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1.
Modeling the suitability of land to support specific land uses is an important and common GIS application. Three classic models, specifically pass/fail screening, graduated screening and weighted linear combination, are examined within a more general framework defined by fuzzy logic theory. The rationale underlying each model is explained using the concepts of fuzzy intersections, fuzzy unions and fuzzy averaging operations. These fuzzy implementations of the three classic models are then operationalized and used to analyze the distribution of kudzu in the conterminous United States. The fuzzy models achieve better predictive accuracies than their classic counterparts. By incorporating fuzzy suitability membership of environment factors in the modeling process, these fuzzy models also produce more informative fuzzy suitability maps. Through a defuzzification process, these fuzzy maps can be converted into conventional maps with clearly defined boundaries, suitable for use by individuals uncomfortable with fuzzy results. 相似文献
2.
Federico Cervi Matteo Berti Lisa Borgatti Francesco Ronchetti Federica Manenti Alessandro Corsini 《Landslides》2010,7(4):433-444
Statistical and deterministic methods are widely used in geographic information system based landslide susceptibility mapping. This paper compares the predictive capability of three different models, namely the Weight of Evidence, the Fuzzy Logic and SHALSTAB, for producing shallow earth slide susceptibility maps, to be included as informative layers in land use planning at a local level. The test site is an area of about 450 km2 in the northern Apennines of Italy where, in April 2004, rainfall combined with snowmelt triggered hundreds of shallow earth slides that damaged roads and other infrastructure. An inventory of the landslides triggered by the event was obtained from interpretation of aerial photos dating back to May 2004. The pre-existence of mapped landslides was then checked using earlier aerial photo coverage. All the predictive models were run on the same set of geo-environmental causal factors: soil type, soil thickness, land cover, possibility of deep drainage through the bedrock, slope angle, and upslope contributing area. Model performance was assessed using a threshold-independent approach (the ROC plot). Results show that global accuracy is as high as 0.77 for both statistical models, while it is only 0.56 for SHALSTAB. Besides the limited quality of input data over large areas, the relatively poorer performance of the deterministic model maybe also due to the simplified assumptions behind the hydrological component (steady-state slope parallel flow), which can be considered unsuitable for describing the hydrologic behavior of clay slopes, that are widespread in the study area. 相似文献
3.
Landslide susceptibility mapping and spatial prediction have been carried out for the headwater region of Manimala river basin in the Western Ghats of Kerala, India, through geographic information technology and bayesian statistics, Weights of Evidence (WofE) model. The variables such as geomorphology, slope, relative relief, terrain curvature, slope length and steepness, soil type and land use/land cover are considered as factors that translate the terrain susceptible to landsliding. The quantitative relationship between landslides and the causative factors were statistically weighted using the ArcSDM extension of ArcGIS software. The posterior probability map, produced on the basis of predictive weights for each variable by combining the weighted layers in GIS, shows a high posterior probability value of 0.1 (highly possible) with a standard deviation of 0.0025. The discrete susceptibility classes in the reclassified posterior probability map reveals that the high and moderate landslide susceptibility classes cover 0.78 and 14.93% respectively of the total study area. The result was validated using the Area Under Curve (AUC) method with a separate set of landslide locations and the validation demonstrates high prediction accuracy with a prediction rate of 81.32%. 相似文献
4.
对美国目前常用的一些用于流域总量管理的数学模型进行了概括分类,把收集到47个模型划分为流域负荷模型、受纳水体模型和集成化模拟系统三类,并选择其中的19个流域污染负荷模型作为重点进行了比较研究。针对这19个模型,首先对各模型的主要输入数据、模型主要的输出信息、模型所能处理的土地利用类型、模型中水文侵蚀和沉积物的计算机理、污染负荷计算结果的基本形式、所能模拟的污染物类型、模型的时间尺度特征、模型软件的提供者等9个方面作了较为详细的介绍。在此基础上,结合文献调研和对部分模型实际使用的效果,从模型处理不同土地利用类型(包括对流域点源的识别和处理)的适用性、对不同时间尺度的适应性、水文过程的模拟能力、不同类型污染物负荷的计算能力、对污染物运移过程的描述、模型结果输出的友好性、输入数据的需求程度、评估和设计污染总量管理措施的支持能力、以及使用说明文档的完备性等9个方面,对模型性能进行了定性评估,分别用“高”、“中”、“低”来表示模型的优劣程度。 相似文献
5.
Samaneh Razavizadeh Karim Solaimani Matteo Massironi Ataollah Kavian 《Environmental Earth Sciences》2017,76(14):499
The main objective of this study is to investigate potential application of frequency ratio (FR), weights of evidence (WoE), and statistical index (SI) models for landslide susceptibility mapping in a part of Mazandaran Province, Iran. First, a landslide inventory map was constructed from various sources. The landslide inventory map was then randomly divided in a ratio of 70/30 for training and validation of the models, respectively. Second, 13 landslide conditioning factors including slope degree, slope aspect, altitude, plan curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, lithology, distance from streams, faults, roads, and land use type were prepared, and the relationships between these factors and the landslide inventory map were extracted by using the mentioned models. Subsequently, the multi-class weighted factors were used to generate landslide susceptibility maps. Finally, the susceptibility maps were verified and compared using several methods including receiver operating characteristic curve with the areas under the curve (AUC), landslide density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies. 相似文献
6.
中国土地质量评价的研究现状及展望 总被引：5，自引：0，他引：5
土地质量评价是一项系统性很强的工作,目前主要开展土地的适宜性评价及利用评价,土地可持续利用的评价成为热门领域。评价指标体系日趋完善,评价方法多样,常见的有层次分析法、模糊综合评价法、权重指数和法及神经网络模型等,同时与GIS技术结合运用成为研究的一大特色。土地质量评价的发展趋势是评价指标的体系化、具体化和处理数据、管理的信息化。 相似文献
7.
Johannes Hendrik Van der Merwe 《GeoJournal》1997,43(2):135-151
A computerised aid to the land use planning process is demonstrated on the urban edge of Cape Town, South Africa. Multi-criteria analysis is performed in the IDRISI GIS package to evaluate development suitability for four land use categories according to appropriately measured and weighted criteria. The four suitability images are then subjected to multi-objective land allocation to demarcate optimum locations for each land use type. The decision-making process entails execution of seven consecutive steps which are discussed in detail and applied in the case study. Technical decisions are rationalised and results displayed. The paper concludes with a call for the development of applications which can incorporate public participation in this type of decision-making process to ensure the wider acceptance of advanced GIS technology as appropriate technology. 相似文献
8.
Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: advantages and limitations 总被引：2，自引：0，他引：2
G&#;mez-Guti&#;rrez &#;lvaro Conoscenti Christian Angileri Silvia Eleonora Rotigliano Edoardo Schnabel Susanne 《Natural Hazards》2015,79(1):291-316
Empirical multivariate predictive models represent an important tool to estimate gully erosion susceptibility. Topography, lithology, climate, land use and vegetation cover are commonly used as input for these approaches. In this paper, two multivariate predictive models were generated for two gully erosion processes in San Giorgio basin (Italy) and Mula River basin (Spain) using only topographical attributes as independent variables. Initially, nine models (five for San Giorgio and four for Mula) with pixel sizes ranging from 2 to 50 m were generated, and validation statistics were calculated to estimate the optimal pixel size. The best models were selected based on model performance using the area under the receiver operating characteristic (AUC) curve and the generalized cross-validation. The best pixel size was 4 m in the San Giorgio basin and 20 m in the Mula basin. The finest resolution was not necessarily the best; rather, the relationship between digital elevation model resolution and size of the landform was important. The two selected models showed an excellent performance with AUC values of 0.859 and 0.826 for San Giorgio and Mula, respectively. The Topographic Wetness Index and the general curvature were identified as key topographical attributes in San Giorgio and Mula basins, respectively. Both attributes were related to the processes observed in the field and described in the literature. Finally, maps of gully erosion susceptibility were produced for each basin. These maps showed that 22 and 20 % of San Giorgio and Mula basins, respectively, present favourable conditions for the development of gullies. 相似文献
9.
Rajendra Bhausaheb Zolekar 《Arabian Journal of Geosciences》2018,11(24):780
Land elements like slope, soil depth, land use/land cover, water holding capacity, soil texture, soil erosion, elevation, potential of hydrogen, etc. determine the suitability for agriculture. Land suitability analysis is a one of the methods of assessment of detecting inherent capacities, potential and suitability levels of the lands for agriculture, and was utilized with the same land elements in this study. A multi-criterion decision making approach using IRS P6 LISS-IV satellite dataset within a GIS environment was used to identify suitable areas for agriculture in the Darna catchment. Experts’ opinions, literature review, and correlation technique were used to decide influencing criteria, assign scores to sub-criteria, and judgment formation in pairwise comparison matrix. All thematic layers of criteria were integrated with each other in GIS using the weighted overlay technique and generated agriculture suitability map into four classes according to FAO. About 23% of the area is under agriculture in the study region. This area can extend up to 69% under agriculture converting fallow land, scrub land, and sparse forest according to soil qualities with suitability levels, i.e., highly suitable (19%), moderately suitable (16%), and marginally suitable (34%). About 31% (19,219 ha) of reviewed area are classified in the class permanently “not suitable” for agriculture. Moderately and marginally suitable land requires the irrigation facility for efficient agriculture. This study emphasizes that about 46% area has potential as agriculture land and it will help improve the financial condition of the farmers. 相似文献
10.
This study deals with landslide susceptibility mapping in the northern part of Lecco Province, Lombardy Region, Italy. In so doing, a valid landslide inventory map and thirteen predisposing factors (including elevation, slope aspect, slope degree, plan curvature, profile curvature, distance to waterway, distance to road, distance to fault, soil type, land use, lithology, stream power index, and topographic wetness index) form the spatial database within geographic information system. The used predictive models comprise a bivariate statistical approach called frequency ratio (FR) and two machine learning tools, namely multilayer perceptron neural network (MLPNN) and adaptive neuro-fuzzy inference system (ANFIS). These models first use landslide and non-landslide records for comprehending the relationship between the landslide occurrence and predisposing factors. Then, landslide susceptibility values are predicted for the whole area. The accuracy of the produced susceptibility maps is measured using area under the curve (AUC) index, according to which, the MLPNN (AUC?=?0.916) presented the most accurate map, followed by the ANFIS (AUC?=?0.889) and FR (AUC?=?0.888). Visual interpretation of the susceptibility maps, FR-based correlation analysis, as well as the importance assessment of predisposing factors, all indicated the significant contribution of the road networks to the crucial susceptibility of landslide. Lastly, an explicit predictive formula is extracted from the implemented MLPNN model for a convenient approximation of landslide susceptibility value.
相似文献11.
Accurate prediction of slope stability is a significant issue in geomechanics with many artificial intelligence (AI) techniques being utilised. However, the application of AI has not reached its full potential because of the lack of more robust algorithms. In this paper, we proposed a hybrid ensemble method for the improved prediction of slope stability using classifier ensembles and genetic algorithm. Gaussian process classification, quadratic discriminant analysis, support vector machine, artificial neural networks, adaptive boosted decision trees, and k‐nearest neighbours were chosen to be individual AI techniques, and the weighted majority voting was used as the combination method. Validation method was chosen to be the 10‐fold cross‐validation, and performance measures were selected to be the accuracy, the receiver operating characteristic curve, and the area under the receiver operating characteristic curve (AUC). Grid search and genetic algorithm were used for the hyperparameter tuning and weight tuning respectively. The results show that the proposed hybrid ensemble method has great potential in improving the prediction of slope stability. Compared with individual classifiers, the optimum ensemble classifier achieved the highest AUC value (0.943) and the highest accuracy (0.902) on the testing set, denoting that the predictive performance has been improved. The optimum ensemble classifier with the Youden's cut‐off was recommended for slope stability prediction with respect to the AUC value, the accuracy, the true positive rate, and the true negative rate. This research indicates that the use of the classifier ensembles, rather than the search for the ideal individual classifiers, might help for the slope stability prediction. 相似文献
12.
Land suitability evaluation is prerequisite for assessing the limitations for sustainable land use planning. We used ten site specific criteria (rainfall, texture, drainage, soil depth, slope, distance to major road, distance to nearest sugar mill, erosion hazard, risk of flooding and pH) and applied weighted multi-criteria evaluation (MCE) technique in a geographic information system (GIS) environment to evaluate land suitability for sugarcane cultivation in Bijnor district, India. The weightage of all the parameters was calculated through fuzzy analytical hierarchy process. Sugarcane suitability map was prepared integrating various parameters through weighted overlay analysis. The map was categorized as highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and unsuitable (N). The analysis revealed that of the total cultivable land of the district, largest area (61%) was highly suitable followed by moderately suitable (24%), marginally suitable (7%) and unsuitable (8%) for sugarcane cultivation. Nagina, Najibabad and Bijnor sub-districts need attention of land managers and policy makers to remove the limitations and increase the suitability of sugarcane in such areas. Only 7% area was unsuitable for sugarcane cultivation. Slope, soil depth and erosion hazard were the major limiting factors making the land unsuitable for sugarcane cultivation. Therefore, these areas should be given priority for land and soil restoration efforts. The study showed effectiveness of integrated GIS and MCE approach for land suitability analysis of sugarcane. 相似文献
13.
Abdı Amina Bouamrane Ali Karech Toufik Dahri Noura Kaouachi Anouar 《Geotechnical and Geological Engineering》2021,39(8):5675-5691
The main purpose of this study was to compare and evaluate the performance of two multicriteria models for landslide susceptibility assessment in Constantine, north-east of Algeria. The landslide susceptibility maps were produced using the analytic hierarchy process (AHP) and Fuzzy AHP (FAHP) via twelve landslides conditioning factors, including the slope gradient, lithology, land cover, distance from drainage network, distance from the roads, distance from faults, topographic wetness index, stream power index, slope curvature, Normalized Difference Vegetation Index, slope aspect and elevation. In this study, the mentioned models were used to derive the weighting value of the conditioning factors. For the validation process of these models, the receiver operating characteristic analysis, and the area under the curve (AUC) were applied by comparing the obtained results to The landslide inventory map which prepared using the archives of scientific publications, reports of local authorities, and field survey as well as analyzing satellite imagery. According to the AUC values, the FAHP model had the highest value (0.908) followed by the AHP model (0.777). As a result, the FAHP model is more consistent and accurate than the AHP in this case study. The outcome of this paper may be useful for landslide susceptibility assessment and land use management.
相似文献14.
A. Salman Mahini M. Gholamalifard 《International Journal of Environmental Science and Technology》2006,3(4):435-445
Landfill has been taken to the bottom of the hierarchy of options for waste disposal but has been the most used method for urban solid waste disposal. However, landfill has become more difficult to implement because of its increasing cost, community opposition, and more restrictive regulations regarding the siting and operation of landfills. Land is a finite and scarce resource that needs to be used wisely. Appropriate allocation of landfills involves the selection of areas that are suitable for waste disposal. The present work describes a type of multi-criteria evaluation (MCE) method called weighted linear combination (WLC) in a GIS environment to evaluate the suitability of the study region for landfill. The WLC procedure is characterized by full tradeoff among all factors, average risk and offers much flexibility than the Boolean approaches in the decision making process. The relative importance weights of factors are estimated using the analytical hierarchy process (AHP). In the final aggregated suitability image, zones smaller than 20 hectares are eliminated from the allocation process. Afterwards, the land suitability of a zone is determined by calculating the average of the suitability of the cells belonging to that zone, a process called zonal land suitability. The application of the presented method to the Gorgan city (Iran) indicated that there are 18 zones for landfill with their zonal land suitability varying from 155.426117 to 64.149024. The zones were ranked in descending order by the value of their zonal land suitability. The results showed the use of GIS as a decision support system (DSS) available to policy makers and decision makers in municipal solid waste (MSW) management issues. 相似文献
15.
The demands and needs of our presently increasing population require that the social decisions for land use must be based on the best and most reliable information that the decision-makers can obtain. Growing mass of natural resource information should be presented in a concise and practical form which the planners and public administrators can readily use without a need to learn about natural resources in detail. One of the means to present this information is a resource evaluation system which consists of four levels of maps: basic resource maps, single-factor maps, limiting-factor maps and final land suitability maps. The basic process used in the system is developing a series of individual maps showing specific kinds of information and superimposing them to identify a degree of suitability for a specific land use. The authors provide an overview of the use of land suitability maps in land use planning in the United States; show what natural resource data planners can require and put to use; outline the way in which earth scientists can evaluate and present the data needed; and finally demonstrate the land suitability analysis by case studies. Also briefly discussed is the need for information other than earth-science data. Land use planning and decision-making process is an intricate mixture of scientific, engineering, ecological, sociological, economic, cultural and political factors and forces. Obvious as it may seem this fact is nevertheless often ignored in practice resulting in misunderstandings and mutual disbelief of parties involved. 相似文献
16.
Statistical models are one of the most preferred methods among many landslide susceptibility assessment methods. As landslide
occurrences and influencing factors have spatial variations, global models like neural network or logistic regression (LR)
ignore spatial dependence or autocorrelation characteristics of data between the observations in susceptibility assessment.
However, to assess the probability of landslide within a specified period of time and within a given area, it is important
to understand the spatial correlation between landslide occurrences and influencing factors. By including these relations,
the predictive ability of the developed model increases. In this respect, spatial regression (SR) and geographically weighted
regression (GWR) techniques, which consider spatial variability in the parameters, are proposed in this study for landslide
hazard assessment to provide better realistic representations of landslide susceptibility. The proposed model was implemented
to a case study area from More and Romsdal region of Norway. Topographic (morphometric) parameters (slope angle, slope aspect,
curvature, plan, and profile curvatures), geological parameters (geological formations, tectonic uplift, and lineaments),
land cover parameter (vegetation coverage), and triggering factor (precipitation) were considered as landslide influencing
factors. These influencing factors together with past rock avalanche inventory in the study region were considered to obtain
landslide susceptibility maps by using SR and LR models. The comparisons of susceptibility maps obtained from SR and LR show
that SR models have higher predictive performance. In addition, the performances of SR and LR models at the local scale were
investigated by finding the differences between GWR and SR and GWR and LR maps. These maps which can be named as comparison
maps help to understand how the models estimate the coefficients at local scale. In this way, the regions where SR and LR
models over or under estimate the landslide hazard potential were identified. 相似文献
17.
Natural Hazards - The suitability assessment of land use is crucial to avoid wasting land resources. However, the traditional methods with subjective weights are prone to reduce the reasonability... 相似文献
18.
花江峡谷喀斯特区土壤质量两种定量化评价方法研究 总被引：3，自引：0，他引：3
选取贵州省花江峡谷喀斯特区5种主要的土地利用类型（灌丛、乔木林、人工林、耕地、草坡）研究土地利用方式对土壤质量的影响。研究结果表明:不同土地利用类型下土壤质量有显著差异。本文应用土壤质量指数法及退化指数法对花江峡谷喀斯特区不同的土地利用方式下土壤质量的变化情况进行定量化的对比研究,得出相同的变化趋势,即灌丛>乔木林>人工林>耕地>草坡。并且相关性分析表明,两种定量评价方法的土壤综合质量指数与土壤退化指数之间存在线性相关性（R2=9.277）,说明两种评价方法都能有效地反映不同土地利用方式下土壤质量的变化情况。 相似文献
19.
基于GIS的岩溶煤矿区土地利用适宜性评价研究——以重庆市南桐矿区为例 总被引：1，自引：0，他引：1
以SPOT5卫星为基本信息获取该地区的地貌数据,并根据该地区的土地利用现状、土壤特征、煤炭开采状况等9个指标,采用多因素分级指标综合评价法对南桐矿区的土地利用适宜性进行评价。按照土地的用途划分宜耕、宜园和宜林土地类三个类型,根据影响因子对土地用途的影响程度制定分级指标。评价结果表明,高度适宜、中等适宜、基本适宜和不适宜耕地分别为5 321hm2、8361hm2、7 973hm2和4 656hm2;园地分别为315hm2、629hm2、405hm2 和117hm2;林地分别为8 867hm2、7303hm2、5 977hm2 和2 643hm2。为能更好地利用土地资源和促进矿区的可持续发展,建议将原来的耕地、园地和林业用地比例10∶ 0. 6∶ 9. 4调整为10∶ 5. 7∶ 14。 相似文献
20.
Gully erosion is an important environmental issue with severe impacts. This study aimed to characterize gully erosion susceptibility and assess the capability of information value (InfVal) and frequency ratio (FR) models for its spatial prediction in Ourika watershed of the High Atlas region of Morocco. These two bivariate statistical methods have been used for gully erosion susceptibility mapping by comparing each data layer of causative factor to the existing gully distribution. Weights to the gully causative factors are assigned based on gully density. Gullies have been mapped through field surveys and Google earth high-resolution images. Lithofacies, land use, slope gradient, length-slope, aspect, stream power index, topographical wetness index and plan curvature were considered predisposing factors to gullying. The digitized gullies were randomly split into two parts. Sixty-five percent (65%) of the mapped gullies were randomly selected as training set to build gully susceptibility models, while the remaining 35% cases were used as validation set for the models’ validation. The results showed that barren and sparse vegetation lands and slope gradient above 50% were very susceptible to gully erosion. The ROC curve was used for testing the accuracy of the mentioned models. The analysis confirms that the FR model (AUC 80.61%) shows a better accuracy than InfVal model (AUC 52.07%). The performance of the gully erosion susceptibility map constructed by FR model is greater than that of the map produced by InfVal model. The findings proved that GIS-based bivariate statistical methods such as frequency ratio model could be successfully applied in gully susceptibility mapping in Morocco mountainous regions and in other similar environments. The produced susceptibility map represents a useful tool for sustainable planning, conservation and protection of land from gully processes. 相似文献