Flow forecasting is an essential topic for flood prevention and mitigation. This study utilizes a data-driven approach, the Long Short-Term Memory neural network (LSTM), to simulate rainfall–runoff relationships for catchments with different climate conditions. The LSTM method presented was tested in three catchments with distinct climate zones in China. The recurrent neural network (RNN) was adopted for comparison to verify the superiority of the LSTM model in terms of time series prediction problems. The results of LSTM were also compared with a widely used process-based model, the Xinanjiang model (XAJ), as a benchmark to test the applicability of this novel method. The results suggest that LSTM could provide comparable quality predictions as the XAJ model and can be considered an efficient hydrology modeling approach. A real-time forecasting approach coupled with the k-nearest neighbor (KNN) algorithm as an updating method was proposed in this study to generalize the plausibility of the LSTM method for flood forecasting in a decision support system. We compared the simulation results of the LSTM and the LSTM-KNN model, which demonstrated the effectiveness of the LSTM-KNN model in the study areas and underscored the potential of the proposed model for real-time flood forecasting.

Physically based distributed hydrological models were used to describe small-scale hydrological information in detail. However, the sensitivity of the model to spatially varied parameters and inputs limits the accuracy for application. In this paper, relevant influence factors and sensitive parameters were analyzed to solve this problem. First, a set of digital elevation model (DEM) resolutions and channel thresholds were generated to extract the hydrological influence factors. Second, a numerical relationship between sensitive parameters and influence factors was established to define parameters reasonably. Next, the topographic index (TI) was computed to study the similarity. At last, simulation results were analyzed in two different ways: (1) to observe the change regularity of influence factors and sensitive parameters through the variation of DEM resolutions and channel thresholds and (2) to compare the simulation accuracy of the nested catchment, particularly in the subcatchments and interior grids. Increasing the grid size from 250 m to 1000 m, the TI increased from 9.08 to 11.16 and the Nash-Sutcliffe efficiency (NSE) decreased from 0.77 to 0.75. Utilizing the parameters calculated by the established relationship, the simulation results show the same NSE in the outlet and a better NSE in the simple subcatchment than the calculated interior grids.

Previous research has found a greater congruency effect when participants are alerted in the Attention Network Test relative to situations where participants are not alerted. However, the interaction between alerting and congruency that has been reported with arrow stimuli as well as in the Simon task does not generalize to the Stroop task (Schneider, 2019 Schneider, D. W. (2019). Alertness and cognitive control: Is there a spatial attention constraint? Attention, Perception, & Psychophysics, 81(1), 119–136. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2019-12485-008&site=ehost-live doi: 10.3758/s13414-018-1613-9 [Crossref], [PubMed], [Web of Science ®] , [Google Scholar] ). To test the hypothesis that the interaction between alerting and congruency requires pre-existing directional associations, Experiment 1 (N = 40) used numeric stimuli since numbers are associated with spatial directions (i.e., the SNARC effect; spatial-numerical association of response codes). Results show the typical interaction between congruency and alerting. Experiment 2 (N = 40) further tested this by replacing numeric stimuli with characters that do not have directional associations and the interaction disappeared. Together these data support the proposal that alerting boosts stimulus processing, which facilitates responses to the target, but also enhances stimulus-response directional associations, which magnifies effects of the distractors.

The rainy season characteristics are directly modulated by atmospheric circulation and moisture transport dynamics. Focusing on the characteristics of the rainy season onset date (RSOD), this study aims to advance the understanding and prediction of climate change impacts on agricultural production and disaster mitigation strategies. Based on rainfall data from 66 meteorological stations in northeast China (NEC) from 1961 to 2020, this study determined the patterns of the RSOD in the region and established its mechanistic linkages with atmospheric circulation and water vapor transport mechanisms. This study identifies a climatic regime shift around 2000, with the RSOD transitioning from low to high interannual variability in NEC. Further analysis reveals a strong correlation between the RSOD and atmospheric circulation characteristics: cyclonic vorticity amplifies before the RSOD and dissipates afterward. Innovatively, this study reveals a significant transition in the water vapor transport paths during the early rainy season in NEC around 2000, shifting from eastern Mongolia–Sea of Japan to the northwestern Pacific region. Moreover, the advance or delay of the RSOD directly influences the water vapor transport intensity—an early (delayed) RSOD is associated with enhanced (weakened) water vapor transport. These findings provide a new perspective for predicting the RSOD in the context of climate change while providing critical theoretical underpinnings for optimizing agricultural strategies and enhancing disaster prevention protocols.

The article predicts floods in the Pahang region by trying to build an ANN neural network model to reduce the damage caused by flood disasters and suggests that the flood detection dashboard is a good tool. The second part of the literature review se...