Early Warning Research Articles

Machine learning emulation of high resolution inundation maps

SUMMARY Estimating coastal tsunami impact for early-warning or long-term hazard analysis requires the calculation of inundation metrics such as flow-depth or momentum flux. Both applications require the simulation of large numbers of scenarios to capture both the aleatory variability and the epistemic tsunami uncertainty. A computationally demanding step in simulating inundation is solving the non-linear shallow water (NLSW) equations on meshes with sufficiently high resolution to represent the local elevation accurately enough to capture the physics governing the flow. This computational expense is particularly challenging in the context of Tsunami Early Warning where strict time constraints apply. A machine learning (ML) model that predicts inundation maps from offshore simulation results with acceptable accuracy, trained on an acceptably small training set of full simulations, could replace the computationally expensive NLSW part of the simulations for vast numbers of scenarios and predict inundation rapidly and with reduced computational demands. We consider the application of an encoder–decoder based neural network to predict high-resolution inundation maps based only on more cheaply calculated simulated time-series at a limited number of offshore locations. The network needs to be trained using input offshore time-series and the corresponding inundation maps from previously calculated full simulations. We develop and evaluate the ML model on a comprehensive set of inundation simulations for the coast of eastern Sicily for tens of thousands of subduction earthquake sources in the Mediterranean Sea. We find good performance for this case study even using relatively small training sets (order of hundreds) provided that appropriate choices are made in the specification of model parameters, the specification of the loss function and the selection of training events. The uncertainty in the prediction for any given location decreases with the number of training events that inundate that location, with a good range of flow depths needed for accurate predictions. This means that care is needed to ensure that rarer high-inundation scenarios are well-represented in the training sets. The importance of applying regularization techniques increases as the size of the training sets decreases. The computational gain of the proposed methodology depends on the number of complete simulations needed to train the neural network, ranging between 164 and 4196 scenarios in this study. The cost of training the network is small in comparison with the cost of the numerical simulations and, for an ensemble of around 28 000 scenarios, this represents a 6- to 170-fold reduction in computing costs.

Antecedent Conditions Mitigate Carbon Loss During Flash Drought Events

AbstractFlash droughts– the rapid drying of land and intensification of drought conditions—have devasting impacts to natural resources, food supplies, and the economy. Less is currently known about the drivers of flash droughts and their impact on landscape carbon losses. We leverage carbon and water cycle data from NASA OCO‐2 and Soil Moisture Active and Passive missions to quantify flash drought impacts on U.S. carbon exchange. On average, pre‐onset carbon uptake fully offsets post‐onset losses, creating a carbon neutral biosphere over a ±3 month period surrounding flash drought onset. This contrasts with ecosystem models, which underestimate pre‐onset uptake and overestimate post‐onset loss. Furthermore, spaceborne observations of solar induced fluorescence (SIF) provide a reliable indicator of flash droughts at lead times of 2–3 months, due to feedbacks between vegetation growth and soil water loss. This study is expected to improve understanding of flash drought impacts on carbon exchange, and facilitate flash drought early warning.

Determining the net input fluxes of pollutants based on the spatial source apportionment receptor model for early warning of regional soil pollution

Understanding the soil pollutants' net input fluxes is essential for accurate early warning of regional soil pollution. However, the traditional input-output investigation method for soil pollutants' net input fluxes is often costly, especially at the regional scale. This study first assessed the land-use effects on soil heavy metals around a typical copper smelting area in China. Next, an improved spatial source apportionment receptor model, namely robust absolute principal component scores/robust geographically weighted regression with category land-use information (RAPCS/RGWR-CLU), was proposed to apportion the net source contributions, and its performance was compared with those of RAPCS/RGWR and the traditional absolute principal component scores/multiple linear regression (APCS/MLR). Finally, the net input fluxes of soil heavy metals were determined based on RAPCS/RGWR-CLU, and its performance was compared with that of the traditional input-output investigation method. Results showed that (i) land-use effects are significant for soil As, Cu, Pb, and Zn; (ii) RAPCS/RGWR-CLU achieves higher source apportionment accuracy than RAPCS/RGWR and APCS/MLR; and (iii) the net input fluxes determined by RAPCS/RGWR-CLU have similar accuracy to those determined by the traditional input-output investigation method but with significantly lower costs. Therefore, this study provided a cost-effective solution to determine the net input fluxes of soil pollutants.

Comparative Analysis of Machine Learning-Based Predictive Models for Fine Dead Fuel Moisture of Subtropical Forest in China

The moisture content of fine dead surface fuel in forests is a crucial metric for assessing its combustibility and plays a pivotal role in the early warning, occurrence, and spread of forest fires. Accurate prediction of the moisture content of fine dead fuel on the forest surface is a critical challenge in forest fire management. Previous research on fine surface fuel moisture content has been mainly focused on coniferous forests in cold temperate zones, but there has been less attention given to understanding the fuel moisture dynamics in subtropical forests, which limits the development of regional forest fire warning models. Here, we consider the coupled influence of multiple meteorological, terrain, forest stand, and other characteristic factors on the fine dead fuel moisture content within the subtropical evergreen broadleaved forest region of southern China. The ability of five machine learning algorithms to predict the moisture content of fine dead fuel on the forest surface is assessed, and the key factors affecting the model accuracy are identified. Results show that when a single meteorological factor is used as a forecasting model, its forecasting accuracy is less than that of the combined model with multiple characteristic factors. However, the prediction accuracy of the model is improved after the addition of forest stand factors and terrain factors. The model prediction ability is the best for the combination of all feature factors including meteorology, forest stand, and terrain. The overall prediction accuracy of the model is ordered as follows: random forest > extreme gradient boosting > support vector machine > stepwise linear regression > k-nearest neighbor. Canopy density in forest stand factors, slope position and altitude in terrain factors, and average relative air humidity and light intensity in the previous 15 days are the key meteorological factors affecting the prediction accuracy of fuel moisture content. Our results provide scientific guidance and support for understanding the variability of forest surface fuel moisture content and improved regional forest fire warnings.

An innovative fast-start aerobic anode microbial fuel cell biosensor for copper ion detection

The potential of microbial fuel cells (MFCs) based biosensors for water monitoring and early warning is widely recognized. However, the electrochemically active biofilms (EABs) are typically obtained under anaerobic conditions and the formation requires a lengthy time span, which critically limits the application and advancement of the MFCs based monitoring technology. To accelerate the formation rate of EABs and thus shorten the establishment time of MFCs to better meet the needs for early warning and detection of pollutants in the actual aerobic water environment, the aerobic sludge was employed as an inoculation source to construct the MFCs sensing system in this study. It was revealed that biofilms with stable electrochemical properties could be promptly formed within 35 h, reaching a level far ahead of previous studies. Moreover, toxicity tests for copper (Cu2+) ions at concentrations of 1 mg/L, 10 mg/L, and 50 mg/L demonstrated outstanding performance with the maximum inhibition rates of 19.99%, 46.65%, and 53.02% respectively. Remarkably, the response time was found to decrease significantly with the increase of copper ion concentrations. Our results thus open up a new avenue for achieving rapid start-up of MFCs in aerobic conditions, which facilitates the applicability of MFCs based biosensing technology.

Seasonality of influenza-like illness and short-term forecasting model in Chongqing from 2010 to 2022

BackgroundInfluenza-like illness (ILI) imposes a significant burden on patients, employers and society. However, there is no analysis and prediction at the hospital level in Chongqing. We aimed to characterize the seasonality of ILI, examine age heterogeneity in visits, and predict ILI peaks and assess whether they affect hospital operations.MethodsThe multiplicative decomposition model was employed to decompose the trend and seasonality of ILI, and the Seasonal Auto-Regressive Integrated Moving Average with exogenous factors (SARIMAX) model was used for the trend and short-term prediction of ILI. We used Grid Search and Akaike information criterion (AIC) to calibrate and verify the optimal hyperparameters, and verified the residuals of the multiplicative decomposition and SARIMAX model, which are both white noise.ResultsDuring the 12-year study period, ILI showed a continuous upward trend, peaking in winter (Dec. - Jan.) and a small spike in May-June in the 2–4-year-old high-risk group for severe disease. The mean length of stay (LOS) in ILI peaked around summer (about Aug.), and the LOS in the 0–1 and ≥ 65 years old severely high-risk group was more irregular than the others. We found some anomalies in the predictive analysis of the test set, which were basically consistent with the dynamic zero-COVID policy at the time.ConclusionThe ILI patient visits showed a clear cyclical and seasonal pattern. ILI prevention and control activities can be conducted seasonally on an annual basis, and age heterogeneity should be considered in the health resource planning. Targeted immunization policies are essential to mitigate potential pandemic threats. The SARIMAX model has good short-term forecasting ability and accuracy. It can help explore the epidemiological characteristics of ILI and provide an early warning and decision-making basis for the allocation of medical resources related to ILI visits.

Multi-source domain transfer learning with small sample learning for thermal runaway diagnosis of lithium-ion battery

Data-driven thermal runaway diagnosis based on small amounts of thermal runaway data often struggles to produce satisfactory accuracy. However, in actual application scenarios, obtaining real thermal runaway data has a high cost. To this end, we propose a diagnostic method for multi-source domain transfer learning with few-shot learning (MDTL-FSL), which combines the ideas of small sample learning and adversarial learning. Use multiple different but related thermal runaway case data to obtain common diagnostic knowledge to achieve thermal runaway diagnosis. First of all, in order to avoid negative migration of the algorithm model caused by large differences in data distribution between multi-source domains, a data distribution difference measurement method under segmented pressure differences is proposed. This measurement method is simple and effective from multi-source domains. Select the source domain with a small difference in data distribution from the target domain. Secondly, the adversarial learning idea is integrated into multi-source domain transfer learning to learn temporal invariant features. Then, due to the small number of samples in each source domain in the multi-source domain under the thermal runaway scenario, a source domain reorganization mechanism was designed to find the decision boundary based on meta-learning ideas to achieve small-sample learning in the multi-source domain. Finally, we used cells produced by different battery manufacturers to trigger thermal runaway. By comparison and verification with other data-driven algorithms, the results show that the MDTL-FSL model proposed in this article has higher accuracy. At the same time, we use batteries of different types and capacities to trigger thermal runaway under different working conditions. The MDTL-FSL algorithm can issue early warnings before thermal runaway occurs, thereby effectively ensuring the safe operation of the energy storage system.

Intelligent Recognition of Valid Microseismic Events Based on Self-supervised Learning

The identification of valid microseismic events is a crucial stage in the processing of microseismic data and plays a central role in subsequent monitoring and early warning of rockburst disasters. However, obtaining microseismic data is difficult, and manually identifying effective microseismic signals is even more challenging. Therefore, exploring high-precision intelligent classification methods with limited labeled data is necessary. In this study, we applied a self-supervised learning method based on contrastive learning to classify small samples of labeled data. Firstly, use large unlabeled data to pre-train SimCLR (a simple contrastive learning framework) for transfer learning to classify small sample labeled data. The classification accuracy was 87.14%, an increase of 11.3% compared to the supervised-deep-learning method. Furthermore, we directly applied the pre-trained SimCLR to the microseismic data of various projects, achieving a classification accuracy of 86.70%. This demonstrates strong classification performance and generalization and provides valuable technical support for the subsequent monitoring and warning of rockburst disasters.

Deep Learning-Based Forest Fire Risk Research on Monitoring and Early Warning Algorithms

With the development of image processing technology and video analysis technology, forest fire monitoring technology based on video recognition is more and more important in the field of forest fire prevention and control. The objects currently applied to forest fire video image monitoring system monitoring are mainly flames and smoke. This paper proposes a forest fire risk monitoring and early warning algorithm, which integrates a deep learning model, infrared monitoring and early warning, and forest fire weather index. The algorithm first obtains the current visible image and infrared image of the same forest area, utilizing a smoke detection model based on deep learning to detect smoke in the visible image, and obtains the confidence level of the occurrence of fire in said visible image. Then, it determines whether the local temperature value of said infrared image exceeds a preset warning value, and obtains a judgment result based on the infrared image. It calculates again a current FWI based on environmental data, and determines a current fire danger level based on the current FWI. Finally, it determines whether or not to carry out a fire warning based on said fire danger level, said confidence level of the occurrence of fire in said visible image, and said judgment result based on the infrared image. The experimental results show that the accuracy of the algorithm in this paper reaches 94.12%, precision is 96.1%, recall is 93.67, and F1-score is 94.87. The algorithm in this paper can improve the accuracy of smoke identification at the early stage of forest fire danger occurrence, especially by excluding the interference caused by clouds, fog, dust, and so on, thus improving the fire danger warning accuracy.

Review of participatory epidemiology application in human health and nutrition and future directions

Participatory Epidemiology (PE) developed as a branch of veterinary epidemiology and has been widely used in infectious disease control and early warning in resource-limited settings. In the beginning, Participatory Epidemiology was focused on linking the researcher’s communication skills with participatory methods to enable the participation of livestock owners and herders in the identification and assessment of livestock diseases as well as in the development, monitoring, and evaluation of the disease control strategies. In this respect, the use of participatory techniques gradually developed into Participatory Epidemiology with increased recognition by epidemiologists who have developed several tools which are being adapted in human health and nutrition. Relative to veterinary uses and human nutrition, Participatory Epidemiology has not been used in public health in Kenya or for studies on important human diseases, such as Kala-azar. However, in the recent past there have been growing interests from epidemiologists to extend the testing and piloting of the Participatory Epidemiology approach in the arid and semi-arid settings of Kenya geared at empowering communities to identify and solve their health needs. Additionally, there is ongoing debate about the ability of epidemiology to give results that can be better applied in public health practice besides its ability to identify disease risk factors. Scientific research has recommended that participatory research be applied in epidemiology to bridge this descriptive gap and action. This review describes the implementation experiences and lessons learnt in the use of Participatory Epidemiology in understanding human diseases, both communicable and non-communicable as well as human nutrition, a key factor in health and wellness. The reviewed articles provide an understanding into the application of PE approaches which have been limited in pastoralist areas, where zoonotic diseases are prone. The article establishes that as opposed to more conventional methods, using Participatory Epidemiology can generate precise and scientifically credible information within a relatively short period. Data generated from Participatory Epidemiology can be sustainably used to effectively find solutions to the common community health problems based on local preferences for control options.

Coronary artery lesions in children with Kawasaki disease: status quo and nursing care.

Coronary artery lesion (CAL) is a common yet serious complication in children with Kawasaki disease. The aim of the present study was to evaluate the influencing factors of CAL in children with Kawasaki disease, to provide reference for the clinical treatment and care of children with Kawasaki disease. A retrospective cohort study. Children with Kawasaki disease treated in a tertiary hospital in China between 1 January 2021 and 31 December 2022 were selected. The characteristics and clinical data of children with Kawasaki disease were analyzed. Spearman's correlation analysis was conducted to evaluate the relationship between CAL and the characteristics of children with Kawasaki disease. A logistic regression analysis was used to analyze the influencing factors of CAL in children with Kawasaki disease. In total, 185 children with Kawasaki disease were included; the incidence of CAL in children with Kawasaki disease was 18.38%. Pearson's correlation analysis showed that gender (r = 0.504), age (r = 0.611), duration of fever ≥10 days (r = 0.579), hemoglobin (Hb) (r = 0.623), and C-reactive protein (CRP) (r = 0.558) were all correlated with the CAL in children with Kawasaki disease (all p < 0.05). Logistic regression analyses showed that male [odds ratio (OR) = 2.543, 95% confidence interval (CI): 1.801-3.077, p = 0.040], age ≤2 years (OR = 3.002, 95% CI: 2.744-3.641, p = 0.012), duration of fever ≥10 days (OR = 2.089, 95% CI: 1.624-2.515, p = 0.028), Hb ≤105 g/L (OR = 1.914, 95% CI: 1.431-2.406, p = 0.013), and CRP ≥100 mg/L (OR = 2.168, 95% CI: 1.893-2.531, p = 0.035) were the risk factors of CAL in children with Kawasaki disease (all p < 0.05). The incidence of CAL in children with Kawasaki disease is high and there are many related risk factors. Clinical medical workers should take early warning and carry out interventions and nursing care according to these risk factors to improve the prognosis of children with Kawasaki disease.

Perceptions On Nurses' Preparedness For Flooding Disasters: A Qualitative Study

Preparedness is a series of activities carried out to anticipate disasters through organization and through appropriate and effective measures with the expected result being a significant reduction in the risk and losses caused by disasters. This study aims to explore nurses' perceptions regarding readiness to respond to disasters, nurses' preparedness in facing disasters, the role of nurses in disasters, obstacles and support in providing disaster nursing services. A descriptive qualitative approach was used in this study. Purposive sampling was used to select 10 nurses for focus group discussions and four nurses for in-depth interviews. Data were collected from January to February 2024 and analyzed using content analysis. Readiness to respond to disasters includes two subcategories: disaster experience and reasons to immediately respond to disasters. Health workers' preparedness in facing disasters consists of four subcategories: early warning, collaboration, resources, development of specific training. The role of health workers in disasters consists of two subcategories: preparedness stage and response stage. In addition, obstacles and support in providing disaster services consist of two subcategories: obstacles experienced and support needed in providing nursing services when a disaster occurs. The findings of this study can serve as a basis for developing disaster preparedness enhancement programs for nurses and other health professionals. The results of the study can also be used as important evidence to encourage disaster health service policies in an effort to reduce risks caused by disasters with the involvement of stakeholders.

Spanish vs USA cohort comparison of prehospital trauma scores to predict short-term mortality

BackgroundThis study aimed to evaluate three prehospital early warning scores (EWSs): RTS, MGAP and MREMS, to predict short-term mortality in acute life-threatening trauma and injury/illness by comparing United States (US) and Spanish cohorts. MethodsA total of 8,854 patients, 8,598/256 survivors/nonsurvivors, comprised the unified cohort. Datasets were randomly divided into training and test sets. Training sets were used to analyse the discriminative power of the scores in terms of the area under the curve (AUC), and the score performance was assessed in the test set in terms of sensitivity (SE), specificity (SP), accuracy (ACC) and balanced accuracy (BAC). ResultsThe three scores showed great discriminative power with AUCs>0.90, and no significant differences between cohorts were found. In the test set, RTS/MREMS/MGAP showed SE/SP/ACC/BAC values of 86.0/89.9/89.6/87.1%, 91.0/86.9/87.5/88.5%, and 87.7/82.9/83.4/85.2%, respectively. ConclusionsAll EWSs showed excellent ability to predict the risk of short-term mortality, independent of the country.

Oily wastewater detector based on superhydrophobic liquid marbles

Rapid detection of oily wastewater plays a vital role in safeguarding public health, protecting the environment, and supporting effective resource management. It serves as an indispensable tool for early warning, surveillance, and response to various challenges that can affect communities and ecosystems. Here, activated carbon was ground and modified to realize superhydrophobicity, and superhydrophobic activated carbon liquid marbles were prepared with special droplet repulsion properties. The robustness and stability of the liquid marbles were investigated. We demonstrated that the prepared liquid marbles break up immediately once contact with the oils. In addition, we developed a self-made oily wastewater detector based on the high sensitivity of liquid marbles to oily wastewater. Compared with the traditional oily wastewater detection methods, our oily wastewater detector does not require a large amount of chemical reagents, thereby reducing the environmental impact effectively. We believe that this study has a wide range of applications in pollution prevention and liquid treatment.

Water Level Inversion Detection Method for Water Level Images without a Scale in Complex Environments

Accurately perceiving changes in water level information is key to achieving the fine control of water and flooding; however, the existing technology cannot achieve water level recognition in complex and harsh environments, such as at night; in haze, rain, or snow; or during obscuration by floating objects or shadows. Therefore, on the basis of a deep analysis of the characteristics of water level images in complex and harsh environments, in this study, we took full advantage of a deep learning network’s ability to characterise semantic features and carried out exploratory research on water level detection in no-water-ruler scenarios based on the two technical means of target detection and semantic segmentation. The related experiments illustrate that all the methods proposed in this study can effectively adapt to complex and harsh environments. The results of this study are valuable for applications in solving the difficulties of accurate water level detection and flood disaster early warnings in poor-visibility scenarios.

Integrating Microseismic Monitoring for Predicting Water Inrush Hazards in Coal Mines

The essence of roof water inrush in coal mines fundamentally stems from the development of water-bearing fracture zones, facilitating the intrusion of overlying aquifers and thereby leading to water hazard incidents. Monitoring rock-fracturing conditions through the analysis of microseismic data can, to a certain extent, facilitate the prediction and early warning of water hazards. The water inflow volume stands as the most characteristic type of data in mine water inrush accidents. Hence, we investigated the feasibility of predicting water inrush events through anomalies in microseismic data from the perspective of water inflow volume variations. The data collected from the microseismic monitoring system at the 208 working face were utilized to compute localization information and source parameters. Based on the hydrogeological conditions of the working face, the energy screening range and its calculation grid characteristics were determined, followed by the generation of kernel density cloud maps at different depths. By observing these microseismic kernel density cloud maps, probabilities of roof water-conducting channel formation and potential locations were inferred. Subsequently, based on the positions of these roof water-conducting channels on the planar domain, the extension depth and expansion direction of the water-conducting channels were determined. Utilizing microseismic monitoring data, a quantitative assessment of water inrush risk was conducted, thereby establishing a linkage between microseismic data and water (inrush) data, which are two indirectly related datasets. The height of microseismic events was directly proportional to the trend of water inflow in the working face. In contrast, the occurrence of water inflow events and microseismic events exhibited a specific lag effect, with microseismic events occurring prior to water inrush events. Abnormalities in microseismic monitoring data partially reflect changes in water-conducting channel patterns. When connected with coal seam damage zones, water inrush hazards may occur. Therefore, abnormalities in microseismic monitoring data can be regarded as one of the precursor signals indicating potential floor water inrushes in coal seams.

AI-based technology to prognose and diagnose complex crack characteristics of railway concrete sleepers

Railway concrete sleepers are key safety-critical components in ballasted railway tracks. Due to frequent high-intensity impact loadings from train-track interaction over irregularities together with hostile environmental conditions, complicated characteristics of various crack patterns can incur on railway concrete sleepers, which will decrease their durability and service life overtime. Early warning of those cracks can help railway engineers to plan and schedule for renewal and maintenance timely and effectively. This study thus explores the artificial intelligence application of YOLOv5OBB (YOLOv5 with Oriented Bounding Box output) in the identification and classification of cracks in railway sleepers into three distinct types: longitudinal, transverse, and inclined, based on their specific crack angles, which have not been investigated in the past. The identification of crack angles is the novelty of this study. Recognising the various types of cracks is critical, given their varying causes and degrees of severity. Current corrective maintenance methods pose considerable safety risks to workers and exhibit low efficiency, underscoring the need for a more autonomous and efficient solution. This study marks a significant stride towards revolutionising railway maintenance, evidenced by an impressive mAP (Mean Average Precision) of 0.72 for crack detection and a 92% accuracy rate for angle detection. These promising results substantiate our study's potential to pioneer advancements in railway infrastructure maintenance.

A complex network perspective on spatiotemporal propagations of extreme precipitation events in China

Extreme precipitation events (EPEs) cause catastrophic losses to human lives and social economy. Focusing on the wet season from May to October, this paper formulates the complex network to characterize the spatiotemporal propagations of EPEs over 218 river basins in China. Specifically, based on the daily Multi-Source Weighted-Ensemble Precipitation version 2, the event synchronization along with significance test facilitates the complex network of EPEs from 1979 to 2020. By characterizing the topology of EPEs, the results show that synchronous EPEs generally occur over the Hetao Area of Yellow River Basin, Haihe River Basin and Northeast China. The complex network of EPEs classifies the 218 river basins into six communities. In May and June, more than 50 % of EPEs generally occur in South China. From July to September, over 60 % of EPEs tend to be observed in the Huang-Huai-Hai Plain, upper-middle reaches of Yellow River Basin, Northeast China and Southwest China. In October, about 65 % of EPEs happen in South and Southwest China. In the meantime, less than 10 % of EPEs occur in Northwest China. Among the synchronous EPEs starting in late June or early July and peaking in late July to early August, there exist propagations along the Yellow River, Haihe, Liaohe and Songhua River Basin within one to eight days. Eastward moisture transport along this pathway within four to six days drives the propagation of synchronous EPEs. The complex network analysis of this paper provides useful information for early warning and forecasting of EPEs and flood hazards.

Research on Regional Early Warning of Flash Flood Disasters in Small Watershed Based on Multi-source Early Warning Data Fusion

The research on the early warning method of flash floods serves as an important groundwork for the prediction and early warning of flash flood disasters. Being restricted by various objective factors such as lack of data and complicated formation mechanism of flash flood disasters in small watersheds, accurate and targeted early warning has consistently been a weak link within the flash flood control system. In this regard, focusing on a series of problems, such as "short risk forecasting period", "frequent missing and false reports" and "poor early warning accuracy", this research explores a hierarchical partition management system that fulfills the demands of early warning based on the analysis of watershed lag time and risk situation. Meanwhile, based on the comprehensive consideration of the characteristics of various multi-source early warning data, such as meteorological forecast rainfall and monitoring rainfall water level, this research proposes corresponding early warning modes for different types of early warning objects. On these grounds, this research applies the proposed early warning system to Xiaguan Creek in Shangyu District, Zhejiang Province. Relevant findings indicate that the effective extension of the forecasting period, coupled with the reduction of the phenomenon of "missing and false reports", provides scientific support for the early warning and transfer decision related to flash floods.

A data-driven global flood forecasting system for medium to large rivers.

Losses from catastrophic floods are driving intense efforts to increase preparedness and improve response to disastrous flood events by providing early warnings. Yet accurate flood forecasting remains a challenge due to uncertainty in modeling, calibrating, and validating a useful early warning system. This paper presents the Requisitely Simple (ReqSim) flood forecasting system that includes key variables and processes of basin hydrology and atmospheric forcing in a data-driven modeling framework. The simplicity of the modeling structure and data requirements of the system allows for customization and implementation in any medium to large rain-fed river basin globally, provided there are water level or discharge measurements at the forecast locations. The proposed system's efficacy is demonstrated in this paper through providing useful forecasts for various river basins around the world. This include 3-10-day forecasts for the Ganges and Brahmaputra rivers in South Asia, 2-3-day forecast for the Amur and Yangtze rivers in East Asia, 5-10-day forecasts for the Niger, Congo and Zambezi rivers in West and Central Africa, 6-8-day forecasts for the Danube River in Europe, 2-5-day forecasts for the Parana River in South America, and 2-7-day forecasts for the Mississippi, Missouri, Ohio, and Arkansas rivers in the USA. The study also quantifies the effect of basin size, topography, hydrometeorology, and river flow controls on forecast accuracy and lead times. Results indicate that ReqSim's forecasts perform better in river systems with moderate slopes, high flow persistence, and less flow controls. The simple structure, minimal data requirements, ease of operation, and useful operational accuracy make ReqSim an attractive option for effective real-time flood forecasting in medium and large river basins worldwide.