The weight of interaction of mining activities: groundwater in environmental impact assessment using fuzzy analytical hierarchy process (FAHP)

Mining operations threaten the environment if the monitoring and controlling steps are not implemented completely. One of the important methods for control of the environmental situation in the mining district is the environmental impact assessment (EIA) method, which is performed by matrix calculations. In this method, the environmental problem is broken into several parts as the Impacting Factors that is evaluated their influences on Environmental Components by the mathematical calculations. For these calculations, the weight of each Impacting Factor must be evaluated by using comprehensive scenarios that are involved all the predicable environmental issues. Based on literature, it has not been organized a comprehensive scenario about “Interference with groundwater” as an Impacting Factor yet. By consideration of the importance degree of groundwater and its role in supply the drinking water resource, it is felt to demand for an organization a developed scenario in relation with groundwater pollution in mining district. Therefore, the main aim of this study is developing a new scenario to weight the “Interference with groundwater” in EIA matrix. For this purpose, the 8 criteria and 63 subcriteria are defined and their weights are determined using the Fuzzy Analytic Hierarchy Process. The proposed scenario can be successfully evaluated the weight of “Interference with groundwater” Impacting Factor more exactly than the former one, because it considers 8 criteria and 63 subcriteria instead of 2 criteria in the former scenario. Finally, the application of proposed scenario is illustrated by an imaginary ideal case study. Such studies can be used by mining engineers and planners to design an appropriate environmental plan for the mining districts.     

Selection of wastewater treatment process based on the analytical hierarchy process and fuzzy analytical hierarchy process methods

This paper presents an application of the analytical hierarchy process and fuzzy analytical hierarchy process methods for selecting the best wastewater treatment process. The analytical hierarchy process is one of the best ways for deciding among the complex criteria structure in different levels, and the fuzzy analytical hierarchy process is a synthetic extension of the classical method when the fuzziness of the decision makers are considered. After reviewing aerobic treatment processes operated in Iran’s industrial estates and determining the main criteria used for treatment process evaluation, they are arranged in a hierarchy structure. Selection of the best wastewater treatment process is a multi-criteria decision making problem. Conventional methods are inadequate for dealing with the imprecise or vague nature of linguistic assessment. To overcome this difficulty, the fuzzy analytical hierarchy process is proposed for dealing with the vagueness of decision makers’ judgments. The alternatives consist of extended aeration, absorption bio-oxidation, integrated fixed-film activated sludge, sequencing batch reactor, aerated lagoon. Based on the general condition of industrial estate’s wastewater treatment plants, technical/administrative, economic and environmental criteria and their sub-criteria are weighted and then criteria evaluated and priorities of alternatives have been done by analytical hierarchy process and fuzzy analytical hierarchy process methods by the use of triangular fuzzy numbers. Finally, selection of the best process and ranking of these five processes are carried out by these foregoing methods, and some sensitivity analyses are conducted to show the results’ sensitiveness to the changes of the weights of the evaluation criteria.     

Assessing soil erosion risk in karst area using fuzzy modeling and method of the analytical hierarchy process

Soil erosion is a geo-ecological problem in Southwest China, which can result in karst rocky desertification when it is very serious. The grade delineation of soil erosion risk can be used as a guide for controlling regional and hierarchical soil erosion, and provide scientific references for restraining rocky desertification. Chaotian Town, an area of Karst Mountains and hills in Guilin, northeast part of Guangxi Zhuang Autonomous Region, China, was chosen as the study area. Settlement buffer, roadway buffer, lithology, slope, elevation, and aspect were selected as the main indicators of soil erosion in the karst area. After the indicators were scored by fuzzy modeling, the potential risk of soil erosion was quantified using indicator-weight union method. Five grades of soil erosion risk were delineated based on Geographic Information System. The grades of minimal, low, medium, high, and extreme soil erosion risk accounted for 1.62, 19.46, 52.35, 24.86, and 1.71 %, respectively. As a whole, the soil erosion risk was moderate, because the proportion data of different grades of soil erosion risk were a normal distribution, and about 52.35 % of the study area was in the medium grade. Soil erosion risk was higher in the southeast of the study area and lower in the northwest. Obvious variances have been found in the grade distribution of soil erosion risk, corresponding to different indicators.     

Landfill site selection using integrated fuzzy logic and analytic network process (F-ANP)

Locating a suitable place to dispose the municipal solid wastes hygienically (sanitary landfill) is one of the fundamental subjects relating the environmental stability of cities and, in general, the human settlements. This final stage of solid waste management requires observance of a series of principles and criteria mainly including environmental, social, economical, and general acceptance criteria. The set of these criteria along with their sub-criteria causes some complexities in decision making for landfill site selection. Considering effective parameters and criteria, we developed an integrated fuzzy logic and analytic network process (i.e. F-ANP) to locate a suitable location for landfilling municipal solid wastes generated in Kahak Town, Qom, Iran. Our findings revealed that integration of fuzzy logic and ANP can give better idea compared with other models like AHP, fuzzy logic, and ANP (individually). Therefore, this model can be applied in site selection for landfill of other similar places.     

Mapping debris flow susceptibility using analytical network process in Kodaikkanal Hills,Tamil Nadu (India)

Rapid debris flows, a mixture of unconsolidated sediments and water travelling at speeds > 10 m/s are the most destructive water related mass movements that affect hill and mountain regions. The predisposing factors setting the stage for the event are the availability of materials, type of materials, stream power, slope gradient, aspect and curvature, lithology, land use and land cover, lineament density, and drainage. Rainfall is the most common triggering factor that causes debris flow in the Palar subwatershed and seismicity is not considered as it is a stable continental region and moderate seismic zone. Also, there are no records of major seismic activities in the past. In this study, one of the less explored heuristic methods known as the analytical network process (ANP) is used to map the spatial propensity of debris flow. This method is based on top-down decision model and is a multi-criteria, decision-making tool that translates subjective assessment of relative importance to weights or scores and is implemented in the Palar subwatershed which is part of the Western Ghats in southern India. The results suggest that the factors influencing debris flow susceptibility in this region are the availability of material on the slope, peak flow, gradient of the slope, land use and land cover, and proximity to streams. Among all, peak discharge is identified as the chief factor causing debris flow. The use of micro-scale watersheds demonstrated in this study to develop the susceptibility map can be very effective for local level planning and land management.     

Land suitability assessment for sugarcane cultivation in Bijnor district,India using geographic information system and fuzzy analytical hierarchy process

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.     

Applications of analytical hierarchy process (AHP) and analytical network process (ANP) for industrial site selections in Isfahan,Iran

Industrial sites are key factors in urban and regional land use planning. Therefore, determining the location of industrial areas is a critical and complex process for development and success. Industrial site selection aims in identifying the most suitable sites for industry creation, considering a set of influential criteria. Therefore, site selection generally and industrial site selection specifically can be categorised as a multi-criteria decision-making (MCDM) problem that requires detailed evaluation of various dimensions. This study developed a set of clusters containing 10 selection criteria for industrial site selection in Isfahan metropolitan area, Iran. The relationships between the criteria and clusters were modelled and analysed using analytical hierarchy process (AHP) and analytical network process (ANP). AHP and ANP agree in finding distance to water bodies and distance to other industries as the most and least important selection criteria. Four patches have been identified as suitable alternatives for industrial construction. While AHP found Borkhar Patch 1 as the most appropriate alternative, ANP demonstrated the superiority of Ardestan Patch over others. Conducting a sensitivity analysis for the models confirmed both models robustness in industrial site selection decisions.     

Prioritization of disaster risk in a community using GIS

Prioritization of disaster risk was carried out for a community in Toronto, Canada. Geographic information systems (GIS) were used for spatial analysis, including spatial overlays and clipping for extracting spatial and attribute information related to people’s vulnerability, critical infrastructure and landuse. In order to determine disaster risk, the overall community vulnerability was evaluated by combining social, economic, physical and environmental vulnerabilities. This paper uses the propane explosion incident as the case in point to demonstrate the methodology and procedure used to evaluate risk using GIS techniques. City of Toronto spatial data have been integrated with the study area to gather landuse information, identify risk zones based on the propane storage facility location and evaluate risks. Statistics Canada 2006 census data have been used for area demographics and people’s social and economic status. Vulnerability indicators were determined based on the GIS-derived spatial and attribute data for the hazard and evacuation zones followed by a quantitative spatial risk estimation and ranking. The methodology of this study, based on the risk evaluation and prioritization conducted, can be applied to future decision making in effective landuse planning and the development of risk management strategies.     

Minimization of environmental risk of landfill site using fuzzy logic, analytical hierarchy process, and weighted linear combination methodology in a geographic information system environment

This paper presents a GIS-based multi-criteria site selection for municipal solid waste landfilling in Ariana Region, Tunisia. Based on the regional characteristics, literature related to disposal sites and waste management, local expert, data availability and assessments via questionnaires, 15 constraints, and 5 factors were built in the hierarchical structure for landfill suitability by multi-criteria evaluation. The factors are divided into environmental and socio-economic groups. The methodology is used for preliminary assessment of the 20-year most useful lifetime suitable landfilling sites by combining fuzzy set theory, weighted linear combination (WLC) and analytic hierarchy process (AHP) in a GIS environment. The criteria standardization is undertaken by application of different fuzzy membership functions. The fuzzy membership functions shape and their control points are chosen through assessment of expert opinion. The weightings of each selection criterion are assigned depending on the relative importance using the AHP methodology. The WLC approach is applied for alternative landfill sites prioritization. The results of this study showed five potential candidate sites, which are generated when the environmental factors are valued higher than socio-economic factors. These sites are ranked in descending order using the ELECTRE III method. However, the final decision will require further detailed geotechnical and hydrogeological analyses toward the protection of groundwater as well as surface water.     

模糊马尔科夫链状模型在斜坡稳定性预测中的应用

滑坡灾害的突发特征和滑坡运动的不确定性,使得斜坡稳定状态预测成为岩土工程领域内的一个难点。模糊马尔科夫链状模型能够预测序列从某种模糊状态出发转移到其它状态的概率,非常适合斜坡工程的稳定状态预测。文章首先介绍了模糊马尔科夫链的概念、特点及该方法的原理、算法,之后将其应用到实际的边坡稳定状态预测分析中。实际边坡滑动经过稳定状态、基本稳定状态、临界状态、失稳状态,应用模糊马尔科夫原理分析边坡稳定状态变化,进行斜坡变形-失稳过程预测预报。通过实例应用表明,预测结果与实际情况吻合较好。为此,该模型能够准确地对斜坡稳定状态进行预测。     

LiDAR-supported prediction of slope failures using an integrated ensemble weights-of-evidence and analytical hierarchy process

The present study investigates a potential application of different resolution topographic data obtained from airborne LiDAR and an integrated ensemble weight-of-evidence and analytic hierarchy process (WoE–AHP) model to spatially predict slope failures. Previously failed slopes of the Pellizzano (Italy) were remotely mapped and divided into two subsets for training and testing purposes. 1, 2, 5, 10, 15, and 20 m topographic data were processed to extract nine terrain attributes identified as conditioning factors for landslides: slope degree, aspect, altitude, plan curvature, profile curvature, stream power index, topographic wetness index, sediment transport index, and topographic roughness index. Landslide (slope failure) susceptibility maps were produced using a single WoE (Model 1), an ensemble WoE–AHP model that used all conditioning factors (Model 2), and an ensemble WoE–AHP model that only used highly nominated conditioning factors (Model 3). The validation results proved the efficiency of high-resolution (≤ 5 m) topographic data and the ensemble model, particularly when all factors were used in the modeling process (Model 2). The average success rates and prediction rates for Model 2 that used ≤ 5 m resolution datasets were 84.26 and 82.78%, respectively. The finding presented in this paper can aid in planning more efficient LiDAR surveys and the handling of large datasets, and in gaining a better understanding of the nature of the predictive models.     

Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran

The main goal of this study is to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) models. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 78 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70?% (55 landslides) for training the models and the remaining 30?% (23 landslides) was used for validation purpose. Twelve data layers, as the landslide conditioning factors, are exploited to detect the most susceptible areas. These factors are slope degree, aspect, plan curvature, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, stream power index, slope length, and topographic wetness index. Subsequently, landslide susceptibility maps were produced using fuzzy logic and AHP models. For verification, receiver operating characteristics curve and area under the curve approaches were used. The verification results showed that the fuzzy logic model (89.7?%) performed better than AHP (81.1?%) model for the study area. The produced susceptibility maps can be used for general land use planning and hazard mitigation purpose.     

GIS-based vulnerability mapping of the coastal stretch from Puri to Konark in Odisha using analytical hierarchy process

Natural Hazards - The 485-km-long coastline of Odisha, a state in the northeastern part of the Indian peninsula, is potentially vulnerable to several disaster events that take place frequently. In...     

Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region,Uttarakhand

A comprehensive use of analytical hierarchy process (AHP) method in landslide susceptibility mapping (LSM) has been presented for rim region of Tehri reservoir. Using remote sensing data, various landslide causative factors responsible for inducing instability in the area were derived. Ancillary data such as geological map, soil map, and topographic map were also considered along with remote sensing data. Exhaustive field checks were performed to define the credibility of the random landslide conditioning factors considered in this study. Apart from universally acceptable inherent causative factors used in the susceptibility mapping, others such as impact of reservoir impoundment on terrain, topographic wetness index and stream power index were found to be important causative factors in rim region of the Tehri reservoir. The AHP method was used to acquire weights of factors and their classes respectively. Weights achieved from AHP method matched with the existing field conditions. Acceptable consistency ratio (CR) value was achieved for each AHP matrix. Weights of each factor were integrated with weighted sum technique and a landslide susceptibility index map was generated. Jenk’s natural break classifier was used to classify LSI map into very low, low, moderate, high and very high landslide susceptible classes. Validation of the susceptibility map was performed using cumulative percentage/success rate curve technique. Area under curve value of the success rate curve was converted to percentage validation accuracy and a reasonable 78.7% validation accuracy was achieved.     

Prioritization of soil conservation measures using erodibility indices as criteria in Sikkim (India)

Erodibility indices are important parameter that can be used to describe the intensity of the soil erosion problems causing environmental concerns. These indices are convenient to estimate the susceptibility to erosion where physical measurement is very difficult. Sikkim is one such state in India where measuring erosion is a tedious process due to its difficult and inaccessible terrain conditions. In the present study, spatial variation of susceptibility of erosion in East district of Sikkim was estimated by using indices such as clay ratio, dispersion ratio, mod clay ratio and critical level of soil organic matter. The result indicates soils in East district are mostly dominated by sand particles (40.5–81.06%) in majority of soil samples. The dispersion ratio values in most of the soils were >15% indicating very high vulnerability to erosion. The values of clay ratio (3.44–9), modified clay ratio (mean value of 6.9) and critical level of soil organic matter content (<5%) indicated high susceptibility to erosion. The trends of indices were generated by IDW interpolation method to understand the spatial variation of the susceptibility to erosion. The interpolated maps were overlaid on subwatershed maps to prioritize the subwatershed for planning treatment measures.     

Logistic innovation in global supply chains: an empirical test of dynamic transaction-cost theory

Upgrading logistic services in the context of international supply chains is not a smooth process. Upgrading may require the development of co-operative relationships, or alliances, involving large logistic service firms and their customers: multinational enterprises. Both sides may be unwilling and/or unable to engage in upgrading strategies in an alliance context. Fourth-party logistics is an example of a logistic product innovation entailing research-based advice as well as the design and implementation of new supply-chain solutions. It has the potential of stimulating spatial shift in global production networks and regional distribution systems (Visser and Lambooy, Geographisches Zeitschrift 92(heft 1+2):5–20, 2005). Using dynamic transaction-cost theory (Nooteboom, Learning and innovation in organisations and economies, 2000), this paper theoretically specifies and empirically measures three risks associated with the development of fourth-party logistics: dependence, unwanted spillovers, and conservatism. Case-study evidence reveals that dynamic transaction-cost theory partially explains the slow development of new logistic services in an alliance setting. Some aspects of conservatism are found to be relevant, along with risks of dependence due to specific investments in human resources and information systems. Other, not predicted but important factors, however, are the lack of credibility of service innovation by asset-based logistic firms, and strategic considerations of customers regarding logistic organization and control. The first factor implies that firms specializing in fourth-party services are likely to remain (very) limited in number. The second factor implies that this type of service provision is more likely to develop in dynamic port clusters, as customers prefer to tap into a variety of ideas from many different suppliers. In local clusters, interactions between firms can be relatively frequent, casual, short-lived and open, so that the structure of local networks is relatively decentralized, dense and flexible. This stimulates (logistic) innovation (Nooteboom, in Hanusch and Pyka (eds.) Elgar companion to neo Schumpeterian economics, 2006). In global supply chains, interactions between logistic service firms and their customers tend to have the opposite characteristics, which do not stimulate innovation.     

模糊神经网络在矿震预测中的应用

矿震同天然地震一样会给矿山生产及人身安全等带来重大灾难。也是目前尚不能准确预测的矿山灾害现象之一。根据现有的研究成果可知，矿震是一个多输入、多干扰、单输出的复杂系统。由于干扰项的存在，使利用建模、神经网络等手段对系统进行预测时会导致很大误差。模糊神经网络系统在建立对象输入、输出关系时与传统数学方法不同。即可以建立在无模型基础上，并利用其较强的学习训练特性，自动获取对象的输入、输出关系表达；可以将专家的评价语言作为系统的干扰项引入。这在一定程度上缓解了人为因素对预测结果的影响，且平滑了观测数据的随机性。文章利用改进的模糊神经网络及抚顺老虎台矿的矿震资料，对矿震最大震级的预测方法进行了探索。‘初步探讨了改进的模糊神经网络在矿震预测中的应用。得出在运用模糊神经网络进行预测时，为减小预测误差，应综合多种因素并提高专家评判语言的精确度的结论。指出在建立矿震系统预测模型时，利用干扰项将人为因素引入系统是必需的。通过实际应用证明其可行性。     

Landslide hazard zonation using analytical hierarchy process along National Highway-3 in mid Himalayas of Himachal Pradesh,India

Landslide zonation studies emphasize on preparation of landslide hazard zonation maps considering major instability factors contributing to occurrence of landslides. This paper deals with geographic information system-based landslide hazard zonation in mid Himalayas of Himachal Pradesh from Mandi to Kullu by considering nine relevant instability factors to develop the hazard zonation map. Analytical hierarchy process was applied to assign relative weightages over all ranges of instability factors of the slopes in study area. To generate landslide hazard zonation map, layers in geographic information system were created corresponding to each instability factor. An inventory of existing major landslides in the study area was prepared and combined with the landslide hazard zonation map for validation purpose. The validation of the model was made using area under curve technique and reveals good agreement between the produced hazard map and previous landslide inventory with prediction accuracy of 79.08%. The landslide hazard zonation map was classified by natural break classifier into very low hazard, low hazard, moderate hazard, high hazard and very high landslide hazard classes in geographic information system depending upon the frequency of occurrence of landslides in each class. The resultant hazard zonation map shows that 14.30% of the area lies in very high hazard zone followed by 15.97% in high hazard zone. The proposed model provides the best-fit classification using hierarchical approach for the causative factors of landslides having complex structure. The developed hazard zonation map is useful for landslide preparedness, land-use planning, and social-economic and sustainable development of the region.