Flood Velocity Prediction Using Deep Learning Approach

Floods are one of the most serious natural disasters that can cause huge societal and economic losses.Extensive research has been conducted on topics like flood monitoring,prediction,and loss estimation.In these research fields,flood velocity plays a crucial role and is an important factor that influences the reliability of the outcomes.Traditional methods rely on physical models for flood simulation and prediction and could generate accurate results but often take a long time.Deep learning technology has recently shown significant potential in the same field,especially in terms of efficiency,helping to overcome the time-consuming associated with traditional methods.This study explores the potential of deep learning models in predicting flood velocity.More specifically,we use a Multi-Layer Perceptron(MLP)model,a specific type of Artificial Neural Networks(ANNs),to predict the velocity in the test area of the Lundesokna River in Norway with diverse terrain conditions.Geographic data and flood velocity simulated based on the physical hydraulic model are used in the study for the pre-training,optimization,and testing of the MLP model.Our experiment indicates that the MLP model has the potential to predict flood velocity in diverse terrain conditions of the river with acceptable accuracy against simulated velocity results but with a significant decrease in training time and testing time.Meanwhile,we discuss the limitations for the improvement in future work.

Application of Machine Learning for Flood Prediction and Evaluation in Southern Nigeria

This study explored the application of machine learning techniques for flood prediction and analysis in southern Nigeria. Machine learning is an artificial intelligence technique that uses computer-based instructions to analyze and transform data into useful information to enable systems to make predictions. Traditional methods of flood prediction and analysis often fall short of providing accurate and timely information for effective disaster management. More so, numerical forecasting of flood disasters in the 19th century is not very accurate due to its inability to simplify complex atmospheric dynamics into simple equations. Here, we used Machine learning (ML) techniques including Random Forest (RF), Logistic Regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), and Neural Networks (NN) to model the complex physical processes that cause floods. The dataset contains 59 cases with the goal feature “Event-Type”, including 39 cases of floods and 20 cases of flood/rainstorms. Based on comparison of assessment metrics from models created using historical records, the result shows that NB performed better than all other techniques, followed by RF. The developed model can be used to predict the frequency of flood incidents. The majority of flood scenarios demonstrate that the event poses a significant risk to people’s lives. Therefore, each of the emergency response elements requires adequate knowledge of the flood incidences, continuous early warning service and accurate prediction model. This study can expand knowledge and research on flood predictive modeling in vulnerable areas to inform effective and sustainable contingency planning, policy, and management actions on flood disaster incidents, especially in other technologically underdeveloped settings.

Real-time prediction of mechanical behaviors of underwater shield tunnel structure using machine learning method based on structural health monitoring data

Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of influencing factors,the prediction time scale of existing studies is rough.Therefore,this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network(ATENet)based on structural health monitoring(SHM)data.An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions,and the recurrent neural network is applied to understanding the temporal correlation from the time series.Then,the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h.As a case study,the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel.The robustness study is carried out to verify the reliability and the prediction capability of the proposed model.Finally,the ATENet model is compared with some typical models,and the results indicate that it has the best performance.ATENet model is of great value to predict the realtime evolution trend of tunnel structure.

Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling

Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding privacy.This paper addresses these requirements through the integration of enabler paradigms,including federated learning(FL),cloud/edge computing,softwaredefined/virtualized networking infrastructure,and converged prediction algorithms.The study focuses on achieving reliability and efficiency in real-time prediction models,which depend on the interaction flows and network topology.In response to these challenges,we introduce a modified version of federated logistic regression(FLR)that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks.To establish the FLR framework for mission-critical healthcare applications,we provide a comprehensive workflow in this paper,introducing framework setup,iterative round communications,and model evaluation/deployment.Our optimization process delves into the formulation of loss functions and gradients within the domain of federated optimization,which concludes with the generation of service experience batches for model deployment.To assess the practicality of our approach,we conducted experiments using a hypertension prediction model with data sourced from the 2019 annual dataset(Version 2.0.1)of the Korea Medical Panel Survey.Performance metrics,including end-to-end execution delays,model drop/delivery ratios,and final model accuracies,are captured and compared between the proposed FLR framework and other baseline schemes.Our study offers an FLR framework setup for the enhancement of real-time prediction modeling within intelligent healthcare networks,addressing the critical demands of QoS reliability and privacy preservation.

Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

Flooding is a hazardous natural calamity that causes significant damage to lives and infrastructure in the real world.Therefore,timely and accurate decision-making is essential for mitigating flood-related damages.The traditional flood prediction techniques often encounter challenges in accuracy,timeliness,complexity in handling dynamic flood patterns and leading to substandard flood management strategies.To address these challenges,there is a need for advanced machine learning models that can effectively analyze Internet of Things(IoT)-generated flood data and provide timely and accurate flood predictions.This paper proposes a novel approach-the Adaptive Momentum and Backpropagation(AM-BP)algorithm-for flood prediction and management in IoT networks.The AM-BP model combines the advantages of an adaptive momentum technique with the backpropagation algorithm to enhance flood prediction accuracy and efficiency.Real-world flood data is used for validation,demonstrating the superior performance of the AM-BP algorithm compared to traditional methods.In addition,multilayer high-end computing architecture(MLCA)is used to handle weather data such as rainfall,river water level,soil moisture,etc.The AM-BP’s real-time abilities enable proactive flood management,facilitating timely responses and effective disaster mitigation.Furthermore,the AM-BP algorithm can analyze large and complex datasets,integrating environmental and climatic factors for more accurate flood prediction.The evaluation result shows that the AM-BP algorithm outperforms traditional approaches with an accuracy rate of 96%,96.4%F1-Measure,97%Precision,and 95.9%Recall.The proposed AM-BP model presents a promising solution for flood prediction and management in IoT networks,contributing to more resilient and efficient flood control strategies,and ensuring the safety and well-being of communities at risk of flooding.

Prediction of the flooding area of the northeastern Caspian Sea from satellite images

Studying the dynamic changes in the coastline of the northeastern Caspian Sea is significant since the level of the Caspian is unstable,and the coastline change can cause enormous damage to the ecology,economy,and population of the coastal part of Kazakhstan.In this work,we use remote sensing and Geographic Information System(GIS)technologies to study the changes in the coastline of the northeastern Caspian Sea and predict the extent of flooding with increasing water levels.The proposed methodology for creating dynamic maps can be used to monitor the coastline and forecast the extent of flooding in the area.As a result of this work,the main factors affecting changes in the coastline were identified.After analyzing the water level data from 1988 to 2019,it was revealed that the rise in water level was observed from 1980 to 1995.The maximum sea level rise was recorded at-26.04 m.After that,the sea level began to fall,and between 1996 and 2009,there were no significant changes;the water level fluctuated with an average of-27.18 m.Then,a map of the water level dynamics in the Caspian Sea from 1988 to 2019 was compiled.According to the dynamics map,water level rise and significant coastal retreat were revealed,especially in the northern part of the Caspian Sea and the northern and southern parts of Sora Kaydak.The method for predicting the estimated flooding area was described.As a result,based on a single map,the flooding area of the northeast coast was predicted.A comparative analysis of Landsat and SRTM data is presented.

A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm

Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented.Firstly,a function excluding invalid and abnormal data is established to distinguish TBM operating state,and a feature selection method based on the SelectKBest algorithm is proposed.Accordingly,ten features that are most closely related to the cutter-head torque are selected as input variables,which,in descending order of influence,include the sum of motor torque,cutter-head power,sum of motor power,sum of motor current,advance rate,cutter-head pressure,total thrust force,penetration rate,cutter-head rotational velocity,and field penetration index.Secondly,a real-time cutterhead torque prediction model’s structure is developed,based on the bidirectional long short-term memory(BLSTM)network integrating the dropout algorithm to prevent overfitting.Then,an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is proposed.Early stopping and checkpoint algorithms are integrated to optimize the training process.Finally,a BLSTMbased real-time cutter-head torque prediction model is developed,which fully utilizes the previous time-series tunneling information.The mean absolute percentage error(MAPE)of the model in the verification section is 7.3%,implying that the presented model is suitable for real-time cutter-head torque prediction.Furthermore,an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM tunneling.Comparison of the prediction performance between the base and incremental learning models in the same tunneling section shows that:(1)the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%,and both the coefficient of determination(R^(2))and correlation coefficient(r)between measured and predicted values exceed 0.95;and(2)the incremental learning method is suitable for realtime cutter-head torque prediction and can effectively improve the prediction accuracy and generalization capacity of the model during the excavation process.

Real-time 3-D space numerical shake prediction for earthquake early warning

In earthquake early warning systems, real-time shake prediction through wave propagation simulation is a promising approach. Compared with traditional methods, it does not suffer from the inaccurate estimation of source parameters. For computation efficiency, wave direction is assumed to propagate on the 2-D surface of the earth in these methods. In fact, since the seismic wave propagates in the 3-D sphere of the earth, the 2-D space modeling of wave direction results in inaccurate wave estimation. In this paper, we propose a 3-D space numerical shake pre- diction method, which simulates the wave propagation in 3-D space using radiative transfer theory, and incorporate data assimilation technique to estimate the distribution of wave energy. 2011 Tohoku earthquake is studied as an example to show the validity of the proposed model. 2-D space model and 3-D space model are compared in this article, and the prediction results show that numerical shake prediction based on 3-D space model can estimate the real-time ground motion precisely, and overprediction is alleviated when using 3-D space model.

Efficient Harmonic Analysis Technique for Prediction of IGS Real-Time Satellite Clock Corrections

Real-time satellite orbit and clock corrections obtained from the broadcast ephemerides can be improved using IGS real-time service (RTS) products. Recent research showed that applying such corrections for broadcast ephemerides can significantly improve the RMS of the estimated coordinates. However, unintentional streaming interruption may happen for many reasons such as software or hardware failure. Streaming interruption, if happened, will cause sudden degradation of the obtained solution if only the broadcast ephemerides are used. A better solution can be obtained in real-time if the predicted part of the ultra-rapid products is used. In this paper, Harmonic analysis technique is used to predict the IGS RTS corrections using historical broadcasted data. It is shown that using the predicted clock corrections improves the RMS of the estimated coordinates by about 72%, 58%, and 72% in latitude, longitude, and height directions, respectively and reduces the 2D and 3D errors by about 80% compared with the predicted part of the IGS ultra-rapid clock corrections.

Real-time numerical shake prediction and updating for earthquake early warning

Ground motion prediction is important for earthquake early warning systems, because the region＇s peak ground motion indicates the potential disaster. In order to predict the peak ground motion quickly and pre- cisely with limited station wave records, we propose a real- time numerical shake prediction and updating method. Our method first predicts the ground motion based on the ground motion prediction equation after P waves detection of several stations, denoted as the initial prediction. In order to correct the prediction error of the initial prediction, an updating scheme based on real-time simulation of wave propagation is designed. Data assimilation technique is incorporated to predict the distribution of seismic wave energy precisely. Radiative transfer theory and Monte Carlo simulation are used for modeling wave propagation in 2-D space, and the peak ground motion is calculated as quickly as possible. Our method has potential to predict shakemap, making the potential disaster be predicted before the real disaster happens. 2008 Ms8.0 Wenchuan earthquake is studied as an example to show the validity of the proposed method.

Flood Hazard Prediction from Soil Properties by Remote Sensing and Genographic Information System:A Case Study of Mae Rim Watershed,Chiang Mai Province,Thailand

Physiography and soil in Mae Rim watershed, Chiang Mai Province, Thailand were investigated by using aerial photographs and satellite image in conjunction with field work, and soil infiltration rate and soil shear resistance were measured in field. Many factors affecting runoff were analyzed using the Integrated Land and Water Information System (ILWIS). As a result, a model determining flood hazard was set up. Three maps including runoff curve number map, runoff coefficient map, and flood inundation map were created. In addition, the time of concentration was predicted.

A production prediction method of single well in water flooding oilfield based on integrated temporal convolutional network model

Since the oil production of single well in water flooding reservoir varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed and verified. This method is started from data processing, the correspondence between water injectors and oil producers is determined according to the influence radius of the water injectors, the influence degree of a water injector on an oil producer in the month concerned is added as a model feature, and a Random Forest(RF) model is built to fill the dynamic data of water flooding. The single well history is divided into 4 stages according to its water cut, that is, low water cut, middle water cut, high water cut and extra-high water cut stages. In each stage, a TCN based prediction model is established, hyperparameters of the model are optimized by the Sparrow Search Algorithm(SSA). Finally, the models of the 4 stages are integrated into one whole-life model of the well for production prediction. The application of this method in Daqing Oilfield, NE China shows that:(1) Compared with conventional data processing methods, the data obtained by this processing method are more close to the actual production, and the data set obtained is more authentic and complete.(2) The TCN model has higher prediction accuracy than other 11 models such as Long Short Term Memory(LSTM).(3) Compared with the conventional full-life-cycle models, the model of integrated stages can significantly reduce the error of production prediction.

On the Flood Disasters in the Lower Jingjiang Reaches: Grey Prediction Model and Application

In the light of the historical substantial data (covering a 70-year period) collected in the Lower Jingjiang segment and aided by topological grey method, here we attempt to characterize the occurrence and future trend of flood calamities in the study area. Our findings indicate that overall the high-frequent flood disasters with middle to lower damage prevail at present. A series of dramatic flood waves will appear in the years of 2016, 2022, 2030 and 2042, particularly a destructive flood will occur between 2041 and 2045 in the Lower Jingjiang reaches. Typical of sensitive response to flood hazards in close association with its special geographical location, the lower Jingjiang segment hereby can reflect the development trend of floods in the middle Yangtze reaches. According to the results, a good fitness was revealed between the prediction and practical values. This actually hints that the topological grey method is an effective mathematical means of resolving problems containing uncertainty and indetermination, thus providing valuable information for the flood prediction in the middle Yangtze catchment.

Analysis of Causes and Seasonal Prediction of the Severe Floods in Yangtze／Huaihe Basins during Summer 1991

The present paper shows that a seasonal prediction for the large scale flooding and waterlogging of the mid-lower Yangtze/ Huaihe River basins in the summer of 1991 made successfully in early April 1991.The seasonal forecasting method and some predictors are also introduced and analyzed herein. Because the extra extent of the abnormally early onset of the plum rain period in 1991 was unexpected,great efforts have been made to find out the causes of this abnormality. These causes are mainly associated with the large scale warming of SST surrounding the south and east part of Asia during the preceding winter,while the ENSO-like pattern of SSTA occurred in the North Pacific.In addition,the possible influence of strong solar proton events is analyzed.In order to improve the seasonal pre4iction the usage of the predicted SOl in following spring/summer is also introduced.The author believes thatthe regional climate anomaly can be correctly predicted for one season ahead only on the basis of physical understanding of the interactions of many preceding factors.

Application of predictive control scheduling method to real-time periodic control tasks overrun

Based on the abort strategy of fixed periods, a novel predictive control scheduling methodology was proposed to efficiently solve overrun problems. By applying the latest control value in the prediction sequences to the control objective, the new strategy was expected to optimize the control system for better performance and yet guarantee the schedulability of all tasks under overrun. The schedulability of the real-time systems with p-period overruns was analyzed, and the corresponding stability criteria was given as well. The simulation results show that the new approach can improve the performance of control system compared to that of conventional abort strategy, it can reduce the overshoot and adjust time as well as ensure the schedulability and stability.

INFLUENCE FACTORS AND PREDICTION METHOD ON FLOOD/DROUGHT DURING THE ANNUALLY FIRST RAINY SEASON IN SOUTH CHINA

By using the significance test of two-dimensional wind field anomalies and Monte Carlo simulation experiment scheme, the significance features of wind field anomalies are investigated in relation to flood/drought during the annually first rainy season in south China. Results show that westem Pacific subtropical high and wind anomalies over the northeast of Lake Baikal and central Indian Ocean are important factors. Wind anomalies over the northem India in January and the northwest Pacific in March may be strong prediction signals. Study also shows that rainfall in south China bears a close relation to the geopotential height filed over the northern Pacific in March.

Seasonal Prediction Experiments of the Summer Droughts and Floods during the Early 1990′s in East Asia with Numerical Models

It has been shown by the observed data that during the early 1990′s, the severe disastrous climate occurred in East Asia. In the summer of 1991, severe flood occurred in the Yangtze River and the Huaihe River basin of China and in South Korea, and it also appeared in South Korea in the summer of 1993. However, in the summer of 1994, a dry and hot summer was caused in the Huaihe River basin of China and in R. O. K.. In order to investigate the seasonal predictability of the summer droughts and floods during the early 1990′s in East Asia, the seasonal prediction experiments of the summer droughts and floods in the summers of 1991-1994 in East Asia have been made by using the Institute of Atmopsheric Physics-Two-Level General Circulation Model (IAP-L2 AGCM), the IAP-Atmosphere/Ocean Coupled Model (IAP-CGCM) and the IAP-L2 AGCM including a filtering scheme, respectively. Compared with the observational facts, it is shown that the IAP-L2 AGCM or IAP-CGCM has some predictability for the summer droughts and floods during the early 1990′s in East Asia, especially for the severe droughts and floods in China and R. O. K.. In this study, a filtering scheme is used to improve the seasonal prediction experiments of the summer droughts and floods during the early 1990′s in East Asia. The predicted results show that the filtering scheme to remain the planetary-scale disturbances is an effective method for the improvement of the seasonal prediction of the summer droughts and floods in East Asia.

PREDICTION OF FLOOD SEASON PRECIPITATION IN SOUTHWEST CHINA BASED ON IMPROVED PSO-PLS

In order to achieve the best predictive effect of the Partial Least Squares(PLS) regression model, Particle Swarm Optimization(PSO) algorithm is applied to automatically filter the optimal subset of a set of candidate factors of PLS regression model in this study. An improved version of the Particle Swarm Optimization-Partial Least Squares(PSO-PLS) regression model is applied to the station data of precipitation in Southwest China during flood season.Using the PSO-PLS regression method, the prediction of flood season precipitation in Southwest China has been studied. By introducing the precipitation period series of the mean generating function(MGF) extension as an alternative factor, the MGF improved PSO-PLS regression model was also built up to improve the prediction results.Randomly selected 10%, 20%, 30% of the modeling samples were used as a test trial; random cross validation was conducted on the MGF improved PSO-PLS regression model. The results show that the accuracy of PSO-PLS regression model and the MGF improved PSO-PLS regression model are better than that of the traditional PLS regression model.The training results of the three prediction models with regard to the regional and single station precipitation are considerable, whereas the forecast results indicate that the PSO-PLS regression method and the MGF improved PSO-PLS regression method are much better than the traditional PLS regression method. The MGF improved PSO-PLS regression model has the best forecast performance on precipitation anomaly during the flood season in the southwest of China among three models. The average precipitation(PS score) of 36 stations is 74.7. With the increase of the number of modeling samples, the PS score remained stable. This shows that the PSO algorithm is objective and stable. The MGF improved PSO-PLS regression prediction model is also showed to have good prediction stability and ability.

Yellow River Valley flood and drought disaster：spatial－temporal distribution prediction and early－warning

By means of analysing the historical data of flood-drought grade series in the past 2000 years(A.D.0-1900),especially in the last 5000 years (1470-1900) , this paper revealed the spatial-temporaldistribution features of severe flood and drought in Yellow River Valley. Statistical methods of varianceanalysis, probability transition and the principles of scale correspondence were employed tocomprehensively predicate 90's tendency of severe flood and drought in the Yellow River Valley. In addi-tion, this paper pointed out the possible breaching dikes, sectors and the flooding ranges by future's se-vere flood, meanwhile estimating the associated economic losses and impact to environment.

A Real-time Updated Model Predictive Control Strategy for Batch Processes Based on State Estimation

Nonlinear model predictive control(NMPC) is an appealing control technique for improving the performance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a real-time updated model predictive control method based on state estimation. The method includes two strategies: a multiple model building strategy and a real-time model updated strategy. The multiple model building strategy is to produce a series of sim-plified models to reduce the on-line computational complexity of NMPC. The real-time model updated strategy is to update the simplified models to keep the accuracy of the models describing dynamic process behavior. The me-thod is validated with a typical batch reactor. Simulation studies show that the new method is efficient and robust with respect to model mismatch and changes in process parameters.