Evaluation and dynamic prediction of ecological security from the perspective of sustainable development: a case study of Shaanxi Province, China

A coupling model based on spatial characteristics and evolution of terrestrial ecosystem carbon storage: a case study of Hanzhong

Ecosystem carbon storage (ECS) is a critical consideration in reducing the impact of global warming and tackling environmental challenges, positioning it at the forefront of contemporary research. Due to the significant differences in the influence of land usage patterns on ECS in various policy contexts and China's commitment to attaining a carbon-neutral status, a model simulating different scenarios is needed to analyze the spatiotemporal characteristics and evolutionary process of carbon storage in terrestrial ecosystems accurately. To address this challenge, this study established a coupling model of "Geographical analysis -Evolution analysis -Predicting (GEP)" for assessing ecosystem ECS and analyzing its spatial characteristics and evolutionary patterns and projecting the spatial distribution of ECS under various developmental scenarios, which analyzed variations in ECS across different levels of magnitude and delineated the changing areas across a range of varying scenarios in the future additionally. The outcomes suggested that the ECS decreased by 1.17 × 106 t from 1990 to 2020, which pertaining to the utilization transfer of land in the area, whose change in ECS levels with a positive trend. It is predicted that the ECS will grow by 1.15 × 106 t and 1.44 × 106 t, in 2030 and 2060 compared with 2020 within the framework of natural development scenario (NDS), while within the framework of ecological protection scene (EPS), ECS will increase significantly, increasing by 3.06 × 106 t and 4.44 × 106 t. There will be more areas where ECS increases within the framework of EPS, by comparing with the NDS. This study offers a comprehensive analysis of Hanzhong City's carbon storage trends, demonstrating its significant impact on climate change mitigation and serving as a predictive model for similar regions, which underscores the importance of localized carbon management strategies, offering valuable insights for local governments in formulating effective climate adaptation and mitigation policies.

Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates

BACKGROUND

Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research.

RESULTS

The infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575.

CONCLUSIONS

We propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.

The role of land use change in affecting ecosystem services and the ecological security pattern of the Hexi Regions, Northwest China

The land use and land cover change (LUCC) associated with climate change and human activities is supposed to exert a significant effect on ecosystem functions in arid inland regions. However, the role of LUCC in shaping the spatio-temporal patterns of ecosystem services and ecological security remain unclear, especially under different future LUCC scenarios. Here, we evaluated dynamic changes of ecosystem services and ecological security pattern (ESP) in the Hexi Regions based on LUCC and other environment variables by integrating morphological spatial pattern analysis (MSPA), entropy weight method and circuit theory. Our result showed that the LUCC was generally stable from 1980 to 2050. Compare to 2020, the land conversion under natural growth (NG), ecological protection (EP) and urban development (UD) scenarios in 2050 has changed by 10.30 %, 10.10 %, and 10.31 %, respectively. The forest, medium-cover grassland and water increased in the EP scenario, and construction land and cropland greatly expanded in the other two scenarios. Ecosystem services grew larger in the EP scenario by 2050 in comparison with the NG and UD scenarios. The ESP in the Hexi Regions has obvious spatial differences during 1980-2050. The larger ecological sources and less resistance corridors were mainly distributed in the central and eastern of the Hexi Regions with high ecosystem services. Conversely, fragmented ecological sources and larger resistance corridors were mostly located in the western regions blocked by sandy land, bare land or mountains. Compared to 2020, the area of ecological sources and pinch points under the EP scenario in 2050 increased by 4.10 × 10³ km² and 0.31 × 10³ km², respectively. The number of ecological corridors reduced while the length and resistance increased apart from the EP scenario. Our results highlighted the importance of ecological protection in shaping the LUCC, which further enhances the integrity of ecosystem and ecological security.