Multicriteria Analysis by Codification for the Determination of Soil Landscape Units in Forest and Pre-Forest Zones of Cote D’Ivoire: The Case of the Square Degrees of M’Bahiakro and Daloa

The present study, carried out in the forest (Daloa) and pre-forest (M’Bahiakro) zones of Cote d’Ivoire, aims to determine soil landscape units using the coding method. Geological maps and satellite images (SRTM and Landsat) were used for this purpose. The methodological approach adopted consisted in producing maps of slope, geology, land use and topography using the codification method. These various maps, integrated into a GIS using the coding aggregation method, were used to generate soil landscape maps. Twenty-seven (27) soil landscapes have been identified for the pre-forest zone (M’Bahiakro), with a strong dominance of acid rock over a moderate relief under savannah, forest/degraded forest and crops/fallow. However, the forest zone (Daloa), with forty-one (41) soil landscapes identified over the entire zone, is characterized by a majority of mafic rocks on a medium altitude under forest/degraded forest, water and crops/fallow. The criteria used from the codification method (sum of aggregations) made it possible to predict the spatial distribution of soil map units according to agro-ecological environments in the humid intertropical zone. This is essential for the orientation and reinforcement of soil survey tools. However, a comparative evaluation of the different multicriteria analysis methods for coding and weighting soil landscape unit mapping would enable us to identify the most suitable and efficient method for drawing up base maps for soil surveys.

Ranking environmental projects model based on multicriteria decision-making and the weight sensitivity analysis

With the fast growth of Chinese economic, more and more capital will be invested in environmental projects. How to select the environmental investment projects （alternatives） for obtaining the best environmental quality and economic benefits is an important problem for the decision makers. The purpose of this paper is to develop a decision-making model to rank a finite number of alternatives with several and sometimes conflicting criteria. A model for ranking the projects of municipal sewage treatment plants is proposed by using exports＇ information and the data of the real projects. And, the ranking result is given based on the PROMETHEE method. Furthermore, by means of the concept of the weight stability intervals （WSI）, the sensitivity of the ranking results to the size of criteria values and the change of weights value of criteria are discussed. The result shows that some criteria, such as “proportion of benefit to project cost”, will influence the ranking result of alternatives very strong while others not. The influence are not only from the value of criterion but also from the changing the weight of criterion. So, some criteria such as “proportion of benefit to project cost” are key critera for ranking the projects. Decision makers must be cautious to them.

Modeling Landfill Suitability Based on GIS and Multicriteria Decision Analysis: Case Study in Al-Mahaweelqadaa

Waste management system is not regulated in Iraq. At present, there are various techniques used for solid waste management such as landfill, thermal treatment, biological treatment, recycling etc. Landfill is the most common mode for the disposal of solid waste. However, landfill site selection is a quite complex process and it depends on several criteria and regulations. In this study landfill site selection is performed for Al-Mahaweelqadaa using Multicriteria Decision Analysis (MCDA) and Geographic Information System (GIS). It should be mentioned however, that the existing landfill in this area, is temporary and does not fulfill the environmental conditions. To select suitable landfill site, several criteria were considered such as Urban centers, Land use, Airports, Pipes, Power lines, Railways, Roads, slope, streams, Surface water, Industrial areas, Oil pipes, Liquid gas pipes, Soil types which are prepared. (MCDA) was used to evaluate the relative importance of each criterion. Each map layers were formed with the aid of GIS and final suitability map was created by overlay analyses of each criterion map. According to obtained results, high and low suitable areas were determined in the study area. Field and office checks were performed out to determine the accuracy and suitability of the candidate sites.

GIS and Multicriteria Decision Analysis for Landfill Site Selection in Al-Hashimyah Qadaa

Waste management system is not well regulated in Iraq. Despite the fact that there are various techniques used for solid waste disposal, landfill is the most common mode for the disposal of solid waste in Iraq, landfill site selection criteria is quite complex process and it depends on several regulation and factors. In this study landfill site selection is performed by using Multicriteria Decision Analysis (MCDA) and Geographic Information System (GIS) for the Al-Hashimyahqadaa. Existing landfills in qadaa are temporary and/or non-conforming to the environmental conditions. In order to determine landfill site, several criteria were examined such as urban centers, land use, airports, pipes, power lines, railways, roads, slope, streams, surface water, industrial areas, oil pipes, liquid gas pipes, soil types are prepared. MCDA was used to measure the relative importance weighting for each criterion. Each map layer was prepared using GIS and the final suitable map was created by overlay analyses of each criterion map. According to the results, high and low suitable areas were determined in the study area. Field site check was performed to determine the accuracy and suitability of the candidate sites.

Al-Mseiab Qadaa Landfill Site Selection Using GIS and Multicriteria Decision Analysis

Solid waste management is becoming a more serious problem with time especially in developing countries like Iraq. It is not regulated sufficiently in Iraq. At present, there are various techniques used for solid waste management such as landfill, thermal treatment, biological treatment, recycling etc. The landfill is the most common mode for the disposal of solid waste. There are no regulations for landfill site selection which is quite complex process. In this study, landfill site selection was performed by using Multicriteria Decision Analysis (MCDA) and Geographic Information System (GIS) for the Al-Mseiabqadaa. Even in the case of existing landfills in this qadaa, it’s temporary or non-conforming to the environmental condition. In order to determine landfill site that is good for the inhabitants and the environment several criteria (e.g. as Urban centers, Land use, Airports, Pipes, Power lines, Railways, Roads, Slope, Streams, Surface water, Industrial areas, Oil pipes, Liquid gas pipes, Soil types) were used to select the proper site. The MCDA was used to measure the relative importance weighting for each criterion used. Each map layer was formed with the aid of GIS and final suitability map was created by overlay analyses of each criterion map. According to obtained results, high and low suitable areas were determined in the study area. Field checks were also performed to determine the accuracy and suitability of candidate site.

Mapping of Malaria Risk Related to Climatic and Environmental Factors by Multicriteria Analysis in the MarahouéRegion of Côte d’Ivoire

Malaria, a febrile human disease transmitted by female anopheles whose ecology is linked to water, is a major public health problem in Côte d’Ivoire, more precisely in the Marahoué region located in the southwest of the country. In order to effectively control this disease, it is necessary to understand the etiology and the diffusion pattern of the vectors. This justifies this study, which proposes to determine the areas at risk of malaria transmission in order to carry out an effective fight against this disease in this region of Côte d’Ivoire. To achieve this, a combined approach of geographic information systems and multicriteria analysis was adopted. The analysis reveals that the south and northwest of the Marahoué region present a high risk for malaria transmission. This risk is linked to indicators such as climatic factors that cover 48.36% of the study area, environmental factors such as vegetation cover (NDVI), soil moisture (NDWI), altitude, hydrography (water point) and population that covers 55.29% of the area and land use. Also, the results indicated that 50.70% of the region has favorable conditions for malaria transmission. Overall, climatic and environmental indicators are the risk factors associated with the resurgence of malaria.

Modeling of Quaternary Groundwater Pollution Risk by GIS and Multicriteria Analysis in the Southern Part of Abidjan District(Cote d’Ivoire)

The pollution generated by human activities in the area “Vridi canal-Grand Bassam” puts more at risk the quaternary groundwater that supplies water to a large part of the population of the southern part of Abidjan city. This paper deals with the combined approach of geographic information systems and multicriteria analysis to delineate areas at risk of pollution of groundwater in the study area. The multicriteria analysis also incorporated the statistical and geostatistical techniques for defining the factor ratings and weightings of all the parameters under a GIS environment. The criterion for these factor ratings and weightings was the correlation coefficient of each parameter with the nitrates concentration in groundwater. Two indicators were combined for creating situations of pollution risks according to their importance: the intrinsic vulnerability indicators which combined the depth to groundwater, the net recharge, the slope, the soil media, the unsaturated hydraulic conductivity and extrinsic vulnerability characterized by the land use. The results showed that all to the site presents nitrate pollution risks with 50.7% of moderate risk, and 49.3% of high and very high risks. The areas which present the highest risks of pollution are the densely urbanized communes of Treichville, Marcory, Koumasi, Port-Bouet and Grand-Bassam. The sensitivity analysis indicates that parameters causing high sensitivity on the nitrate pollution risks map are the depth to groundwater and the recharge. The errors committed on the maps establishment are ±0.15%, ±0.40% and ±6% respectively for IV map, EV map and pollution risk map.

Wind turbine clutter mitigation using morphological component analysis with group sparsity

To address the problem that dynamic wind turbine clutter(WTC)significantly degrades the performance of weather radar,a WTC mitigation algorithm using morphological component analysis(MCA)with group sparsity is studied in this paper.The ground clutter is suppressed firstly to reduce the morphological compositions of radar echo.After that,the MCA algorithm is applied and the window used in the short-time Fourier transform(STFT)is optimized to lessen the spectrum leakage of WTC.Finally,the group sparsity structure of WTC in the STFT domain can be utilized to decrease the degrees of freedom in the solution,thus contributing to better estimation performance of weather signals.The effectiveness and feasibility of the proposed method are demonstrated by numerical simulations.

基于多形态学成分分析的图像融合

将多尺度分解与稀疏表示相结合,提出了一种基于多形态学成分分析(MCA)的图像融合算法。采用基于联合稀疏表示(JSR)的方法融合卡通子图像中的冗余和互补信息,并利用基于方向特征的方法融合具有更多细节信息和噪声的纹理子图像。结果表明,提出的图像融合算法在主观视觉效果和客观评价指标上均优于先进的图像融合算法。

一种用于PCA与MCA的神经网络学习算法

主元分析（ＰＣＡ）和次元分析（ＭＣＡ）是用于特征提取、数据压缩、频率估计、曲线拟合等信号处理的基本技术．以神经网络来实现ＰＣＡ和ＭＣＡ是当今研究的一大热点，相关矩阵Ｒ的特征值重数不为１时的主、次元分析则是其中一大难题．本文提出了一种新的学习算法，使得在输入数据的相关矩阵含多重特征值时。

基于改进MCA的干涉高光谱图像分解

干涉高光谱图像特殊的成像原理,使其帧内存在着大幅值且位置固定的干涉条纹,而帧间存在着水平移位的背景图像,这种特点会严重的破坏原始图像的固有结构,从而导致新兴的压缩感知理论与传统压缩算法的直接应用无法得到理想的效果。由于干涉条纹信息与背景图像信息的特征不同,能够对干涉条纹与背景图像进行稀疏表示的正交基也是不同的。基于这种思想,使用MCA(morphological component analysis)算法对干涉高光谱图像中干涉条纹信息与背景图像信息进行分离处理。由于干涉高光谱图像数据量庞大,传统的MCA算法对干涉高光谱数据的图像分解,迭代收敛速度慢,运算效率较低,故而针对干涉高光谱数据特点对传统MCA算法进行改进,改变其迭代收敛条件,当分离后的图像信号与原始图像信号的误差已经基本保持不变时,即终止迭代;并根据对应正交基能且仅能稀疏表示对应信号的思想,对阈值采用自适应的方式进行更新,在新的阈值更新模式中,图像信号在不同正交基下的映射系数被计算与比较。大量实验结果表明,对于LASIS数据与LAMIS数据,MCA算法都能够较完美的将干涉高光谱图像分解,改进的MCA算法更能在保持完美分解输出结果的同时,相对于传统MCA方法显著的减小迭代次数,更快的达到迭代收敛条件,从而有效的提高了算法的运算效率与实时性需求,也为新兴的压缩感知理论在干涉高光谱图像中的进一步应用提供了一种很好的解决方案。

MCA框架下Shearlet和DCT字典组合地震数据重建

相比于单一变换,形态分量分析(MCA)是信号稀疏表示的一种更有效的手段。在MCA框架下,提出了一种Shearlet字典和DCT字典组合的地震数据重建方法。首先,基于MCA框架,分别选取DCT字典和Shearlet字典稀疏表示地震数据中的局部奇异分量与平滑线状分量;随后,通过加入指数阈值模型和指数阈值函数的块坐标松弛(BCR)算法重建各个分量;最后,合并各个分量得到重建结果。合成数据实验和真实数据实验均表明,该方法能够有效地重建缺失地震数据,且重建精度高于单一Shearlet字典、Curvelet+DCT字典组合和Shearlet+Curvelet字典组合。

MCA分解的唐墓室壁画修复算法

壁画数字化修复工作极大降低了手工修复时带来的不可逆的风险。根据唐墓室壁画人工修复时先整体结构、后局部纹理的思路,提出一种基于形态学成分分析(morphological component analysis,MCA)分解的唐墓室壁画修复算法。首先结合唐墓室壁画的特点,采用改进的MCA方法进行图像分解,得到结构部分和纹理部分;然后根据图像分解后纹理和结构的复杂程度与稀疏程度,分别采用简化的全变分(total variation,TV)算法和K奇异值分解(K-singular value decomposition,K-SVD)算法进行修复。实验结果表明,该算法可兼顾纹理与结构的修复效果,唐墓室壁画中的裂缝现象的破损修复精度得到提高。

Text Detection in Natural Scene Images Using Morphological Component Analysis and Laplacian Dictionary

Text in natural scene images usually carries abundant semantic information. However, due to variations of text and complexity of background, detecting text in scene images becomes a critical and challenging task. In this paper, we present a novel method to detect text from scene images. Firstly, we decompose scene images into background and text components using morphological component analysis(MCA), which will reduce the adverse effects of complex backgrounds on the detection results.In order to improve the performance of image decomposition,two discriminative dictionaries of background and text are learned from the training samples. Moreover, Laplacian sparse regularization is introduced into our proposed dictionary learning method which improves discrimination of dictionary. Based on the text dictionary and the sparse-representation coefficients of text, we can construct the text component. After that, the text in the query image can be detected by applying certain heuristic rules. The results of experiments show the effectiveness of the proposed method.

Oscillatory-Plus-Transient Signal Decomposition Using TQWT and MCA

This paper describes a method for decomposing a signal into the sum of an oscillatory component and a transient component. The process uses the tunable Q-factor wavelet transform (TQWT): The oscillatory component is modeled as a signal that can be sparsely denoted by high Q-factor TQWT;similarly, the transient component is modeled as a piecewise smooth signal that can be sparsely denoted using low Q-factor TQWT. Since the low and high Q-factor TQWT has low coherence, the morphological component analysis (MCA) can effectively decompose the signal into oscillatory and transient components. The corresponding optimization problem of MCA is resolved by the split augmented Lagrangian shrinkage algorithm (SALSA). The applications of the proposed method to speech, electroencephalo-graph (EEG), and electrocardiograph (ECG) signals are included.

应用AT IV对直流电动机绕组的MCA分析判别方法

对直流电动机绕组故障的监测一直是电机监测工作的一个难题。与测量一般交流电机不同,测量直流电动机时,没有可供对比的多相绕组,因此分析判断测量数据变得比较困难。文章基于MCA(电动机电路分析)技术的实际运用经验,针对各种不同接法的直流电动机,以海军当前通用的AT IV测量仪器为例,提出了比较有效的监测分析方法和判别标准,基本可以满足对直流电动机绕组故障监测诊断的一般要求。

Wireless Sensor Network Components for Air Pollution Monitoring in the Urban Environment:Criteria and Analysis for Their Selection

Wireless sensor networks (WSNs) are fast evolving technology for collecting data in real time. Every wireless sensor network (WSN) is consisted of technical and software components which have to refer to the selected application. The paper focuses on the selection of WSN components. The WSN will be situated in the center of Olomouc City (OWSN). It will focus on measurements of harmful air pollutants and selected basic meteorological elements. The criteria for selection of WSN components including the most important parameters will be chosen and the final evaluation of the option utility will be made on the basis of multicriteria decision making process.

Application of Decomposition and Denoising of Gearbox Signal Based on Morphological Component Analysis

Morphological component analysis( MCA) is a signal separation method based on signal morphological diversity and sparse representation. MCA can extract the signal components of different morphologies by different dictionary combinations. Firstly,the theory of MCA was analyzed with sparse representation principle and relaxation criterion. Then detailed steps of block coordinate relaxation( BCR) were given. Finally,algorithm performance was verified by simulation signals analysis, MCA was applied to decomposing and denoising gearbox signals, and the fault parameters were extracted by energy operator demodulation envelop of morphological component.

Spatial Analysis to Identify Urban Areas with Higher Potential for Social Investment

There has been great interest on the part of public and private initiatives in knowing the location of urban areas with potential for investments in social resources. The aim of the present article is to propose a methodology to identify such areas, using the city of São Paulo (Brazil) as a case study. This approach, supported by The Geographic Information System (GIS), is based on multicriteria analysis and quantification of spatial data. The results, presented spatially in a synthesis map, made possible to identify promissory areas for social investment in São Paulo. The method allows us to analyze data at different spatial scales from the GIS base, enabling a systematization to find out areas with higher potential for investment in social equipment, services and facilities. Furthermore, it gives possibility of modeling several variables, thus being adaptable to different geographic areas.

Image Restoration Using Hybrid Features Improvement on Morphological Component Analysis

Images are generally corrupted by impulse noise during acquisition and transmission.Noise deteriorates the quality of images.To remove corruption noise,we propose a hybrid approach to restoring a random noisecorrupted image,including a block matching 3D(BM3D)method,an adaptive non-local mean(ANLM)scheme,and the K-singular value decomposition(K-SVD)algorithm.In the proposed method,we employ the morphological component analysis(MCA)to decompose an image into the texture,structure,and edge parts.Then,the BM3D method,ANLM scheme,and K-SVD algorithm are utilized to eliminate noise in the texture,structure,and edge parts of the image,respectively.Experimental results show that the proposed approach can effectively remove interference random noise in different parts;meanwhile,the deteriorated image is able to be reconstructed well.