Fuzzy expert systems and GIS for cholera health risk prediction in southern Africa

Cholera (Vibrio cholerae) is endemic in southern Africa and frequently breaks out in epidemics along the eastern seaboard. Extensive resources are directed at combating cholera yet it remains a significant problem. Limited resources could better be directed to prevent outbreaks if it were possible to assess the risk of an outbreak in space and time. The CSIR in South Africa is investigating technologies to predict health risk in line with national priorities. This paper describes an early warning GIS prototype tool aimed at identifying favourable preconditions for cholera outbreaks. These preconditions were defined using an expert system approach. The variables thus identified were input into a spatial fuzzy logic model that outputs risks. The model is based on the assumption that endemic reservoirs of cholera occur and that environmental conditions, especially algal blooms, trigger Vibrio growth in the natural environment. If the preconditions are met, the subsequent spread of cholera depends mainly on socio-economic factors such as human behaviour and access to safe water supply and sanitation. This paper focuses on the environmental preconditions. The methodology described relies on capturing expert knowledge and historic data that integrate climatic and biophysical parameters with epidemiological data to produce a fuzzy surface of cholera outbreak risk potential.     

Forest fire analysis with remote sensing data

Forest fires cause major damage to the environment, human health and property, and endanger life. Fires can be monitored and analysed over large areas in a timely and cost-effective manner by using satellite sensor imagery in combination with spatial analysis as provided by Geographical Information Systems (GIS). In this study, the forest area damage caused by a large fire which occurred in the Marmaris, province of Mugla in July 1996 was analysed using satellite sensor images. Digital image processing methods, such as spectral profile analysis, vegetation indices and multispectral classification, were applied to the satellite sensor images acquired before and after the forest fire. Besides the conventional maximum likelihood classification algorithm, a multilayer feed-forward neural network architecture was also used for comparison and evaluation of its effectiveness. A GIS database was constructed from the raster (satellite sensor data), vector (the forest type and topographical maps) and ancillary data (meteorological data). The GIS is being used to develop an information and decision support system to monitor and predict forest fire activity, and to enhance fire management efficiency. This study highlights the deficiencies in the current approach to fire management and emphasizes the need for an improved method along the lines outlined.     

Integrated use of satellite remote sensing,GIS, and ground spectroscopy techniques for monitoring olive oil mill waste disposal areas on the island of Crete,Greece

Olive oil mill wastes (OOMW) constitute a major pollution factor in olive-growing regions and an important problem to be solved for the agricultural industry. Olive oil mill wastes are normally deposited in tanks, or directly into the soil or even on adjacent torrents, rivers, and lakes, posing a high risk of environmental pollution in regard to public health. This study aims to develop integrated satellite remote sensing, geographical information systems (GIS), and ground spectroscopy methodologies to detect and monitor OOMW disposal areas on the island of Crete, Greece in the Southeastern Mediterranean. More than 1000 disposal tanks were mapped through an extended global positioning system (GPS) survey that took place throughout the island. Satellite images of both high (IKONOS) and medium (Landsat 8 OLI (Operational Land Imager)) resolution were preprocessed and analysed by applying geometric, radiometric, and atmospheric corrections. A library with a spectral signature of OOMW including both different time periods and satellite sensors was developed. At the same time, ground spectroscopy campaigns were carried out and a complementary spectral signature library was developed. The narrow band reflectance of ground measurements was recalculated using the relative response filters of the corresponding satellite sensors. Both libraries were compared for their accuracy through statistical approaches and the optimum spectral range for detecting OOMW areas was estimated. Subsequently, further auxiliary image-processing techniques such as image fusion, linear spectral unmixing (LSU), false-colour composites (FCCs), image classification, and principal component analysis (PCA) were applied to satellite images to enhance OOMW patterns, and an innovative OOMW detection index for Landsat 8 was developed. In addition, several vegetation indices were applied and compared in regard to their efficiency in detecting waste ponds. Finally an integrated, semi-automatic methodology was developed in the GIS environment employing classification algorithms for the detection of waste ponds. This study highlights the potential of satellite remote sensing, GIS, and ground spectroscopy in the semi–automatic detection of OOMW disposal areas in the context of the Mediterranean landscape.     

基于自治计算的流行病传播网络建模与推断

现有的传播网络结构推断方法大都面向信息传播过程,所能处理的数据与可获得的流行病监控数据形式和特性均不相同,不适合处理具有粗粒度、时空多尺度和数据缺失等特性的流行病监控数据.针对该问题,提出了基于自治计算的流行病传播网络建模方法和网络结构推断方法.该方法采用多自治体建模传播网络结构和流行病传播过程,采用蒙特卡罗模拟结合群智能优化的反馈过程调节系统参数,以缩小模拟系统涌现行为与真实监控数据间差异为目标,改变自治体的行为,促使模拟系统向真实系统逐步演化,以此方式推断出传播网络结构及与流行病相关的主要生物学参数.采用2009年H1N1猪流感在香港爆发的真实监控数据分析验证了所提出的模型与方法的有效性和适用情况,并以香港地区流行病风险评估为例介绍了流行病传播网络推断的一种应用模式.     

A framework for predicting endemic cholera using satellite derived environmental determinants

Cholera remains one of the most prevalent water-related infections in many tropical regions of the world. Macro-environmental processes provide a natural ecological niche for Vibrio cholerae and because powerful evidence of new biotypes is emerging, it is unlikely that the bacteria will be fully eradicated. Consequently, to develop effective intervention and mitigation strategies, it is necessary to develop cholera prediction models with several months' lead time. Almost all cholera outbreaks originate near the coastal areas and cholera bacteria exhibit a strong relationship with coastal plankton. Using chlorophyll as a surrogate for plankton bloom in coastal areas, recent studies have postulated a relationship between chlorophyll and cholera incidence. Here, we show that seasonal cholera outbreaks in the Bengal Delta can be predicted two to three months in advance with an overall prediction accuracy of over 75% by using satellite-derived chlorophyll and air temperature data. Such high prediction accuracy is achievable because the two seasonal peaks of cholera are predicted using two separate models representing distinctive macro-scale environmental processes. We have shown that interannual variability of pre-monsoon cholera outbreaks can be satisfactorily explained with coastal plankton blooms and a cascade of hydro-coastal processes. Post-monsoon cholera outbreaks, on the other hand, are related to macro-scale monsoon processes and subsequent breakdown of sanitary conditions. Our results demonstrate that satellite data over a range of space and time scales are effective in developing a cholera prediction model for the Bengal Delta with several months' lead time. We anticipate our modeling framework and findings will provide the impetus to explore the utility of satellite derived macro-scale variables for cholera prediction in other cholera endemic regions.     

Estimating cholera risk from an exploratory analysis of its association with satellite-derived land surface temperatures

Occurrence and growth of Vibrio cholerae, the causative agent of cholera, is linked to modalities of elevated temperatures and heavy precipitation. Previous studies have employed temperature- and satellite-derived precipitation data to determine the risk of cholera, but predictions were limited because of the coarse spatial resolution of temperature data (about 50 km). Cholera estimation has a severe impact on those in vulnerable regions with marginal civil infrastructure and those suffering additional damage after a natural disaster. In this study, a new remote-sensing data-based algorithm is proposed that includes a pathway to associate coarse-resolution cholera prediction with high-resolution land surface temperature (LST) dataset. The algorithm allows identification and prediction of regions with elevated risk of cholera at least four weeks in advance. Additionally, it employs a hierarchical structure comprising long-term anomalous LST values to determine hot spots of potential Vibrio cholerae. The algorithm was tested in five cholera epidemic regions of Sub-Saharan Africa (Mozambique, Central African Republic, Cameroon, South Sudan, and Rwanda), with realistic accuracy in demarcating regions where human cholera cases had been reported.     

Estimation of Mediterranean forest attributes by the application of k‐NN procedures to multitemporal Landsat ETM+ images

Routine applications of nonparametric estimation methods to satellite data for assisting the creation of forest inventories in Northern European countries are stimulating interest in the possible extension of these methods to more complex Mediterranean areas. This is the subject of the current work, which presents an experiment based on the integration of remotely sensed images and sample field measurements aimed at producing forest attribute maps in central Italy. Testing was carried out in an area where 370 geocoded field plots, sampled on a single‐stage cluster design, were collected to characterize wood and non‐wood forest attributes. These ground data served to apply various k‐Nearest Neighbour (k‐NN) estimation procedures to multitemporal Landsat 7 ETM+ images in order to map major forest attributes (basal area and simulated leaf area index, LAI). More specifically, the investigation focused on evaluating the effects of using satellite images from different periods of the growing season and spectral metrics of increasing complexity. The results achieved by the examined methods are finally discussed in order to provide guidelines for possible operational utilization.     

Geomorphological changes associated with underground coal mining in the Fushun area,northeast China revealed by multitemporal satellite remote sensing data

Fushun is a famous coal-mining city in northeastern China with more than 100 years of history. Long-term underground coal mining has caused serious surface subsidence in the eastern part of the city. In this study, multitemporal and multisource satellite remote sensing data were used to detect subsidence and geomorphological changes associated with underground coal mining over a 10-year period (1996–2006). A digital elevation model (DEM) was generated through Synthetic Aperture Radar (SAR) interferometry processing using data from a pair of European Remote Sensing Satellite (ERS) SAR images acquired in 1996. In addition, a Shuttle Radar Topography Mission (SRTM) DEM obtained from data in 2000 and an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM from 2006 were used for this study. The multitemporal DEMs indicated that the maximum vertical displacement due to subsidence was around 13 m from 1996 to 2006. Multitemporal ASTER images showed that the flooded water area associated with subsidence had increased by 1.73 km² over the same time period. Field investigations and ground level measurements confirmed that the results obtained from the multitemporal remote sensing data agreed well with ground truth data. This study demonstrates that DEMs derived from multisource satellite remote sensing data can provide a powerful tool to map geomorphological changes associated with underground mining activities.     

The use of optic and radar satellite data for coastal environments

The pressure of population and its demands for fuel, for heating or industrial usage, causes destruction of the natural environment. One of the clearest examples for the accelerated degradation of changes in the forest area and coastline is in the Kilyos–Karaburun coastline located in the northern part of Istanbul metropolitan city. The open‐pit mining activities in the region are threatening the balances of both sea and land ecosystems, and cause degradation of forest areas and changes along the coastline. In this paper, the changes are examined with multisensor satellite data such as Landsat Thematic Mapper (TM), SPOT Panchromatic (P), and RADARSAT images by using different image‐processing techniques. The degrees of changes in coastline length and coastal region area were calculated, and the effectiveness of the use of different image data sets was outlined for coastal environments.     

近20多年来赣州地区稀土矿区遥感动态监测

稀土资源是现代科技所需的重要资源,由于其有很高的经济价值,稀土资源的开采活动越来越频繁,过度开采现象严重,对稀土矿区的实时监控成为保护环境资源的重要环节。遥感技术在监测土地利用变化方面已经有了完善的技术方法。相较于普遍使用的Landsat-TM/ETM+数据,我国研发的HJ卫星(中国环境与灾害监测预报小卫星)数据具有更短的重访周期,能够对稀土矿的开采进行更加有效的检测。通过结合Landsat-TM/ETM+与HJ-1/CCD数据,根据矿区植被覆盖度的变化及时监测稀土矿区活动情况,对江西定南地区20a来稀土矿区开采变化情况进行监测,并提出保护建议,为实现矿产资源的可持续发展提供理论依据。     

The use of high-resolution remote sensing for plague surveillance in Kazakhstan

Bubonic plague, caused by the bacteria Yersinia pestis, persists as a public health problem in many parts of the world, including central Kazakhstan. Bubonic plague occurs most often in humans through a flea bite, when a questing flea fails to find a rodent host. For many of the plague foci in Kazakhstan the great gerbil is the major host of plague, a social rodent well-adapted to desert environments. Intensive monitoring and prevention of plague in gerbils started in 1947, reducing the number of human cases and mortalities drastically. However, the monitoring is labour-intensive and hence expensive and is now under threat due to financial constraints. Previous research showed that the occupancy rate of the burrow systems of the great gerbil is a strong indicator for the probability of a plague outbreak. The burrow systems are around 30 m in diameter with a bare surface. This paper aims to demonstrate the automatic classification of burrow systems in satellite images using object-oriented analysis. We performed field campaigns in September 2007 and May and September 2008 and acquired corresponding QuickBird images of the first two periods. User's and producer's accuracy values of the classification reached 60 and 86%, respectively, providing proof of concept that automatic mapping of burrow systems using high-resolution satellite images is possible. Such maps, by better defining great gerbil foci, locating new or expanding foci and measuring the density of great gerbil burrow systems could play a major role in a renewed monitoring system by better directing surveillance and control efforts. Furthermore, if similar analyses can separate occupied burrow systems from empty ones, then very-high-resolution images stand to play a crucial role in plague surveillance throughout central Asia.     

Integrated data fusion and mining techniques for monitoring total organic carbon concentrations in a lake

Monitoring water quality on a near-real-time basis to address water resource management and public health concerns in coupled natural systems and the built environment is by no means an easy task. Total organic carbon (TOC) in surface waters is a known precursor of disinfection by-products in drinking water treatment such as total trihalomethanes (TTHMs), which are a suspected carcinogen and have been related to birth defects if water treatment plants cannot remove them. In this paper, an early warning system using integrated data fusion and mining (IDFM) techniques was proposed to estimate spatiotemporal distributions of TOC on a daily basis for monitoring water quality in a lake that serves as the source of a drinking water treatment plant. Landsat satellite images have high spatial resolution, but such application suffers from a long overpass interval of 16 days. On the other hand, coarse-resolution sensors with frequent revisit times, such as MODIS, are incapable of providing detailed water quality information because of low spatial resolution. This issue can be resolved by using data or sensor fusion techniques, such as IDFM, in which the high-spatial-resolution Landsat and the high-temporal-resolution MODIS images are fused and analysed by a suite of regression models to optimally produce synthetic images with both high spatial and temporal resolution. Analysis of the results using four statistical indices confirmed that the genetic programming model can accurately estimate the spatial and temporal variations of TOC concentrations in a small lake. The model entails a slight bias towards overestimating TOC, and it requires cloud-free input data for the lake. The IDFM efforts lead to the reconstruction of the spatiotemporal TOC distributions in a lake in support of healthy drinking water treatment.     

Land-use mapping and change detection in a coal mining area - a case study in the Jharia coalfield,India

In an area like the Jharia coalfield (JCF), where extensive and rapid underground and opencast mining is going on continuously, land-use studies are of paramount importance. This paper discusses the remote sensing-GIS techniques used for identification of various land-use classes on satellite imagery and enhanced products and identification of time-sequential changes in land-use patterns. The various land-use classes, recognised from satellite image data and field surveys, are dense vegetation, sparse vegetation, fire, opencast mining (coal), overburden dump, subsidence and barren wasteland, settlement, transport network, river and water pond. A number of image processing operations have been carried out on remote sensing data for enhancing land-use patterns. It has been found that Landsat TM false colour composites (FCC) of bands 4, 3 and 2; FCC of bands 7, 5 and 3; FCC of bands 5, 4 and 2 and ratio images provide very useful information for land-use mapping. The normalised difference vegetation index (NDVI) images have been used for vegetation studies. Image characters of various land-use classes on black-and-white and enhanced colour products have been tabulated. Land-use maps of selected windows have been prepared and examples given. Time-sequential surface changes that have occurred in the JCF since 1975 and particularly between November 1990 to November 1994 have been investigated. For change detection analysis, data manipulation in several steps involving preprocessing, processing and colour display have been carried out. Land-use changes have been detected by (a) image differencing, (b) image ratioing, and (c) differencing of NDVI images. It is inferred from the remote sensing images that extensive mining, establishment of communication networks, expansion of settlements, decrease in the vegetation cover etc., have remodelled the face of the JCF.     

Detection of temporary flooded areas with potential floodwater mosquito production using imaging radar

Temporarily flooded areas can produce enormous numbers of floodwater mosquitoes, causing tremendous nuisance to people living in the vicinity. The aim of this study is to develop a remote-sensing method for detecting temporary flooded areas that can produce floodwater mosquitoes. For this objective, synthetic aperture radar (SAR) imagery from the European Remote Sensing satellite (ERS-2) and the Environmental Satellite (Envisat) are chosen. The images cover both flooded and dry periods around Lake Färnebofjärden, located in the lowlands of the River Dalälven, central Sweden, during the vegetation season between 2000 and 2006. Unsupervised classification and principal component analysis (PCA) are tested as methods for detecting floodwater mosquito production sites. In the unsupervised classification experiment, four types of images are tested. The classification of a synthetic colour image gives the best result with an overall accuracy of 85.7% and a kappa value of 0.7, as well as a 46% detection rate of field-mapped flooded areas. PCA is performed on a data set of 16 time series radar images. The resulting principal component (PC) bands provide information about flooding probability as well as vegetation structures. Regular flooding increases the probability for an area to provide breeding sites for floodwater mosquitoes. Thus, this approach will be very useful in estimating the risk of floodwater mosquito establishment.     

基于VPRS的渔场老化风险规则挖掘

在研究如何更好地开发利用网箱渔场的过程中，人们已经认识到污染所引起的环境风险和其他影响因子。对于网箱渔场的风险评估，粗糙集理论是一种数据推理的有力工具。文章介绍了利用变精度粗糙集进行风险规则挖掘的一种方法。论述了Pawlak粗糙集和变精度粗糙集的有关概念，量化描述了渔业环境中老化程度和风险等级，给出了基于精度粗糙集的网箱渔场老化风险规则的挖掘方法。     

医学数据挖掘系统研究——糖尿病并发症流行病学知识发现

针对流行病学研究的特点,论文提出计算机辅助医学数据挖掘系统构架,以糖尿病并发症为研究实例,探讨医学数据的冗余性消除、规范化储存、知识归纳及可视化表达等问题。以天津总医院3022例普查数据为研究对象,尝试解决用计算机实现糖尿病并发症这类定性数据的定量化数据挖掘和知识发现。通过对于43种并发症的定性数据挖掘,可以发现诸如高血脂、冠心病、高血压、脑血管病等具有明显并发倾向的知识规则18条。同时,采用知识树方式和决策树等方法实现知识规则的可视化表达。基于数据挖掘和知识发现计算机辅助医学数据挖掘系统能够对现有病历数据库中数据进行自动分析并且提供有价值医学知识,特别适合流行病学分析和全民健康评估,因此与社区医疗和医院HIS系统结合是未来一个非常现实的发展方向。     

Comparative use of processed satellite images in remote sensing of mass movements: Lebanon as a case study

Mass movements (MM) represent a serious threat to human life and activities in most mountainous areas. However, due to the rugged nature of such terrain, it is often difficult to detect such phenomena in remote areas. Hence, satellite imagery offers many attractions for the examination of MM in such environments, especially in less developed nations in which resources are stretched and levels of environmental information limited. There is a need to ensure that the techniques and images used are effective, reliable, and cheap in terms of the amount and accuracy of data that can be extracted. Taking Lebanon as a case study, this paper compares the applicability of different satellite data sensors (Landsat TM (Thematic Mapper), IRS (Indian Remote Sensing Satellite), SPOT4 (Système Probatoire pour l’Observation de la Terre)) and preferred image‐processing techniques (False Colour Composite ‘FCC’, pan‐sharpen, principal‐component analysis ‘PCA’, Anaglyph) for the mapping of MM recognized as landslides, rock and debris falls, and earth flows. Results from the imagery have been validated by field surveys and analysis of IKONOS imagery acquired in some locations witnessing major MM during long periods. Then, levels of accuracies of detected MM from satellite imageries were plotted. This study has demonstrated that the anaglyph produced from the two panchromatic stereo‐pairs SPOT4 images remains the most effective tool setting the needed 3D properties for visual interpretation and showing a maximum accuracy level of 67%. The PCA pan‐sharpened Landsat TM‐IRS image gave better results in detecting MM, among other processing techniques, with a maximum accuracy level of 62%.

     

多源时序国产卫星影像的森林火灾动态监测

四川省木里县及周边林区是全国林火最为高发和易发区之一,近两年连续发生了扑火人员重大伤亡的事件。利用时序国产卫星影像、无人机影像和现场勘查数据等,从监测火灾蔓延时空过程的角度,对该区林火热点进行了动态监测,并分析了重点火场火灾发展过程,结果表明：以国产GF-4卫星影像为主,辅助以2 m/8 m光学卫星星座影像,可较好地监测林火热点;研究提出林火热点判定阈值为白天亮温值T≥360 K或夜间亮温值T≥330 K;监测发现了该区3月30日至4月6日间共6处火场的25次林火事件,并重点反演了①号木里和②号西昌火灾发展的时空过程。通过将卫星监测热点与现场勘查热点、无人机影像解译热点对比,表明在火灾早期和中期卫星林火热点监测精度可达89%。建议利用时序国产多源卫星影像对该区林火进行持续监测,并结合权威部门现场勘查数据适时发布预警信息,避免造成重大生命财产损失。     

Improving accuracy of long-term land-use change in coal mining areas using wavelets and Support Vector Machines

Old satellite sensors lack several quality features such as high spatial and spectral resolution. For accurate long-term change detection, improvement of the quality of old satellite images is required. In the present study, we used two wavelet-based image enhancement techniques [(discrete wavelet transform (DWT) and dual tree-complex wavelet transform (DT-CWT)] for improving the quality of Landsat 2 data of 1975 and Landsat 8 data of 2015 to study the impact of coal mining on land use change over a period of four decades. The enhanced images were subjected to land-use classification using Support Vector Machines. Land-use classification accuracy was measured using confusion matrix-based accuracy assessment. Accuracy assessment revealed that the overall classification accuracy of DWT enhanced images was 82.10% for the year 1975 and 88.46% for the year 2015. The overall classification accuracy of DT-CWT enhanced images was 85.71% for the year 1975 and 88.54% for the year 2015. The results of change detection revealed that the total areal coverage of dense vegetation increased by 65%, indicating that the rate of land degradation had slowed down due to the legislative and policy changes to promote sustainable development in coal mining after 1986.     

An agent-based modeling approach applied to the spread of cholera

Cholera is an intestinal disease and is characterized by diarrhea and severe dehydration. While cholera has mainly been eliminated in regions that can provide clean water, adequate hygiene and proper sanitation; it remains a constant threat in many parts of Africa and Asia. Within this paper, we develop an agent-based model that explores the spread of cholera in the Dadaab refugee camp in Kenya. Poor sanitation and housing conditions contribute to frequent incidents of cholera outbreaks within this camp. We model the spread of cholera by explicitly representing the interaction between humans and their environment, and the spread of the epidemic using a Susceptible-Exposed-Infected-Recovered model. Results from the model show that the spread of cholera grows radially from contaminated water sources and seasonal rains can cause the emergence of cholera outbreaks. This modeling effort highlights the potential of agent-based modeling to explore the spread of cholera in a humanitarian context.