeneous Network Based on Association Rules

Contact Info

Product

Resources

About