Flood Disaster Monitoring Research Articles

Research on Flood Disaster Monitoring and Early Warning Technology Based on UAV Remote Sensing and AI Algorithm

This paper, based on UAV far-flung sensing science and synthetic brain algorithm, studies the monitoring and early warning technological know-how of flood catastrophes. Firstly, the far-flung sensing monitoring science of flood catastrophes is summarized. Then, blended with geographic records machine (GIS) technology, the UAV far-flung sensing science software in flood catastrophe monitoring is analyzed. Finally, combining the micro-UAV technological know-how and AI technology, the key applied sciences of the micro-UAV flood catastrophe monitoring and early warning device primarily based on AI imaginative and prescient are studied, along with the lookup of the micro-UAV key gadget and the lookup of AI imaginative and prescient key technology. The key device lookup of micro-UAV is the definition of mindset. Mindset A mindset lookup on key applied sciences of AI, imaginative and prescient broadly speaking, expounds the precept of CNN and introduces Faster RCNN. It lays the groundwork for the later lookup on flood catastrophe monitoring and early warning technological know-how primarily based on UAV far-off sensing and AI algorithms.

Artificial Intelligence Based Flood and Landslide Disaster Monitoring and Dynamic Numerical Prediction System

Monitoring and dynamic numerical prediction of flood and landslide disasters are of great significance for reducing disaster risks, ensuring urban safety and stable development. The existing systems have limitations in real-time and dynamic performance, making it difficult to provide effective support for disaster prevention and management. To improve the efficiency of disaster monitoring, this article combined artificial intelligence (AI) to conduct in-depth research on the construction of flood and landslide disaster monitoring and dynamic numerical prediction systems. This article first analyzed the system functional requirements, and then designed the system architecture based on this, dividing it into three levels: user interface layer, application layer, and data layer. Finally, the BP (Back Propagation) neural network algorithm and dynamic numerical model were utilized to analyze the monitoring and dynamic numerical prediction of flood and landslide disasters. To verify the effectiveness of the system, this article compared it with GIS (Geographic Information System) based methods, and conducted testing and analysis on the system from monitoring errors, real-time monitoring, numerical simulation, and several levels. The results showed that in terms of real-time monitoring, compared with GIS based disaster prediction systems, the average response time of the AI based disaster monitoring and prediction system in this paper was shortened by 1.36 milliseconds. The conclusion indicated that the AI-based flood and landslide disaster monitoring and dynamic numerical prediction system in this article could effectively improve the efficiency of disaster monitoring and early warning, and help promote the efficient and intelligent development of disaster prevention

Change Detection in SAR Images through Clustering Fusion Algorithm and Deep Neural Networks

The detection of changes in synthetic aperture radar (SAR) images based on deep learning has been widely used in landslides detection, flood disaster monitoring, and other fields of change detection due to its high classification accuracy. However, the inherent speckle noise in SAR images restricts the performance of existing SAR image change detection algorithms by clustering analysis. Therefore, this paper proposes a novel method for SAR image change detection based on clustering fusion and deep neural networks. We first used hierarchical fuzzy c-means clustering (HFCM ) to process two different images to obtain HFCM classification results. Then a fusion strategy is designed to obtain the fused image from the two HFCM classified images as the pre-classification result. Furthermore, a lightweight deep neural network com posed of a decomposition convolution module and an auxiliary classification module was proposed; the former module could reduce network parameters by 28%, and the latter could reduce network parameters by 33.3%. To improve the recognition performance of the network, the classification layer was replaced by the regression layer at the outcome of the network. By comparing the experiments of different methods on five data sets, the performance of our proposed method is superior.

An Earth Observation Task Representation Model Supporting Dynamic Demand for Flood Disaster Monitoring and Management

A comprehensive, accurate, and timely expression of earth observation (EO) tasks is the primary prerequisite for the response to and the emergency monitoring of disasters, especially floods. However, the existing information model does not fully satisfy the demand for a fine-grain observation expression of EO task, which results in the absence of task process management. The current study proposed an EO task representation model based on meta-object facility to address this problem. The model not only describes the static information of a task, but it also defines the dynamics of an observation task by introducing a functional metamodel. This metamodel describes the full life cycle of a task; it comprises five process methods: birth, separation, combination, updating, and extinction. An earth observation task modeling and management prototype system (EO-TMMS) for conducting a remote sensing satellite sensor observation task representation experiment on flooding was developed. In accordance with the results, the proposed model can describe various EO tasks demands and the full life cycle process of an EO task. Compared with other typical observation task information models, the proposed model satisfies the dynamic and fine-grain process representation of EO tasks, which can improve the efficiency of EO sensor utilization.

A Novel Water Index Fusing SAR and Optical Imagery (SOWI)

Continuous and accurate acquisitions of surface water distribution are important for water resources evaluation, especially high-precision flood monitoring. During surface water extraction, optical imagery is strongly affected by clouds, while synthetic aperture radar (SAR) imagery is easily influenced by numerous physical factors; thus, the water extraction method based on single-sensor imagery cannot obtain high-precision water range under multiple scenarios. Here, we integrated the radar backscattering coefficient of ground objects into the Normalized Difference Water Index to construct a novel SAR and Optical Imagery Water Index (SOWI), and the water ranges of five study areas were extracted. We compared two previous automatic extraction methods based on single-sensor imagery and evaluated the accuracy of the extraction results. Compared with using optical and SAR imagery alone, the accuracy of all five regions was improved by up to 1–18%. The fusion-derived products resulted in user accuracies ranging 95–99% and Kappa coefficients varying by 85–97%. SOWI was then applied to monitor the 2021 heavy rainfall-induced Henan Province flood disaster, obtaining a time-series change diagram of flood inundation range. Our results verify SOWI’s continuous high-precision monitoring capability to accurately identify waterbodies beneath clouds and algal blooms. By reducing random noise, the defects of SAR are improved and the roughness of water boundaries is overcome. SOWI is suitable for high-precision water extraction in myriad scenarios, and has great potential for use in flood disaster monitoring and water resources statistics.

Water extraction from SAR images based on improved geodesic active contour.

The rapid and accurate acquisition of water body information is of great significance to water resource investigation, flood disaster monitoring, ecological environment protection, and other fields. In this paper, the water boundary is optimized and extracted from single-polarization SAR images based on an improved geodesic active contour model (IMGAC). Firstly, the rough extraction results of the water body were obtained according to the adaptive threshold, and then a narrowband model was established, and the signed pressure force (SPF) function was introduced into the geodesic active contour (GAC) model. Finally, the optimal water boundary was obtained through continuous iteration. Compared with the active contour (AC) model without edge and the traditional GAC model, the results show that the IMGAC model proposed in this paper can reduce the calculation efficiency and improve the accuracy of water boundary detection. The F-measure index was used to evaluate the extraction accuracy of the three methods. IMGAC method had the highest extraction accuracy, which was 96.43%. The kappa coefficient reached 0.929. The F-measure index was 96.20%. Our study can provide a reference for water extraction and water boundary optimization.

Dynamic monitoring of flood disaster based on remote sensing data cube

High-frequency dynamic monitoring of flood disaster using remote sensing technology is crucial for accurate decision-making of disaster prevention and relief. However, the current trade-off between spatial and temporal resolution of remote sensing sensors limits their application in high-frequency dynamic monitoring of flood disaster. To deal with this challenge, in this study, we presented an approach to conduct high-frequency dynamic monitoring of flood disaster based on remote sensing data cube with high spatial and temporal resolution. The presented approach included two steps: a, removing the cloudy areas in original MODIS data to construct the cloud-free MODIS data cube by using the information provided by GPM rainfall data; b, fusing the cloud-free MODIS data cube and Landsat-8 data by using the spatiotemporal data fusion algorithm to construct the high spatiotemporal resolution (Landsat-like) data cube. The approach was tested by conducting high-frequency dynamic monitoring of flood disaster occurred in Henan Province, PR China. Our study had three main results: (1) the presented cloud removal algorithm in the first step was able to retain flood information and performed well in removing clouds during consecutive rainy days. The differences between cloud-free MODIS data cube and original MODIS data were small and the cloud-free MODIS data cube could be used for constructing high spatiotemporal resolution data cube. (2) Our presented approach could be used to conduct high-frequency dynamic monitoring of flood disaster. (3) Testing results showed that there were two floods occurred in the study area from July 17, 2021, to October 16, 2021; the first flood occurred from July 17, 2021, to September 15, 2021, with maximum affected area of 668.36 km2; the second flood occurred from September 18, 2021, to October 16, 2021, with maximum affected area of 303.88 km2. Our study provides a general approach for high-frequency monitoring of flood disaster.

Flood Disaster Monitoring and Emergency Assessment Based on Multi-Source Remote Sensing Observations

Flood disasters are one of the most serious meteorological disasters in China. With the rapid development of information technology, individual monitoring tools could not meet the need for flood disaster monitoring. Therefore, a new integrated air-space-ground method, based on combined satellite remote sensing, unmanned aerial vehicle remote sensing and field measurement technology, has been proposed to monitor and assess flood disasters caused by a dam failure in Poyang County, Jiangxi Province. In this paper, based on an air-space-ground investigation system, the general flooded areas, severely affected areas, and more severely affected areas were 53.18 km2, 12.61 km2 and 6.98 km2, respectively. The size of the dam break gap was about 65 m and 34.7 m on 22 and 23 June. The assessment precision was better than 98%, and the root mean square error (RMSE) was 0.86 m. The method could meet the needs for flood disaster information at different spatiotemporal scales, such as macro scale, medium scale and local small scale. The integrated monitoring of flood disasters was carried out to provide the whole process and all-round information on flood evolution dynamics, the disaster development process for flood disaster monitoring and emergency assessment, and holographic information for emergency rescue and disaster reduction, as well as to meet the need for different temporal and spatial scales of information in the process of disaster emergencies.

Machine Learning Based Analysis of Real-Time Geographical of RS Spatio-Temporal Data

Flood disasters can be reliably monitored using remote sensing photos with great spatiotemporal resolution. However, satellite revisit periods and extreme weather limit the use of high spatial resolution images. As a result, this research provides a method for combining Landsat and MODIS pictures to produce high spatiotemporal imagery for flood disaster monitoring. Using the spatial and temporal adaptive reflectance fusion model (STARFM), the spatial and temporal reflectance unmixing model (STRUM), and three prominent algorithms of flexible spatiotemporal data fusion (FSDAF), Landsat fusion images are created by fusing MODIS and Landsat images. Then, to extract flood information, utilize a support vector machine (SVM) to classify the fusion images. Assess the accuracy of your work. Experimental results suggest that the three spatio-temporal fusion algorithms may be used to effectively monitor floods, with FSDAF's fusion results outperforming STARFM and STRUM in both study areas. The overall flood classification accuracy of the three STARFM, STRUM, and FSDAF algorithms in the Gwydir research region is 0.89, 0.90, and 0.91, respectively, with Kappa coefficients of 0.63, 0.64, and 0.67. The flood classification accuracy of the three fusion algorithms in the New Orleans research region is 0.90, 0.89, and 0.91, with Kappa values of 0.77, 0.76, and 0.81, respectively. The spatio-temporal fusion technique can be used to successfully monitor floods, according to this study.

Outlier Detection Method for Flash Flood Disaster Monitoring Data based on Information Entropy

In the research of flash flood disaster monitoring and early warning, the Internet of Things is widely used in real-time information collection. There are abnormal situations such as noise, repetition and errors in a large amount of data collected by sensors, which will lead to false alarm, lower prediction accuracy and other problems. Aiming at the characteristic that outliers flow of sensors will cause obvious fluctuation of information entropy, this paper proposes a local outlier detection method based on information entropy and optimized by sliding window and LOF (Local Outlier Factor). This method can be used to improve the data quality, thus improving the accuracy of disaster prediction. The method is applied to data stream processing of water sensor, and the experimental results show that the method can accurately detect outliers. Compared with the existing detection methods that only use data distance to determine, the test positive rate is improved and the false positive rate is reduced.

Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China

Flood is a kind of natural disaster that is extremely harmful and occurs frequently. To reduce losses caused by the hazards, it is urgent to monitor the disaster area timely and carry out rescue operations efficiently. However, conventional space observers cannot achieve sufficient spatiotemporal resolution. As spaceborne GNSS-R technique can observe the Earth’s surface with high temporal and spatial resolutions; and it is expected to provide a new solution to the problem of flood hazards. During 19–21 July 2021, Henan province, China, suffered a catastrophic flood and urban waterlogging. In order to test the feasibility of flood disaster monitoring on a daily basis by using GNSS-R observations, the CYGNSS (Cyclone Global Navigation Satellite System) Level 1 Science Data were processed for a few days before and after the flood to obtain surface reflectivity by correcting the analog power. Afterwards, the flood was monitored and mapped daily based on the analysis of changes in surface reflectivity from spaceborne GNSS-R mission. The results were evaluated based on the image from MODIS (Moderate Resolution Imaging Spectroradiometer) data, and compared with the observations of SMAP (Soil Moisture Active Passive) in the same period. The results show that the area with high CYGNSS reflectivity corresponds to the flooded area monitored by MODIS, and it is also in high agreement with SMAP. Moreover, CYGNSS can achieve more detailed mapping and quantification of the inundated area and the duration of the flood, respectively, in line with the specific situation of the flood. Thus, spaceborne GNSS-R technology can be used as a method to monitor floods with high temporal resolution.

SAR image water extraction using the attention U-net and multi-scale level set method: flood monitoring in South China in 2020 as a test case

ABSTRACT Level set method has been extensively used for image segmentation, which is a key technology of water extraction. However, one of the problems of the level-set method is how to find the appropriate initial surface parameters, which will affect the accuracy and speed of level set evolution. Recently, the semantic segmentation based on deep learning has opened the exciting research possibilities. In addition, the Convolutional Neural Network (CNN) has shown a strong feature representation capability. Therefore, in this paper, the CNN method is used to obtain the initial SAR image segmentation map to provide deep a priori information for the zero-level set curve, which only needs to describe the general outline of the water body, rather than the accurate edges. Compared with the traditional circular and rectangular zero-level set initialization method, this method can converge to the edge of the water body faster and more precisely; it will not fall into the local minimum value and be able to obtain accurate segmentation results. The effectiveness of the proposed method is demonstrated by the experimental results of flood disaster monitoring in South China in 2020.

A novel ENSO monitoring index and its potential for drought application

The El Niño–Southern Oscillation (ENSO) changes the air flow in the tropics, causing global droughts, floods, typhoons, and other natural disasters. Therefore, monitoring and forecasting of ENSO events effectively play an important role in disaster prevention and mitigation. This paper proposes a novel ENSO monitoring index using GNSS-derived zenith tropospheric delay (ZTD) and temperature (T), which is called the standardized ENSO monitoring index (SEMI), and validated its performance by introducing the standardized precipitation evapotranspiration index (SPEI). Multichannel singular spectral analysis (MSSA) and Lomb-Scargle periodogram are first applied to determine the periodic signals of ZTD and T. Empirical Orthogonal Function (EOF) is then used to analyze the spatio-temporal characteristics of ZTD and T. Finally, the response thresholds of ZTD and T to ENSO events are determined using the moving window correlation analysis (MWCA) method, and the SEMI is established based on the relationship between abnormal changes of sea surface temperature anomaly (SSTa) and ZTD/T. ZTD and T of 97 GNSS stations and the corresponding SPEI over the period of 2006–2016 in Taiwan province has been obtained to validate the capacity of the proposed SEMI for ENSO monitoring. Experimental results reveal that (1) annual and semi-annual periods existed in ZTD and T with the values of 365.25 and 182.625 days, respectively. (2) ZTD and T have good correlations with SSTa, and the coefficients are 0.64 and 0.70, respectively. (3) The response thresholds of ZTD and T to ENSO events in Taiwan province are [−1.13 °C, 0.84 °C] and [−1.09 °C, 0.69 °C], respectively. (4) The proposed SEMI has a good relationship with SSTa with a correlation coefficient of 0.77 and has been proven to have good ENSO monitoring capacity by comparing with SPEI with a correlation coefficient of 0.83. This result is of great significance for drought and flood disaster monitoring by the GNSS technique.

Real time flood disaster monitoring based on energy efficient ensemble clustering mechanism in wireless sensor network

AbstractIn this article, energy efficient ensemble clustering method (EECM) with black widow optimization (EECM‐BWO) algorithm is proposed for effective data transmission with the help of real time flood disaster monitoring wireless sensor network (WSN). Initially, unified scalable ensemble clustering algorithm based on ensemble generation and consensus function is proposed for selecting the optimal routing path among the node using BWO algorithm. Then, biologically inspired routing black widow spiders optimization algorithm is proposed to trade off the nodes energy level, self‐organization, and self‐configuration in the WSN. The simulation is performed using NS2 simulator for validating the performance of the proposed EECM‐BWO method. Here, in node, low delay achieves 24.07%, 72.58%, 51.36%, 81.75%, 77.74%, high packet delivery ratio achieves 70.83%, 53.93%, 90.23%, 43.58%, 24.58%, low packet drop attains 77.93%, 72.76%, 61.56%, 51.87%, 34.35%, low energy consumption attains 75.9%, 52.94%, 65.81%, 58%, 41.2% compared with existing energy‐efficient clustering approach consolidated game theory as well as dual‐cluster‐head mode for WSNs energy‐aware clustering by cuckoo optimization approach (EECM‐COA), energy‐aware clustering‐based routing using multi‐path reliable transmission with routing and control board (EECM‐RCB‐MRT), adaptive repair algorithm with temporally ordered routing algorithms for flood control strategy (EECM‐AR‐TORA‐FCS), passive multi‐hop clustering algorithm (EECM‐PMC), dynamic source routing protocol based on genetic algorithm‐bacterial foraging optimization (DSR‐GA‐BFO).

An Efficient Decision Support System for Flood Inundation Management Using Intermittent Remote-Sensing Data

Timely acquisition of spatial flood distribution is an essential basis for flood-disaster monitoring and management. Remote-sensing data have been widely used in water-body surveys. However, due to the cloudy weather and complex geomorphic environment, the inability to receive remote-sensing images throughout the day has resulted in some data being missing and unable to provide dynamic and continuous flood inundation process data. To fully and effectively use remote-sensing data, we developed a new decision support system for integrated flood inundation management based on limited and intermittent remote-sensing data. Firstly, we established a new multi-scale water-extraction convolutional neural network named DEU-Net to extract water from remote-sensing images automatically. A specific datasets training method was created for typical region types to separate the water body from the confusing surface features more accurately. Secondly, we built a waterfront contour active tracking model to implicitly describe the flood movement interface. In this way, the flooding process was converted into the numerical solution of the partial differential equation of the boundary function. Space upwind difference format and the time Euler difference format were used to perform the numerical solution. Finally, we established seven indicators that considered regional characteristics and flood-inundation attributes to evaluate flood-disaster losses. The cloud model using the entropy weight method was introduced to account for uncertainties in various parameters. In the end, a decision support system realizing the flood losses risk visualization was developed by using the ArcGIS application programming interface (API). To verify the effectiveness of the model constructed in this paper, we conducted numerical experiments on the model’s performance through comparative experiments based on a laboratory scale and actual scale, respectively. The results were as follows: (1) The DEU-Net method had a better capability to accurately extract various water bodies, such as urban water bodies, open-air ponds, plateau lakes etc., than the other comparison methods. (2) The simulation results of the active tracking model had good temporal and spatial consistency with the image extraction results and actual statistical data compared with the synthetic observation data. (3) The application results showed that the system has high computational efficiency and noticeable visualization effects. The research results may provide a scientific basis for the emergency-response decision-making of flood disasters, especially in data-sparse regions.

Flood disaster monitoring based on Sentinel-1 data: A case study of Sihu Basin and Huaibei Plain, China

Summer floods occur frequently in many regions of China, affecting economic development and social stability. Remote sensing is a new technique in disaster monitoring. In this study, the Sihu Basin in Hubei Province of China and the Huaibei Plain in Anhui Province of China were selected as the study areas. Thresholds of backscattering coefficients in the decision tree method were calculated with the histogram analysis method, and flood disaster monitoring in the two study areas was conducted with the threshold method using Sentinel-1 satellite images. Through satellite-based flood disaster monitoring, the flooded maps and the areas of expanded water bodies and flooded crops were derived. The satellite-based monitoring maps were derived by comparing the expanded area of images during a flood disaster with that before the disaster. The difference in spatiotemporal distribution of flood disasters in these two regions was analyzed. The results showed that flood disasters in the Sihu Basin occurred frequently in June and July, and flood disasters in the Huaibei Plain mostly occurred in August, with a high interannual variability. Flood disasters in the Sihu Basin were usually widespread, and the affected area was between Changhu and Honghu lakes. The Huaibei Plain was affected by scattered disasters. The annual mean percentages of flooded crop area were 14.91% and 3.74% in the Sihu Basin and Huaibei Plain, respectively. The accuracies of the extracted flooded area in the Sihu Basin in 2016 and 2017 were 96.20% and 95.19%, respectively.

The application of big data in the analysis of the impact of urban floods: A case study of Qianshan River Basin

In recent years, with the acceleration of climate change and urban modernization, the flood risk of economic and social development has increased. The problem of urban flood disasters needs to be solved urgently. It is particularly important to conduct flood disaster loss assessment research for further flood control and disaster reduction, and emerging big data have opened a new direction for solving the problem of urban flooding. In this study, the Qianshan River Basin in Guangdong Province was used as an example. The TELEMAC model was used to simulate the inundation process under the design rainstorms of 50a and 100a in the Qianshan River Basin. Based on the heat map service, route planning service, and search service functions of Baidu Maps and Amap open platforms, this study used crawler technology to integrate multi-source data, including traffic survey data, geospatial data, population heat map data at different times (day/night) on weekdays and non-weekdays, and point of interest (POI) data from various industries. A dynamic assessment model of flood disaster loss for traffic, population, and POIs was constructed. The research results could greatly improve the timeliness and accuracy of urban flood disaster analysis, early warning, monitoring, and disaster risk assessment.

Potential of a SAR Small-Satellite Constellation for Rapid Monitoring of Flood Extent

Constellations of small satellites equipped with synthetic aperture radar (SAR) payloads can realize observations in short time intervals independently from daylight and weather conditions and this technology is now in the early stages of development. This tool would greatly contribute to rapid flood monitoring, which is usually one of the main missions in upcoming plans, but few studies have focused on this potential application and a required observation performance for flood disaster monitoring has been unclear. In this study, we propose an unprecedented method for investigating how flood extents would be temporally and spatially observed with a SAR small-satellite constellation and for evaluating that observation performance via an original index. The virtual experiments of flood monitoring with designed constellations were conducted using two case studies of flood events in Japan. Experimental results showed that a SAR small-satellite constellation with sun-synchronous orbit at 570 km altitude, 30-km swath, 15–30° incidence angle, and 20 satellites can achieve 87% acquisition of cumulative flood extent in total observations. There is a difference between the results of observation performance in two cases because of each flood’s characteristics and a SAR satellite’s observation system, which implies the necessity of individual assessments for various types of rivers.

Application of Fengyun-4 Satellite to Flood Disaster Monitoring through a Rapid Multi-Temporal Synthesis Approach

Fengyun-4A (FY-4A) belongs to the second generation of geostationary meteorological satellite series in China. Its observations with high frequency and resolution provide a better data basis for monitoring of extreme weather such as sudden flood disasters. In this study, the flood disasters occurred in Bangladesh, India, and some other areas of South Asia in August 2018 were investigated by using a rapid multi-temporal synthesis approach for the first time for removal of thick clouds in FY-4A images. The maximum between-class variance algorithm (OTSU; developed by Otsu in 2007) and linear spectral unmixing methods are used to extract the water area of flood disasters. The accuracy verification shows that the water area of flood disasters extracted from FY-4A is highly correlated with that from the high-resolution satellite datasets Gaofen-1 (GF-1) and Sentinel-1A, with the square correlation coefficient R2 reaching 0.9966. The average extraction accuracy of FY-4A is over 90%. With the rapid multi-temporal synthesis approach used in flood disaster monitoring with FY-4A satellite data, advantages of the wide coverage, fast acquisition, and strong timeliness with geostationary meteorological satellites are effectively combined. Through the synthesis of multi-temporal images of the flood water body, the influence of clouds is effectively eliminated, which is of great significance for the real-time flood monitoring. This also provides an important service guarantee for the disaster prevention and reduction as well as economic and social development in China and the Asia–Pacific region.

Spatiotemporal variation analysis of regional flood disaster resilience capability using an improved projection pursuit model based on the wind-driven optimization algorithm

Abstract Due to the weak methods available for evaluation of the resilience of regional flood disaster systems and the lack of research on the driving mechanism of resilience, by exploring the principles of regional flood disaster resilience and constructing a suitable evaluation index system, the wind driven optimization (WDO) algorithm was introduced, and an improved projection pursuit (PP) evaluation model of flood disaster resilience was proposed. Twelve farms under the Heilongjiang Agricultural Reclamation Hongxinglong Administration Bureau were included in the research area. A total of 43 primary indicators were selected from four criteria to describe the natural environment, culture, society, economic development and flood control technologies. The R clustering factor analysis method was used to determine 15 optimal indexes. The improved PP model based on the WDO algorithm (WDO-PP) was used to evaluate the flood disaster resilience of 12 farms. The results showed that the number of farms with a level IV rating on flood resilience decreased from 25% to 8.3% from 2002 to 2009. In 2009–2016, with the exception of the Bawuer and Shuguang farms, the flood disaster resilience index decreased, and that of the remaining farms increased. In 2002–2016, the Wujiuqi, Shuangyashan, Shuguang and Hongqiling farms in the central region of the Hongxinglong Administration Bureau were less resilient to disasters, and the farms that responded better to flood disasters were mainly located in the eastern or western Hongxinglong Administration Bureau near a river. Further analysis shows that the forest coverage rate, paddy field coverage ratio, shelter forest area ratio, proportion of primary industry, agricultural water use efficiency, and irrigation and drainage capacity were the key drivers of the flood disaster resilience in the Hongxinglong Management Bureau. Based on the Rastrigin and Schaffer functions, the results show that the success rate of the WDO algorithm is 100% over 10 iterations of the optimization calculation of the test function, while the success rate of the other two algorithms is relatively inadequate; however, in terms of value and standard deviation, both are better than adaptive particle swarm optimization (APSO) and adaptive genetic algorithm (AGA) algorithms. Moreover, in the convergence curve, the WDO algorithm converges fast, the number of iterations can achieve the optimal effect on average 3–5 times, and the AGA and APSO algorithms need more than 40 iterations to achieve the best-seeking effect. Taking the index of agricultural water use efficiency in Bawuer farm as an example, the index weight is greater than 60% and the utilization rate of agricultural water is more than 98%, which is closer to reality. Therefore, the evaluation results of the flood disaster resilience evaluation model proposed in this study are more accurate: WDO-PP>(adaptive genetic algorithm) AGA-PP>(adaptive particle swarm optimization algorithm)APSO-PP. In conclusion, the WDO-PP model has certain reference value for flood disaster recovery, monitoring and early warning.