Prediction of Drought Thresholds Triggering Winter Wheat Yield Losses in the Future Based on the CNN-LSTM Model and Copula Theory: A Case Study of Henan Province DOI Creative Commons

Jianqin Ma,

Yan Zhao,

Bifeng Cui

et al.

Agronomy, Journal Year: 2025, Volume and Issue: 15(4), P. 954 - 954

Published: April 14, 2025

As global warming progresses, quantifying drought thresholds for crop yield losses is crucial food security and sustainable agriculture. Based on the CNN-LSTM model Copula function, this study constructs a conditional probability framework under future climate change. It analyzes relationship between Standardized Precipitation–Evapotranspiration Index (SPEI) winter wheat yield, assesses vulnerability of in various regions to stress, quantifies The results showed that (1) SPEI Zhoukou, Sanmenxia, Nanyang was significantly correlated with yield; (2) southern eastern higher than center, western, northern past (2000–2023) (2024–2047); (3) there were significant differences thresholds. loss below 30, 50, 70 percentiles (past/future) −1.86/−2.47, −0.85/−1.39, 0.60/0.35 (Xinyang); −1.45/−2.16, −0.75/−1.34, −0.17/−0.43 (Nanyang); −1.47/−2.24, −0.97/−1.61, 0.69/0.28 (Zhoukou); −2.18/−2.86, −1.80/−2.36, −0.75/−1.08 (Kaifeng), indicating threshold will reduce future. This mainly due different soil conditions Henan Province. In context change, droughts be more frequent. Hence, research provide valuable reference efficient utilization agricultural water resources prevention control risk change

Language: Английский

Modeling the effect of land use change to design a suitable low impact development (LID) system to control surface water pollutants DOI
Alireza Naeini, Massoud Tabesh, Shahrokh Soltaninia

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 932, P. 172756 - 172756

Published: April 24, 2024

Language: Английский

Citations

10

Synergistic assessment of multi-scenario urban waterlogging through data-driven decoupling analysis in high-density urban areas: A case study in Shenzhen, China DOI
Shiqi Zhou,

Weiyi Jia,

Mo Wang

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 369, P. 122330 - 122330

Published: Sept. 3, 2024

Language: Английский

Citations

9

Building resilient urban drainage systems by integrated flood risk index for evidence-based planning DOI
Shakeel Ahmad, X. Peng, Anam Ashraf

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 374, P. 124130 - 124130

Published: Jan. 14, 2025

Language: Английский

Citations

1

Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms DOI
Saeid Janizadeh, Dongkyun Kim, Changhyun Jun

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 366, P. 121764 - 121764

Published: July 8, 2024

Language: Английский

Citations

8

Characteristics and drivers of flooding in recently built urban infrastructure during extreme rainfall DOI
Chenchen Fan, Jingming Hou, Donglai Li

et al.

Urban Climate, Journal Year: 2024, Volume and Issue: 56, P. 102018 - 102018

Published: July 1, 2024

Language: Английский

Citations

7

A novel framework for the spatiotemporal assessment of urban flood vulnerability DOI
Xianzhe Tang, Xi‐Ping Huang, Juwei Tian

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 109, P. 105523 - 105523

Published: May 13, 2024

Language: Английский

Citations

5

Climate change and urban sprawl: Unveiling the escalating flood risks in river deltas with a deep dive into the GBM river delta DOI

Shupu Wu,

Xudong Zhou, Johan Reyns

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 947, P. 174703 - 174703

Published: Oct. 1, 2024

Language: Английский

Citations

5

Flood risk assessment using machine learning, hydrodynamic modelling, and the analytic hierarchy process DOI Creative Commons

Nguyen Huu Duy,

Le T. Pham,

Nguyen Xuan Linh

et al.

Journal of Hydroinformatics, Journal Year: 2024, Volume and Issue: 26(8), P. 1852 - 1882

Published: July 30, 2024

ABSTRACT The objective of this study was to develop a theoretical framework based on machine learning, the hydrodynamic model, and analytic hierarchy process (AHP) assess risk flooding downstream Ba River in Phu Yen. made up three main factors: flood risk, exposure, vulnerability. Hazard calculated from depth, velocity, susceptibility, which depth velocity were using susceptibility built namely, support vector machines, decision trees, AdaBoost, CatBoost. Flood exposure constructed by combining population density, distance river, land use/land cover. vulnerability poverty level road density. indices each factor integrated AHP. results showed that hydraulic model successful simulating events 1993 2020, with Nash–Sutcliffe efficiency values 0.95 0.79, respectively. All learning models performed well, area under curve (AUC) more than 0.90; among them, AdaBoost most accurate, an AUC value 0.99.

Language: Английский

Citations

5

Planning for green infrastructure by integrating multi-driver: Ranking priority based on accessibility equity DOI Creative Commons
Xinyu Dong, Runjia Yang, Yanmei Ye

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 114, P. 105767 - 105767

Published: Aug. 29, 2024

Language: Английский

Citations

5

Urbanization and the Urban Critical Zone DOI Creative Commons
Peiheng Yu,

Yujiao Wei,

Lanji Ma

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: 1(1), P. 100011 - 100011

Published: June 1, 2024

Language: Английский

Citations

5