Abstract Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency and intensity of extremes and other associated events are continuously increasing due to climate change and global warming. The accurate prediction, characterization, and attribution of atmospheric extreme events is, therefore, a key research field in which many groups are currently working by applying different methodologies and computational tools. Machine learning and deep learning methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric extreme events. This paper reviews machine learning and deep learning approaches applied to the analysis, characterization, prediction, and attribution of the most important atmospheric extremes. A summary of the most used machine learning and deep learning techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. The critical literature review has been extended to extreme events related to rainfall and floods, heatwaves and extreme temperatures, droughts, severe weather events and fog, and low-visibility episodes. A case study focused on the analysis of extreme atmospheric temperature prediction with ML and DL techniques is also presented in the paper. Conclusions, perspectives, and outlooks on the field are finally drawn. 

Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review

The saturation-excess mechanism, infiltration-excess mechanism and subsurface stormflow mechanism are the fundamental of the hybrid runoff generation models. Existing hybrid runoff generation methods pay much attention to the surface runoff and subsurface runoff, but less to the characterization of subsurface stormflow. Additionally, the subsurface stormflow was mostly separated from the saturated-excess. This kind of separation ignores subsurface stormflow in unsaturated areas, and therefore may result in significant errors sometimes. Therefore, a novel conceptual method for subsurface stormflow in unsaturated areas over the interface was proposed through a combination of the Green-Ampt equation with the parabolic infiltration capacity distribution curve. Then, the subsurface stormflow was considered as the sum of the infiltration excess over the unsaturated areas and the saturation excess over the saturated areas. On this basis, an improved flexible runoff generation strategy was developed to incorporate the spatial heterogeneity of underlying surface conditions and rainfall conditions based on the three single runoff generation mechanisms. Finally, the Xun River basin was selected as a case study. Performances of different runoff generation strategies were compared and discussed based on flood events and continues flow. And the sensitivity analysis of the parameters was carried out. Results show that the proposed strategy performed the best among the five strategies, and the accuracy of the simulation results was affected significantly by the infiltration-excess subsurface stormflow over the unsaturated areas. The proposed method can be used for the watersheds where there may be abundant subsurface stormflow. 

Hydrological modelling with an improved flexible hybrid runoff generation strategy

The application of PPP mode in urban underground utility tunnel project has been vigorously promoted and popularized in China. However, the degree of project promotion varies greatly in different regions, and the enthusiasm of social capital to participate in projects varies. In essence, the impact of project risks cannot be ignored. Using the fault tree model to analyze the factors that affect the risk of PPP mode in the urban underground utility tunnel project, and obtains five factors which have great influence on the risk of the project, such as the demand for corridor entrance, revenue, financial capacity, imperfect legal system, land expropriation and historical legacy. Considering the different economic development strengths of different regions, the adaptability of PPP projects is different. Through the empirical analysis of improving the correlation degree, the conclusion is drawn that the application of PPP model in underground comprehensive pipeline corridor is more suitable for developed areas.

http://www.dpi-proceedings.com/index.php/dtetr/article/download/32026/30616

Risk Assessment of PPP Model Based on Fault Tree Model for Urban Underground Utility Tunnel

Hydrological forecasting plays a crucial role in mitigating flood risks and managing water resources. Data-driven hydrological models demonstrate exceptional fitting capabilities and adaptability. Recognizing the limitations of single-model forecasting, this study introduces an innovative approach known as the Improved K-Nearest Neighbor Multi-Model Ensemble (IKNN-MME) method to enhance the runoff prediction. IKNN-MME dynamically adjusts model weights based on the similarity of historical data, acknowledging the influence of different training data features on localized predictions. By combining an enhanced K-Nearest Neighbor (KNN) algorithm with adaptive weighting, it offers a more powerful and flexible ensemble. This study evaluates the performance of the IKNN-MME method across four basins in the United States and compares it to other multi-model ensemble methods and benchmark models. The results underscore its outstanding performance and adaptability, offering a promising avenue for improving runoff forecasting. 

Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction

Study Region: The Fenhe River Basin (FRB) is an important tributary of the Yellow River Basin and locate in a semi-arid and semi-humid region in northwest China. Over the past four decades, significant anthropogenic activities have resulted in significant land use and land cover change (LUCC) that have markedly impacted hydrological processes in FRB. Study Focus: To analyze land use changes under multiple historical land use scenarios from LU1980 to LU2015, and to establish the Water and Energy transfer Processes in Large basins (WEP-L) model to quantify the influences of LUCC on hydrological processes, particularly on baseflow. New hydrological insights for the region: LUCC from LU1980 to LU2015 resulted in a 14.5 % increase in water yield (WY) and a 2.7 % and 1.4 % decrease in baseflow (BF) and evapotranspiration, respectively. Urbanization led to a significant increase in WY and a reduction in BF, exhibiting strong spatial gradients between urban centers and their peripheries. Increases in forestland decreased WY, while ET showed no significant change due to large absolute values and moisture limitations. Furthermore, the response of BF to forest cover exhibits spatial variation. A negative correlation between BF and forest cover was evident in upstream, whereas no significant correlation was observed in downstream. The findings of this study offer valuable insights for land use planning and sustainable watershed management in semi-arid semi-humid region. 

Influence of human-induced land use change on hydrological processes in semi-humid and semi-arid region: A case in the Fenhe River Basin

Short-term wind speed forecasting based on a novel KANInformer model and improved dual decomposition


 Systematic domestic water demand prediction is of great importance for social and economic sustainable development. To explain the system of domestic water demand prediction more completely, a system dynamic model based on the social-hydrology method was proposed in this paper. The key natural and socio-economic factors that affect the domestic water demand most were identified and investigated. The prediction equations of water price considering the feedback between humans and nature were deduced. The expressions of water-saving consciousness were proposed for the first time. Furthermore, the differential and difference equations for domestic water demand prediction were established to realize the future domestic water demand forecasts considering the natural and social driving forces. Finally, three scenarios were designed to investigate the impact of climate change and economic development on the domestic water demand. The upper and middle Pearl River Basin was selected as a case study. The predicted domestic water demand is consistent with the historical results, which demonstrates that the proposed model can be well used for the prediction of the domestic water demand. Under the three scenarios, the domestic water demand increased by 33, 27 and 18% in 2050 compared with that in 2017, respectively.

Domestic water demand prediction based on system dynamics combined with social-hydrology methods

The PPP is just the mode of public-private partnership, referring to the long-term partnership established between the government and the social capital to provide the public products or services, with typical form of organizing a special project company by the government and the social capital based on the modern enterprise joint stock system to sign contracts with the government for special public products or service projects to provide public products and services. The paper selects a concrete case in the Inner Mongolian municipality and considers the practical situation and demands of the ethnic region to select the operation mode of PPP, divide assets ownership, design the concrete operation mode, institute the performance assessment mechanism, and define the price adjustment mechanism and interest sharing mechanism. key words: PPP project, operation mode, BOT

/pdf/research-on-the-operation-mode-of-ppp-project-for-logistics-1qgwhkcgm5.pdf

Research on the Operation Mode of PPP Project for Logistics Park in the Ethnic Regions

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