Flood Forecasting and Warning System: A Survey of Models and Their Applications in West Africa

Flood events occurrences and frequencies in the world are of immense worry for the stability of the economy and life safety. Africa continent is the third continent the most negatively affected by the flood events after Asia and Europe. Eastern Africa is the most hit in Africa. However, Africa continent is at the early stage in term of flood forecasting models development and implementation. Very few hydrological models for flood forecasting are available and implemented in Africa for the flood mitigation. And for the majority of the cases, they need to be improved because of the time evolution. Flash flood in Bamako (Mali) has been putting both human life and the economy in jeopardy. Studying this phenomenon, as to propose applicable solutions for its alleviation in Bamako is a great concern. Therefore, it is of upmost importance to know the existing scientific works related to this situation in Mali and elsewhere. The main aim was to point out the various solutions implemented by various local and international institutions, in order to fight against the flood events. Two types of methods are used for the flood events adaptation: the structural and non-structural methods. The structural methods are essentially based on the implementation of the structures like the dams, dykes, levees, etc. The problem of these methods is that they may reduce the volume of water that will inundate the area but are not efficient for the prediction of the coming floods and cannot alert the population with any lead time in advance. The non-structural methods are the one allowing to perform the prediction with acceptable lead time. They used the hydrological rainfall-runoff models and are the widely methods used for the flood adaptation. This review is more accentuated on the various types non-structural methods and their application in African countries in general and West African countries in particular with their strengths and weaknesses. Hydrologiska Byråns Vattenbalansavdelning (HBV), Hydrologic Engineer Center Hydrologic Model System (HEC-HMS) and Soil and Water Assessment Tool (SWAT) are the hydrological models that are the most widely used in West Africa for the purpose of flood forecasting. The easily way of calibration and the weak number of input data make these models appropriate for the West Africa region where the data are scarce and often with bad quality. These models when implemented and applied, can predict the coming floods, allow the population to adapt and mitigate the flood events and reduce considerably the impacts of floods especially in terms of loss of life.

Analysis of Forecast and Early Warning of Flood in Medium and Small Rivers

Flooding of small and medium rivers is caused by environmental factors like rainfall and soil loosening.With the development and application of technologies such as the Internet of Things and big data,the disaster supervision and management of large river basins in China has improved over the years.However,due to the frequent floods in small and medium-sized rivers in our country,the current prediction and early warning of small and medium-sized rivers is not accurate enough;it is difficult to realize real-time monitoring of small and medium-sized rivers,and it is also impossible to obtain corresponding data and information in time.Therefore,the construction and application of small and medium-sized river prediction and early warning systems should be further improved.This paper presents an analysis and discussion on flood forecasting and early warning systems for small and medium-sized rivers in detail,and corresponding strategies to improve the effect of forecasting and early warning systems are proposed.

River channel flood forecasting method of coupling wavelet neural network with autoregressive model

Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN. The WNN has the characteristics of fast convergence and improved capability of nonlinear approximation. For the purpose of adapting the timevarying characteristics of flood routing, the WNN is coupled with an AR real-time correction model. The AR model is utilized to calculate the forecast error. The coefficients of the AR real-time correction model are dynamically updated by an adaptive fading factor recursive least square（RLS） method. The application of the flood forecasting method in the cross section of Xijiang River at Gaoyao shows its effectiveness.

Forecast of Flood in Chaohu Lake Basin of China Based on Grey-Markov Theory

Flood is one kind of unexpected and the most common natural disasters, which is affected by many factors and has complex mechanism. At home and abroad, there is still no mature theory and method used for the long-term forecast of natural precipitation at present. In the present paper the disadvantages of grey GM (1, 1) and Markov chain are ana- lyzed, and Grey-Markov forecast theory about flood is put forward and then the modifying model is developed by making prediction of Chaohu Lake basin. Hydrological law was conducted based on the theoretical forecasts by grey system GM (1, 1) forecast model with improved Markov chain. The above method contained Stat-analysis, embodying scientific approach, precise forecast and its reliable results.

Real-time flood forecasting of Huai River with flood diversion and retarding areas

A combination of the rainfall-runoff module of the Xin’anjiang model, the Muskingum routing method, the water stage simulating hydrologic method, the diffusion wave nonlinear water stage method, and the real-time error correction method is applied to the real-time flood forecasting and regulation of the Huai River with flood diversion and retarding areas. The Xin’anjiang model is used to forecast the flood discharge hydrograph of the upstream and tributary. The flood routing of the main channel and flood diversion areas is based on the Muskingum method. The water stage of the downstream boundary condition is calculated with the water stage simulating hydrologic method and the water stages of each cross section are calculated from downstream to upstream with the diffusion wave nonlinear water stage method. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The faded-memory forgetting factor least square of error series is used as the real-time error correction method for forecasting discharge and water stage. As an example, the combined models were applied to flood forecasting and regulation of the upper reaches of the Huai River above Lutaizi during the 2007 flood season. The forecast achieves a high accuracy and the results show that the combined models provide a scientific way of flood forecasting and regulation for a complex watershed with flood diversion and retarding areas.

Nash Model Parameter Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

A hydrologic model consists of several parameters which are usually calibrated based on observed hy-drologic processes. Due to the uncertainty of the hydrologic processes, model parameters are also uncertain, which further leads to the uncertainty of forecast results of a hydrologic model. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo simulation based Adaptive Metropolis method (AM-MCMC) was used to study parameter uncertainty of Nash model, while the probabilistic flood forecasting was made with the simu-lated samples of parameters of Nash model. The results of a case study shows that the AM-MCMC based on BFS proposed in this paper is suitable to obtain the posterior distribution of the parameters of Nash model according to the known information of the parameters. The use of Nash model and AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as to find the mean and variance of flood discharge, which may be useful to estimate the risk of flood control decision.

Development of Flood Forecasting System Using Statistical and ANN Techniques in the Downstream Catchment of Mahanadi Basin, India

The floods in river Mahanadi delta are due to either dam release of Hirakud or due to contribution of intercepted catchment between Hirakud dam and delta. It is seen from post-Hirakud periods (1958) that out of 19 floods 14 are due to intercepted catchment contribution. The existing flood forecasting systems are mostly for upstream catchment, forecasting the inflow to reservoir, whereas the downstream catchment is devoid of a sound flood forecasting system. Therefore, in this study an attempt has been made to develop a workable forecasting system for downstream catchment. Instead of taking the flow time series concurrent flood peaks of 12 years of base and forecasting stations with its corresponding travel time are considered for analysis. Both statistical method and ANN based approach are considered for finding the peak to reach at delta head with its corresponding travel time. The travel time has been finalized adopting clustering techniques, there by differentiating high, medium and low peaks. The method is simple and it does not take into consideration the rainfall and other factors in the intercepted catchment. A comparison between both methods are tested and it is found that the ANN methods are better beyond the calibration range over statistical method and the efficiency of either methods reduces as the prediction reach is extended. However, it is able to give the peak discharge at delta head before 24 hour to 37 hour for high to low peaks.

Rainfall-runoff simulation and flood forecasting for Huaihe Basin

The main purpose of this study was to forecast the inflow to Hongze Lake using the Xin＇anjiang rainfall-runoff model. The upper area of Hongze Lake in the Huaihe Basin was divided into 23 sub-basins, including the surface of Hongze Lake. The influence of reservoirs and gates on flood forecasting was considered in a practical and simple way. With a one-day time step, the linear and non-linear Muskingum method was used for channel flood routing, and the least-square regression model was used for real-time correction in flood forecasting. Representative historical data were collected for the model calibration. The hydrological model parameters for each sub-basin were calibrated individually, so the parameters of the Xin＇anjiang model were different for different sub-basins. This flood forecasting system was used in the real-time simulation of the large flood in 2005 and the results are satisfactory when compared with measured data from the flood.

Flood Forecasting GIS Water-Flow Visualization Enhancement (WaVE): A Case Study

Riverine flood event situation awareness and emergency management decision support systems require accurate and scalable geoanalytic data at the local level. This paper introduces the Water-flow Visualization Enhancement (WaVE), a new framework and toolset that integrates enhanced geospatial analytics visualization (common operating picture) and decision support modular tools. WaVE enables users to: 1) dynamically generate on-the-fly, highly granular and interactive geovisual real-time and predictive flood maps that can be scaled down to show discharge, inundation, water velocity, and ancillary geomorphology and hydrology data from the national level to regional and local level;2) integrate data and model analysis results from multiple sources;3) utilize machine learning correlation indexing to interpolate streamflow proxy estimates for non-functioning streamgages and extrapolate discharge estimates for ungaged streams;and 4) have time-scaled drill-down visualization of real-time and forecasted flood events. Four case studies were conducted to test and validate WaVE under diverse conditions at national, regional and local levels. Results from these case studies highlight some of WaVE’s inherent strengths, limitations, and the need for further development. WaVE has the potential for being utilized on a wider basis at the local level as data become available and models are validated for converting satellite images and data records from remote sensing technologies into accurate streamflow estimates and higher resolution digital elevation models.

Evaluation of Tianji and ECMWF high-resolution precipitation forecasts for extreme rainfall event in Henan in July 2021

The extreme rainfall event of July 17 to 22, 2021 in Henan Province, China, led to severe urban waterlogging and flood disasters. This study investigated the performance of high-resolution weather forecasts in predicting this extreme event and the feasibility of weather forecast-based hydrological forecasts. To achieve this goal, high-resolution precipitation forecasts from the Tianji weather system and the forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) were evaluated with the spatial verification metrics of structure, amplitude, and location. The results showed that Tianji weather forecasts accurately predicted the amplitude of 12-h accumulated precipitation with a lead time of 12 h. The location and structure of the rainfall areas in Tianji forecasts were closer to the observations than ECMWF forecasts. Tianji hourly precipitation forecasts were also more accurate than ECMWF hourly forecasts, especially at lead times shorter than 8 h. The precipitation forecasts were used as the inputs to a hydrological model to evaluate their hydrological applications. The results showed that the runoff forecasts driven by Tianji weather forecasts could effectively predict the extreme flood event. The runoff forecasts driven by Tianji forecasts were more accurate than those driven by ECMWF forecasts in terms of amplitude and location. This study demonstrates that high-resolution weather forecasts and corresponding hydrological forecasts can provide valuable information in advance for disaster warnings and leave time for people to act on the event. The results encourage further hydrological applications of high-resolution weather forecasts, such as Tianji weather forecasts, in the future.

Skilful Forecasts of Summer Rainfall in the Yangtze River Basin from November

Variability in the East Asian summer monsoon(EASM)brings the risk of heavy flooding or drought to the Yangtze River basin,with potentially devastating impacts.Early forecasts of the likelihood of enhanced or reduced monsoon rainfall can enable better management of water and hydropower resources by decision-makers,supporting livelihoods and major economic and population centres across eastern China.This paper demonstrates that the EASM is predictable in a dynamical forecast model from the preceding November,and that this allows skilful forecasts of summer mean rainfall in the Yangtze River basin at a lead time of six months.The skill for May–June–July rainfall is of a similar magnitude to seasonal forecasts initialised in spring,although the skill in June–July–August is much weaker and not consistently significant.However,there is some evidence for enhanced skill following El Niño events.The potential for decadal-scale variability in forecast skill is also examined,although we find no evidence for significant variation.

Flood Forecasting of Malaysia Kelantan River using Support Vector Regression Technique

The rainstorm is believed to contribute flood disasters in upstream catchments,resulting in further consequences in downstream area due to rise of river water levels.Forecasting for flood water level has been challenging,present-ing complex task due to its nonlinearities and dependencies.This study proposes a support vector machine regression model,regarded as a powerful machine learning-based technique to forecast flood water levels in downstream area for different lead times.As a case study,Kelantan River in Malaysia has been selected to validate the proposed model.Four water level stations in river basin upstream were identified as input variables.A river water level in downstream area was selected as output of flood forecasting model.A comparison with several bench-marking models,including radial basis function(RBF)and nonlinear autoregres-sive with exogenous input(NARX)neural network was performed.The results demonstrated that in terms of RMSE error,NARX model was better for the proposed models.However,support vector regression(SVR)demonstrated a more consistent performance,indicated by the highest coefficient of determination value in twelve-hour period ahead of forecasting time.The findings of this study signified that SVR was more capable of addressing the long-term flood forecasting problems.

Test Study on Flood Forecast by Merging Multi Precipitation Data

Shuibuya control basin in upper reaches of Qingjiang River,Hubei Province was taken as the case. By combining grouping Z-I relation with ground meteorological rainfall station,rainfall estimation by radar was calibrated,and actual average surface rainfall in the basin was calculated.By combining genetic algorithm with neural network,the corrected AREM rainfall forecast model was established,to improve rainfall forecast accuracy by AREM. Finally,AREM rainfall forecast models before and after correction were input in Xin＇an River hydrologic model for flood forecast test. The results showed that the corrected AREM rainfall forecast model could significantly improve forecast accuracy of accumulative rainfall,and decrease range of average relative error was more than 60%. Hourly rainfall forecast accuracy was improved somewhat,but there was certain difference from actual situation. Average deterministic coefficient of AREM flood forest test before and after correction was improved from -32. 60% to 64. 38%,and relative error of flood peak decreased from 39. 00% to 25. 04%. The improved effect of deterministic coefficient was better than relative error of flood peak,and whole flood forecast accuracy was improved somewhat.

The Operational Forecasting of Total Precipitation in Flood Seasons (April to September) of 5 Years (1983-1987)

Ⅰ.INTRODUCTION We have discovered that there exists a good corresponding relationship between theanomalous axes of soil temperature at a depth of 1.6m in winter (December to February) andprecipitations in following flood season (Tang et al., 1982a). We have also designed a simplethermodynamical model and applied it to the forecasting of precipitations in the flood season(Tang et al., 1982 b,c). The practical forecast started from 1975. Before 1980, however, therewere only 40-50 stations in China for measuring the soil temperature at a 1.6m depth. Since1980, the stations have been increased to a total of about 180, but no available mean valueshad been obtained from newly added stations before 1982. Therefore the analysis and map-ping of anomalies of soil temperature was not performed until 1983, and from then on theprecision of analysis has been greatly improved. The following is the actual situation of forecast in five years from 1983 to 1987.

小型水库预报预警系统研发与应用

针对小型水库洪水预报预警研究不足问题,以构建的分布式水文模型、缺资料地区参数移植技术,以及小型水库防洪预警方式与指标体系、预警方案编制等为支撑,利用Java语言、Vue技术、ElementUI组件、EChart组件、Spring Boot、JSON数据交互等技术,设计开发了一套基于B/S架构的标准化、通用型小型水库预报预警系统。介绍了系统的设计模式及功能结构,阐述了基础信息展示、洪水预报、防洪调度、洪水预警、数据管理、基础空间地理分析等主要系统模块的设计,以及系统开发关键技术。以在安徽省的应用为例,展示了系统具备的可靠性、灵活性与实用性。

上海城区动态洪水风险图应用系统及典型暴雨内涝分析

【目的】为了提高沿海超大城市对洪涝灾害的先知先觉能力,快速预测分析洪涝潮组合影响下的淹没风险时空分布,【方法】以水文-水动力暴雨洪水分析模型为核心模块,通过与外部的气象精细化降雨预报数据库、自动雨量站实时监测数据库及实时水情、闸泵运行数据库相关联,研发了上海城区动态洪水风险图应用系统。采用由雨量站点到气象格网和水力网格的降雨二级空间融合技术,实现了模型与气象预报降雨数据的有效耦合。利用数据挖掘提取和GIS空间分析技术构建了包含10项洪水风险要素要点的城市内涝预报专报自动生成方法,实现了对内涝风险的快速一站式概览。利用系统可以对城市暴雨、河道洪水和风暴潮等单一发生或遭遇组合引起的淹没分布进行快速实时预报计算,模拟和预测城市洪涝潮灾害的有关淹没特征数据和淹没动态过程。【结果】利用系统分析了2023年6月23—24日暴雨的内涝风险分布,将模型模拟的积水区域与积水监测站、灾情直报和热线灾报的积水点进行对比,结果显示在150处对比积水点中,有129处误差不超过20 cm,占86%。模型模拟的积水空间分布与实际情况基本接近。【结论】结果表明:系统满足汛期常态化、业务化运行需求,能够为城市洪涝风险的实时动态分析和防汛指挥决策提供重要工具。

基于集成学习与深度学习的洪水径流预报研究

深度学习模型凭借其对水文因素间复杂作用的优秀处理能力,在水文预报领域得到了一定的应用,然而,针对集成学习与深度学习耦合模型的研究仍有所缺失。通过融合集成学习AdaBoost算法与深度学习Informer模型,提出了一种组合模型,称为AdaBoost-Informer模型,以提高洪水径流预报的精度。该模型以历史雨量和径流数据作为数据输入,将具备长时序依赖捕获能力的Informer作为集成学习的弱预测器,使用网格搜索法进行超参数调优,使用AdaBoost集成学习算法对弱预测器进行加权组合得到强预测器。在浙江省椒江流域的应用分析表明:对比Random Forest、AdaBoost、Transformer、Informer等模型,AdaBoost-Informer模型表现最佳,RMSE为62.08 m^(3)/s,MAE为23.83 m^(3)/s,NSE为0.980,预报合格率为100%。所提模型可有效提高洪水预报精度,为防汛抢险和防洪系统调度提供决策依据。

海河“23·7”流域性特大洪水复盘模拟

为支撑海河流域特大洪水的决策及预报预警工作,基于实测降水与流量资料,采用新安江-海河模型对海河流域2023年7月发生特大洪水的大清河2个典型小流域——紫荆关流域和漫水河流域进行了洪水模拟,并选取紫荆关流域1996—2020年5场洪水、漫水河流域1953—2016年8场洪水进行了参数率定,选取海河“23·7”流域性特大洪水进行了验证。结果表明:新安江-海河模型对“23·7”流域性特大洪水的模拟精度较高,可反映实际洪水过程,两个小流域洪峰、洪量模拟的相对误差均在20%以内,峰现时间误差均为0 h;相较于受人类活动影响小的紫荆关流域,在受人类活动影响更大的漫水河流域,新安江-海河模型的模拟效果较新安江模型的模拟效果提升更为明显。

Boruta-Optuna-XGBoost融合模型的聚驱油田产量智能预测方法研究

在聚驱油田开采时,存在向油层中加注水溶性高分子聚合物的过程,此过程包含大量参数,对油田产量具有较大影响。针对传统油田产量预测方法存在的人工计算量大、准确率低的问题。提出一种Boruta-Optuna-XGBoost融合模型对聚驱油田产量进行预测,解决传统方法存在的问题。通过Boruta特征筛选方法进行聚驱油田特征筛选,降低特征冗余,提高特征相关性,避免模型过拟合;使用Optuna超参数优化算法对XGBoost进行自适应超参数评价,提高模型精度;使用最优超参的XGBoost算法对聚驱油田产量进行回归预测,通过算法建立油田注入参数和油田月产量之间的逻辑关系模型,对聚驱油田的月产量进行预测。所提方法应用在大庆油田的实际有效数据的准确率达95%,证明了方法的有效性,能够对油田的生产效益、资源配置和可持续发展产生影响,也为数字化聚驱油田智能产量预测发展提供了新思路。

基于GPU并行计算和WebGIS的潖江蓄滞洪区洪水预报系统研究

洪水预报所采用的数值模拟涉及大量计算,模拟的结果需经多种专用软件处理后才能展示给用户,操作繁琐,无法满足蓄滞洪区防洪调度及应急抢险处置中迅速做出响应的需求。该文提出了基于GPU并行计算和WebGIS的洪水预报系统,旨在提高洪水计算的效率,延长预见期,并实现洪水演进的可视化。该系统基于最新的GPU加速的计算方法,利用GPU强大的浮点数运算能大幅提高洪水计算的效率,结合WebGIS技术,将水文-洪水演进模型的计算结果与水利底图无缝连接,以图表、图像和动画等形式直观展示洪水演变过程,使决策人员能直观地掌握蓄滞洪区洪水的演变过程,可为潖江蓄滞洪区的调度运用和防洪抢险提供帮助。