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Computers & Geosciences 37 (2011) 992–1002
Contents lists available at ScienceDirect
Computers & Geosciences
0098-30
doi:10.1
n Corr
Wuhan
E-m
journal homepage: www.elsevier.com/locate/cageo
Suitability evaluation of urban construction land based ongeo-environmental factors of Hangzhou, China
Kai Xu a, Chunfang Kong a,b, Jiangfeng Li c, Liqin Zhang c, Chonglong Wu a,d,n
a School of Computer, China University of Geosciences, Wuhan 430074, Chinab Key Lab of Biogeology and Environmental Geology of Ministry of Education, China University of Geosciences, Wuhan 430074, Chinac Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, Chinad Three Gorges Research Center for Geo-hazard, Ministry of Education, China University of Geosciences, Wuhan 430074, China
a r t i c l e i n f o
Article history:
Received 12 January 2010
Received in revised form
28 January 2011
Accepted 4 March 2011Available online 12 April 2011
Keywords:
Suitability evaluation
Grid
K-means clustering
Back-propagation (BP) neural network
Geographic Information System (GIS)
Analytic Hierarchy Process (AHP)
Hangzhou
China
04/$ - see front matter Crown Copyright & 2
016/j.cageo.2011.03.006
esponding author at: School of Computer, Chi
430074, China. Tel.: þ86 27 67883286; fax:
ail address: [email protected] (C. Wu).
a b s t r a c t
Suitability evaluation of urban construction land based on geo-environmental factors is the process of
determining the fitness of a given tract of land for construction. This process involves a consideration of
the geomorphology, geology, engineering geology, geological hazards, and other geological factors and
is the basis of urban construction land planning and management. With the support of Geographic
Information Systems (GIS), grid analysis, and geo-spatial analysis techniques, four factor groups
comprising nine separate subfactors of geo-environmental attributes were selected to be used in the
evaluation of the suitability level for construction land in Hangzhou. This was based on K-means
clustering and back-propagation (BP) neural network methods due to their advantages in fast
computing, unique adaptive capacity, and self-organization. Simultaneously, the evaluation results
based on K-means clustering and BP neural network were compared and analyzed, and the accuracy
evaluation was set. The results showed that the geo-environmental suitability evaluation results of
construction land based on K-means clustering and BP neural network were similar in terms of the
distribution and scale of construction land suitability level. At the same time, the results of the two
evaluation methods were consistent with the variability in suitability level, engineering geology, and
hydrogeology of Hangzhou. The results also showed that the real advantage of the methods proposed in
this paper lies in their capacity to streamline the mapping process and to ensure that the results are
consistent throughout. The suitability level of the urban construction land based on the geo-
environment in Hangzhou was divided into four construction sites: land for building super high-rise
and high-rise buildings, land for building multistorey buildings, land for low-rise buildings, and
nonbuilding land. The results of the suitability evaluation for each category will provide a scientific
basis for decision-making in urban development in Hangzhou.
Crown Copyright & 2011 Published by Elsevier Ltd. All rights reserved.
1. Introduction
In 2008, for the first time ever, more than half of the planet’spopulation resided in urban areas (United Nations Population Fund(UNPF), 2007). Urbanization has profoundly transformed naturallandscapes throughout the world (Theobald et al., 2000; Luck andWu, 2002; Chiesura, 2004; Colding, 2007; Tzoulas et al., 2007;Beardsley et al., 2009). Urban areas have the most concentratedhuman activities and the most intensified land use. This land usechange causes drastic disturbances to the environment, and theenvironment presents responses to the engineering activities ofhuman beings in turn (Li, 2000). Meanwhile, inappropriate use of
011 Published by Elsevier Ltd. All
na University of Geosciences,
þ86 27 67883051.
the geo-environment and improper development of geologicalresources are becoming increasingly significant, directly restrictingconstruction and development of the city. For example, excessivepumping of groundwater has led to lowering of water levels andsignificant land subsidence in many urban areas (the largest landsettlement reached 2.63 m from 1921 to 1965 in Shanghai, and2.04 m from 1959 to 1981 in Tianjin, respectively) and the Karstcollapse (Nanning City) (Wu, 1994). Therefore, reducing geo-environmental deterioration of urban land resources has becomea challenge in urban construction. This is why it is critical toconsider geo-environmental factors in evaluating the suitabilitylevel of urban construction land.
To date, most researchers have focused on urban environmentalengineering and geological quality assessment by using multi-variate statistical analysis and GIS (Rybar, 1973; Anonymous,1976; Matula, 1981; Varnes, 1984; Du, 1989; Price et al., 1996;Griffiths, 2002; Cross, 2002; Lee et al., 2004; Lu et al., 2005; Sarkar
rights reserved.
K. Xu et al. / Computers & Geosciences 37 (2011) 992–1002 993
et al., 2007; Turer et al., 2008; Price, 2009). Others have studiedurban geological disaster risk evaluation by using a comprehensiveanalysis method (Mejıa-Navarro and Garcıa 1996; Cook et al.,1998; Jia and Fang, 1999; Yenigul, 2000) and urban land suitabilityanalysis by using fuzzy classification methods and multicriteriaanalysis (Hall et al., 1992; Pereira and Duckstein, 1993; Davidsonet al., 1994; Van Ranst et al., 1996; Store and Kangas, 2001; Wanget al., 2005), as well as urban ecological suitability assessment forurban development and planning by using ecology methods(Lathrop and Bognar, 1998; Svoray et al., 2005; Chen et al., 2006;Liang et al., 2007; Yang et al., 2009). In recent years, the suitabilityevaluation of urban construction land in China has been investi-gated using different methods in different locations, includinggeological classification of foundations and their suitability forhigh-rise buildings in urban areas of Guangzhou (Lin and Ma,1996), geo-environmental evaluation for urban land-use planningin Lanzhou City based on GIS (Dai et al., 2001), construction landsuitability evaluation of Gaochun District in Nanjing (Wang et al.,2005) and Nanchong City (Yang, 1997) with the assistance of theGIS method, construction land geo-environmental quality of Nan-chang City by using fuzzy clustering comprehensive evaluation(Zhang et al., 2007), and Heihe City with a composite indexevaluation model (Lou et al., 2007). All these studies are of greatsignificance in the development of suitability evaluation for urbanconstruction land. Most of these study methods are knowledgedriven. However, subjectivity is an issue with these techniques.There is thus a need to develop expert systems that keep theknowledge-driven advantages while reducing human errors in dataprocessing. Therefore, K-means clustering and the BP neural net-work have been used for their advantages in fast computing,unique adaptive capacity, and self-organization in evaluating andforecasting purposes in various fields, including in urban environ-mental quality evaluation (Lek and Guegan, 2000; Bai et al., 2001).
The objective of this paper is, therefore, to evaluate the geo-environmental suitability level of urban construction land based ongeo-environmental factors and the land use status by usingK-means clustering and BP neural network techniques, as well asto consider the urban development characteristics and expansiondirection of Hangzhou. The evaluation results may provide impor-tant scientific information to improve decision-making for urbanconstruction land planning, management, and use of Hangzhou.
Fig. 1. Map of the study area of Han
2. Methods
2.1. Study area
Hangzhou, the capital of Zhejiang province, is the well-knownhistorical, political, economic, cultural, scientific, and educationalcenter of Zhejiang. It lies to the west of Hangzhou Bay and near thelower reaches of the Qiantangjiang River and the southern end of theJinghang Canal, so it is an important transport hub in southeast China.
The study area is located at 1191400–1201440 east longitudeand 291500–301340 north latitude and covers about 3068 km2
urban area of Hangzhou, including eight districts, which areShangcheng, Xiacheng, Gongshu, Jianggan, West Lake, Binjiang,Xiaoshan, and Yuhang (Fig. 1). The west, middle, and south studyareas belong to the middle-low mountainous and hilly regions ofWest Zhejiang, where the Karst land and banded valley flats arequite common. In contrast, the northwest study area belongs tothe northern plain of Zhejiang, where the land is low and flat, iscovered with a dense river network, and is part of Hangjiahu Plainand Ningshao Plain, a typical ‘‘southern rivertown.’’
The complicated terrain and landforms of the study area havebeen impacted by drastic paleoclimate changes, multiphasetectonic movements, as well as alluviation and deposition in therecent past of the Tiaoxi River and the Qiantangjiang River. As aresult, the study area has many sedimentary deposit types,various and complicated facies changes, vertically alternating softand hard soil layers, and large variations in thickness of complexQuaternary stratigraphic sequences.
2.2. General approach
Seven procedures were used in evaluating the suitability ofHangzhou for urban construction land based on geo-environmen-tal factors. First, the suitability evaluation factor index system forconstruction land was confirmed based on geo-environmentalfactors. Second, the suitability evaluation units for constructionland were divided into 250�250 m2 raster grid cells by usingMapGIS software. Each cell was considered as a homogeneousunit for any given factor. Third, 11 thematic maps of Hangzhouwere collected as data sources in this study included: geological
gzhou, Zhejiang Province, China.
K. Xu et al. / Computers & Geosciences 37 (2011) 992–1002994
bedrock at 1:100,000, landforms and Quaternary at 1:100,000,tectonic outline at 1:200,000, depth-to-bedrock contour at1:200,000, hydrologic geology with 1:200,000, geological map ofthe Karst subregion at 1:25,000, stability sectional map of Karstaround the West Lake at 1:25,000, engineering geology at1:100,000, foundation load at 1:300,000, typography interpreta-tion map of remote sensing image, and urban environmentalgeochemistry survey map. Fourth, spatial and attribute databasesof different geo-environmental factors were established by thevector-based approach using MapGIS software. Fifth, all thevector maps that reflect different geo-environmental factors werestandardized and overlaid in the analysis with the 250�250 m2
grid cells to obtain a score for each subfactor in each grid unit,respectively. Sixth, the weights of each factor and subfactor wereobtained by different experts and the Analytic Hierarchy Process(AHP) method (Banai-Kashani, 1989; Saaty and Vaargas, 1991;Carver 1991; Eastman et al., 1995; Bandyopadhyay et al., 2009),and then an integrated value of evaluation unit was calculated(see Eqs. (1)–(3)). Finally, the evaluation results for constructionland suitability were obtained by K-means clustering and BPneural network methods, as well as by the transforming ofMapGIS. Our overall strategy of analysis is summarized in theflowchart shown in Fig. 2.
Confirmation of suitability evaluation geo-environm
Establishment of spatialVectorize mapsEstablish attribute databaseRasterize vector maps
Division the evaluation unit
Collection and colgeological maptectonic outline mapbedrock depth contour maplandform and Quaternary geologichydrologic geological mapgeological map of Karst sub-regionstability sectional map of Karst aroengineering geology mapfoundation load maptypography interpretation map of rmulti-media urban environmental g
Standardization oEnsure evaluation factor clAssigned to each sub-facto
Computation of weigCalculate weights for eachCalculate integrated values
Multi-criteriaK-means clusteringBack-Propagation (BP) neEvaluation results analysis
Fig. 2. Flowchart of suitability evaluation for constru
2.3. Establish evaluation factors system
Urban construction land suitability level is a result of theinteractions of each evaluation factor, so proper selection of evalua-tion factors is critical to ensure meaningful suitability evaluationresults for construction land (Chen and Xu, 1997; Joerin et al., 2001).Integrating the urban construction land characteristics of Hangzhouand the special requirements for suitability evaluation, four factorgroups comprising nine separate and sensitive geo-environmentalsubfactors were selected from the above-noted maps for a suitabilityevaluation of Hangzhou urban construction land using AHP: geo-morphic type, slope, site soil type, Holocene saturated soft soildepth, stratum steadiness, groundwater salinization, groundwaterabundance, geological hazard type, and geological hazard degree.They form a two-level hierarchical structure according to theirsubordinate relationship (Fig. 3).
In the geo-environmental suitability evaluation process ofconstruction land, a primary step is to ensure a standardizedmeasurement system according to the degree of importance of allfactors in the evaluation (Dai et al., 2001). Second, it is importantto analyze the relationship between the geological environmentalfactors and the urban land use and development. Table 1 showsthe compatibility between the geological environmental factors
ental factors index system for construction land
attribute databases
using MapGIS software
lation of data
al map
und West Lake
emote sensingeochemistry survey map
f sub-factorsassified standardrs
hts of sub-factors sub-factors for each sub-factors
evaluation
ural network
ction land based on geo-environmental factors.
Geomorphology Engineering geology Hydrological geology Geologic hazard
Geo-environmental construction land suitability evaluation factors
Geomorphic type Slope Site soil type Stratum steadiness
Holocene saturated soft soil depth Groundwater salinization
Groundwater abundance Geologic hazard degree
Geologic hazard type
Fig. 3. Geo-environmental construction land suitability evaluation factor system.
K. Xu et al. / Computers & Geosciences 37 (2011) 992–1002 995
and the construction land in Hangzhou. Table 1 shows that thecloser the compatibility, the better the conditions for urbandevelopment in Hangzhou.
Each subfactor was scored using a paired comparison accord-ing to its influence on construction land geo-environmentalsuitability. In this study, construction land suitability was firstclassified into five levels: levels I, II, III, IV, and V. These five levelswere assigned scores of 9, 7, 5, 3, and 1, respectively. A positivecorrelation between the value awarded and the suitability wasemployed. In other words: the greater the score, the higher thelevel for development of urban construction land (Zhang et al.,2009). Table 2 shows the class boundaries and standardizedmeasurements employed for each factor. It should be noted thatvarious statistical and empirical guidelines from the relatednational codes and the literature were used in determining theboundary values for the urban construction land evaluation.
This study organized four geological factors and nine subfactorsinto an ordered two-level hierarchical structure with AHP. Therelative importance of each factor and subfactor was identifiedusing a paired comparison. Their weight was decided according totheir relative importance ranking with AHP. In addition, wedesigned a weight survey table for 100 experts who were familiarwith the geological environment of Hangzhou, and we asked themto assign a weight for each the geo-environmental factor accordingto their impact on the construction land in Hangzhou. That is thehigher the weight of the factor, the greater the impact of itsimportance for urban construction land; conversely, the lower theweight of the factor, the lower the impact. Also, these principlesare true to the subfactors of his parent factors. In other words, theweights of the subfactors are based on their impact on theimportance of the parent factors, and their total weights are 100,too. We obtained the weight of each factor and subfactor byweighing the average of all the experts’ survey tables. Therefore,the final weight of each factor and subfactor also took into accountthe score given by the experts and AHP (Table 3), which providedthe needed data for the suitability evaluation of the Hangzhouconstruction land based on the geo-environmental factors.
2.4. Divide evaluation units
An evaluation unit is the basic spatial unit for the geo-environmental suitability evaluation of urban construction land(Kalogirou, 2002). The evaluation unit division aims to objectivelyreflect the spatial difference in construction land geo-environ-mental quality. According to the uniformity and difference prin-ciple of land quality, the study area was divided into grids of250�250 m2 as an evaluation unit with MapGIS software beforethe geo-environmental suitability evaluation of the urban con-struction land in Hangzhou. The evaluation unit division helps toperform spatial overlay and model calculation of attribute data,while reducing the subjectivity of the evaluation process.
Moreover, each grid is an information retrieval source as well asa unit for showing the evaluation results. The study area wasdivided into 56,332 raster grid cells in Hangzhou.
All the maps listed above were, respectively, overlaid with theanalysis by a 250�250 m2 grid to obtain a score for eachsubfactor in each grid unit. Based on each subfactor’s score, anattribute database of the subfactor system for the suitabilityevaluation of Hangzhou construction land was established.
2.5. Assign the score for the evaluation subfactors
The geo-environmental suitability level of urban constructionland tries to summarize the complex interaction among manyfactors and subfactors. If the influence of each factor and sub-factor is assigned a certain score and weight, the accumulatedresults will reflect the comprehensive influence of the construc-tion land based on a grid.
It is assumed that m factors have been selected for theevaluation, and that each factor includes n subfactors. Then eachfactor’s score in a construction land evaluation unit equals theweighted sum of each subfactor’s score. That is
Pi ¼Xn
j ¼ 1
FjWj, ð1Þ
where Pi refers to the score of factor i, Fj refers to the score ofsubfactor j of factor i, and Wj refers to the weight value ofsubfactor j.
If P is the total score of a construction land evaluation unit andWi is the weight value of factor i, the total score of P is
P¼Xmi ¼ 1
PiWi: ð2Þ
Taking Pi into Eq. (2), we get
P¼Xmi ¼ 1
Xn
j ¼ 1
FjWjWi: ð3Þ
Four factors were selected: topography, engineering geology,hydrogeology, and geological hazard; so m¼4. The four factorswere subdivided into nine subfactors: geomorphic type, slope,soil type, stratum steadiness, Holocene saturated soft soil depth,groundwater abundance, groundwater salinization, geologicalhazard type, and geological hazard degree; so n¼9.
2.6. Based on K-means clustering suitability evaluation
The calculation was performed with K-means clustering ofStatistical Product and Service Solutions (SPSS) based on the aboveestablished model. In fact, K-means clustering (MacQueen, 1967) isa method commonly used to automatically partition a data set intok groups (Hartigan and Wong, 1979; Bradley and Fayyad, 1998;
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K. Xu et al. / Computers & Geosciences 37 (2011) 992–1002996
Wagstaff et al., 2001). It proceeds by selecting k initial clustercenters and then iteratively refining them as follows:
(1)
Each instance d is assigned to its closest cluster center. (2) Each cluster center K is updated to be the mean of itsconstituent instances.
The algorithm converges when there is no further change inassignment of instances to clusters.
In this work, we initialize the clusters using instances chosen atrandom from the dataset. The calculation of K-means clustering isfirst, specify the cluster category number and the four categories asdefined; i.e., the five-level evaluation result will turn into fourlevels. Second, identify the centers of K initial categories. SPSS canchoose a K representative dataset of geo-environmental subfactorsas initial category centers based on the actual dataset situation.
The Euclidean distance of all datasets to K category centers iscalculated as follows:
EUCLID¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXk
i ¼ 1
ðxi�yiÞ2
vuut : ð4Þ
SPSS classified all datasets of geo-environmental subfactorsinto the categories where each center locates according to theshortest distance between K category centers, and thus, a new K
category is formed. In this study, K¼4, and the iterative processwas finished one by one. The work flow is as follows (Fig. 4).
The geo-environmental suitability of the Hangzhou urbanconstruction land was divided into four levels: a constructionland suitability zone I suitable for super high-rise and high-risebuildings, a construction land suitability zone II suitable formultistorey buildings, a construction land suitability zone IIIsuitable for low-rise buildings, and construction land suitabilityzone IV not suitable for buildings.
2.7. Based on BP neural network suitability evaluation
Back-propagation neural network is a feed-forward connectionmodel formed with multilayer perception. Neurons at the samelayer are not connected with each other, but neurons at adjacentlayers are connected with the weight. The multilayer back-propagation algorithm, namely the BP algorithm, is most widelyused (Rumelhart et al., 1986; Salomon and Hemmen, 1996) for itsself-adaptive learning in a given environment.
According to the urban geo-environment and constructionland characteristics of Hangzhou, the BP neural network modelof geo-environmental suitability evaluation of urban constructionland was established in this study (Fig. 5). Nine subfactors werechosen for the input layers according to the evaluation factorssystem. Four neurons in the hidden layer were identified accord-ing to the number of predetermined Hangzhou urban construc-tion land levels, and one output neuron was decided according tothe predetermined Hangzhou urban construction land suitabilitylevel. Therefore, there were nine neurons in the input layer, oneneuron in the output layer, and four neurons in the hidden layerin this BP neural network. At the same time, nodes at the samelayer are not connected, nodes at different layers form a focus-likeconnection path, and weight value represents the connectionstrength between adjacent nodes in this model.
In the evaluation process for construction land, the model didnot need to understand the relation between input subfactors andoutput subfactors, as it could ‘‘memorize’’ the information on thesample with its self-learning function. The BP neural networkmodel could automatically search the relations and give mathe-matical expression to the suitability evaluation according to thedata of the training sample. As a result, the BP neural network
Table 2Construction land suitability evaluation grading standards based on the geo-environment in the study areas.
No. Construction
grade
9 7 5 3 1
1 Geomorphic type Plain region Coastal plain region Middle-low mountainous and
hilly region
Valley area of
mountains
–
2 Slope o21 2–51 5–151 15–251 4251
3 Site soil type I II III – IV
4 Holocene saturated soft
soil depth
0 m 0–10 m 10–20 m 20–30 m 430 m
5 Stratum steadiness Tiaoxi River
alluvial plain
subregion (Renhe
County and
Pingyao County )
Hard and relatively hard
pyroclastic rock subregion, and
relatively hard clastic rock
subregion, Tiaoxi River alluvial
plain subregion (main city area)
Karst Carbonate subregion,
Qiantangjiang River alluvial plain
subregion, and Tiaoxi River
alluvial plain subregion (Linping-
Tangqi east area)
Soft and hard uneven
clastic rock
subregion, and
Puyangjiang River
alluvial plain
subregion
Flood valley
deposit in
mountainous
subregion, and
residual slope
deposit subregion
6 Groundwater salinization Fresh water Fresh water above and saline water
under
Saline water above and fresh
water under
Slightly saline water Saline water
Phreatic
water
o100 m3/d – 100–1000 m3/d – 41000 m3/d
Confined
water
o100 m3/d 100–1000 m3/d 1000–3000 m3/d – 43000 m3/d
7 Groundwater
abundance
Carbonate
rock
fissure
crave
water
– Bare spring discharge o1.0 L/S – Bare spring discharge
41.0 L/S, and
covered spring
discharge 41.0 L/S
–
Bedrock
fissure
water
– Spring discharge o1.0 L/S – Spring discharge
41.0 L/S
–
8 Geological hazard type Earthquake Earthquake Debris flow, landslide, and
surface collapse
Debris flow,
landslide, and surface
collapse
Debris flow,
landslide, and
surface collapse
9 Geological hazard degree Hardly – Low Middle High
Table 3Weights of factors for construction land suitability evaluation based on the geo-environment.
Evaluation factors Weight (%) Evaluation subfactors Weight (%) Percentage (%)
Geomorphology 25 Geomorphic type 40 10
Slope 60 15
Engineering geology 45 Site soil type 10 4.5
Stratum steadiness 70 31.5
Holocene saturated soft soil depth 20 9
Hydrological geology 15 Groundwater salinization 40 6
Groundwater abundance 60 9
Geological hazard 15 Geological hazard type 40 6
Geological hazard degree 60 9
Total 100 – – 100
K. Xu et al. / Computers & Geosciences 37 (2011) 992–1002 997
model established in this study can accurately reflect the urbanconstruction land level.
Five hundred and forty-nine training samples were selectedrandomly in consideration of a balanced regional distribution anda uniform distribution of evaluation factors and levels in order totrain the BP network by using Matlab 7.0. The actual output valueof the network is compared to the expected output value tocalculate whether the error is within the tolerance. If thedifference is within the tolerance, the network mapping result isconsidered the final result; if not, the input and output para-meters of the model will be normalized as a group of new trainingsamples and added to the original sample set to have networktraining again until the error is within tolerance. The optimalassessment of the BP neural network is determined after thenetwork models have repeated debugging training and testing; atthe same time, the convergence standard is the fastest and theprediction error is the smallest.
After repeating debugging training on the network model withthe 549 training samples, the BP neural network with 0.01 step
size, 0.001 system accuracy, and 1000 iterations was found toprovide the best approximation to the function, the smallesterror, and the least network training time.
The results of the geo-environment suitability evaluation ofthe urban construction land of Hangzhou were obtained using thetrained BP neural network model to calculate the input charac-teristics value of 56,332 evaluation units of Hangzhou urbanconstruction land.
3. Results
3.1. Results of suitability evaluation based on K-means clustering
and BP neural network
Figs. 6 and 7 show the results of the Hangzhou constructionland geo-environmental suitability evaluation based on K-meansclustering and the BP neural network, respectively.
Confirm suitability evaluation factor index system for construction land based on geo-environment
Evaluation factors occurrence actuality Ensure evaluation factors classified standard
Evaluation factors value Evaluation factors weight
Calculate integrated values for evaluation unit
K-Means clustering
Results of geo-environmental construction land suitability evaluation
Fig. 4. Flow based on K-means clustering geo-environmental construction land suitability evaluation.
Output layerHidden layerInput layer
Evaluation factor
Slope
Stratum steadiness
Groundwater salinization
Geological hazard type
Geomorphic type
Site soil type
Holocene saturated soft soil depth
Geological hazard degree
Groundwater abundance
Error
Weight amend
Export
Fig. 5. BP neural network model of construction land geo-environmental suitability evaluation.
K. Xu et al. / Computers & Geosciences 37 (2011) 992–1002998
Construction land geo-environmental suitability zone I hasgood geological conditions, mainly associated with the lakealluvial plain subregion of Tiaoxi River, with slope o21, saturatedsoft soil depth less than 10 m, most site soil of grades II and III,saline or slightly saline groundwater. Water inflow is small, andthe terrain is not prone to geological disasters. It is mainlydistributed in Tangqi District, Liangzhu District, and GongshuDistrict of the major urban area. This area is surrounded by manyscenic sites, and the geological environment is suitable forurbanization and thus has great development potential for superhigh-rise and high-rise buildings.
Construction land geo-environmental suitability zone II has acomparatively strong bearing stratum and relatively good geolo-gical conditions, i.e., mostly distributed in the plains region, lakealluvial plains subregion of Tiaoxi River, and ocean alluvial plainssubregion of the Qiantangjiang River. The slope is o21, Holocenesaturated soft soil depth is about 10–20 m, and site soil of gradeIII. Except for the freshwater distribution in Yunhe County, therest of the zone II area is saline water or slightly saline waterdistribution; the water inflow of Sandun County, ChongxianCounty, Kangqiao County, and north Hezhuang County is43000 m3/day, and the water inflow in the rest of the zone IIarea is low and hardly prone to geological disasters. It is mainlydistributed in the plains of major urban areas and in the north andwest Yuhang District, including Dingqiao County, Qiaosi County,
Shiqiao County, Shangtang County, Sandun County, ChongxianCounty, Kangqiao County, and Yunhe County. In addition, it is alsosparsely distributed in Guali County, east Dangshan County, andnorth Hezhuang County in the Xiaoshan District. This area coversa large territory and has great development potential for multi-storey buildings. It also conforms to the current land use statusand future construction land planning for science and technologydevelopment, conferences and trade fairs, high-tech industry,travel, and leisure.
Construction land geo-environmental suitability zone III hascommon geological conditions and has many restrictions forbuildings, i.e., mostly distributed in the coastal plains region,the Qiantangjiang River ocean alluvial plain subregion, the FuyangRiver lake alluvial plains subregion, the middle-low mountainousand hilly region, and the hard and relatively hard pyroclastic rocksubregion. It has a steeper slope from 21 to 51, with distribution of0–30 m Holocene saturated soft soil depth and site soil grades I, II,and III. Except for the water reservation area, which has freshwater, the rest of the zone III area has generally saline water orslightly saline water, confined and unconfined aquifers fracturedrock aquifers with spring discharge o1.0 L/S. It is mainly dis-tributed in the Xianshan District and on both sides of theQiantangjiang River, especially in the east Xianshan District alongthe river. In addition, it is also sparsely distributed in the JingshanScenic Resort and water reservation areas. The area is only
Fig. 6. Results of Hangzhou construction land geo-environmental suitability evaluation based on K-means clustering.
Fig. 7. Results of Hangzhou construction land geo-environmental suitability evaluation based on the BP neural network.
K. Xu et al. / Computers & Geosciences 37 (2011) 992–1002 999
suitable for low-rise buildings because the bearing stratum here isgenerally made up of soft soil.
Construction land geo-environmental suitability zone IV is notsuitable for construction development because of the steep slopesand poor geological conditions. It is mainly distributed in thesouthwest hills of the study area and sparsely distributed in thesurrounding hills of the West Lake scenic spot and waterreservation areas mainly including Baizhang County, LuniaoCounty, Jingshan County, Zhongtai County, Xianlin County, LiuxiaCounty, Longwu County, Zhuantanag County, Lishan County,Heshang County, and Jinhua County.
3.2. Comparison of K-means clustering and the BP neural network
Table 4 shows the comparison results of the geo-environmen-tal suitability evaluation results of the Hangzhou constructionland based on K-means clustering and the BP neural network.
We can see, from Table 4, that the geo-environmental suit-ability evaluation results of construction land based on K-meansclustering and the BP neural network are similar in terms of thedistribution and scale of construction land suitability level. Table 4shows: (1) the area of construction land geo-environmental suit-ability zone I is 321.74 and 330.12 km2, accounting for 10.48% and
Table 4Suitability evaluation results comparison based on K-means clustering and the BP neural network.
Model Suitability level Suitability zone I Suitability zone II Suitability zone III Suitability zone IV
K-means clustering Area (km2) 321.74 722.81 1412.41 611.01
Percentage (%) 10.48 23.56 46.04 19.92
BP neural network Area (km2) 330.12 692.29 1447.36 598.23
Percentage (%) 10.76 22.56 47.18 19.5
Table 5Comparison analysis between construction land geo-environmental suitability results and distribution of single geo-environmental factors in Hangzhou.
Suitability
level
Suitability zone I Suitability zone II Suitability zone III Suitability zone IV
Geomorphic
type
Zones I and II distributed in plain. Mainly distributed in Xiaoshan District,
the plains on both sides of
Qiantangjiang River, and also
distributed in middle-low mountainous
and hilly regions.
Valley area of mountains and
middle-low mountainous and hilly
region.
Slope Zones I and II distributed in plain. Zones III and IV mainly distributed in area of steep slope.
Site soil type Site soil is generally Grades
II and III.
Zone II is generally located in the area
with grade III site soil, and sparsely
distributed in the area with grade II
site soil.
Zone III can be found in the area of
grades I, II, and III site soil, with poor
consistency with site soil grade.
Zone IV can be found in the area of
grade I and II site soil, with poor
consistency with site soil grade.
Holocene
saturated
soft soil
depth
r10 m, zone I distribution
similar to the soft soil depth
distribution.
Most areas are about 0–20 m except
the north part of Hezhuang county,
which is 20–30 m.
Various Holocene saturated soft soil
depth ranges from 0 to 30 m.
Similar to exposed bedrock
distribution.
Stratum
steadiness
Most areas are in Tiaoxi
River alluvial plain
subregion, zone I
distribution similar to
engineering condition
distribution.
Tiaoxi River alluvial plain subregion
and Qiantangjiang River alluvial plain
subregion.
Coastal plain region, Qiantangjiang River
alluvial plain subregion, Puyangjiang
River alluvial plain subregion, middle-
low mountainous and hilly regions, hard
and relative hard pyroclastic rock
subregion, and relative hard clastic rock
subregion.
Flood deposits of valley area in
mountainous subregion, flood
alluvial plain subregion in river
valley, and eroded and abrased
clastic rock deposit in middle-low
mountainous and hilly subregions.
Groundwater
salinization
Groundwater is generally
saline or slightly saline;
zone I is inconsistent with
distribution of groundwater
salinization.
All zones II has distribution of saline
water or slightly saline water except
that Yunhe county has freshwater
distribution; zone II is inconsistent
with groundwater salinization
distribution.
All zone III has saline water or slightly
saline water distribution, except that
water reservation area has freshwater
distribution; zone III is consistent with
groundwater salinization distribution.
Groundwater is mostly fresh water.
Groundwater
abundance
Zone I is consistent with the
lower groundwater
abundance area.
Zone II is consistent with groundwater
abundance area.
Zone III has poor consistency with
ground water abundance.
Zone IV is consistent with
distribution of groundwater
abundance.
Geological
hazard
degree
Zone I is in the area which is
rarely prone to geological
hazards.
Most zones II is the area which is
barely prone to geological hazards.
Zone III is generally in the area which is
barely prone to geological hazards, a
small portion of middle-low
mountainous and hilly region is barely
prone to earthquakes.
Easily prone to geological hazards,
including distribution in areas
highly, middle, and barely prone to
geological hazards.
K. Xu et al. / Computers & Geosciences 37 (2011) 992–10021000
10.76% of the total area, respectively, and this result indicates thatthere is only about 10% area suitable for super high-rise and high-rise buildings in Hangzhou; at the same time, we can see that thedifference of results is the smallest between the K-means cluster-ing and the BP neural network for construction land geo-environ-mental suitability zone I, which is only 0.28%; (2) the area ofconstruction land geo-environmental suitability zone II is 722.81and 692.29 km2, accounting for 23.56% and 22.56% of the totalarea, respectively, and this result indicates that there is about 23%area suitable for multistorey buildings; (3) the area of constructionland geo-environmental suitability zone III is 1412.41 and1447.36 km2, accounting for 46.04% and 47.18% of the total area,based on K-means clustering and the BP neural network, respec-tively; and this result indicates that most of the area in Hangzhouis suitable for low-rise buildings; we can see that the difference ofresults is the biggest between the K-means clustering and the BPneural network for construction land geo-environmental suitabilityzone III, which is 1.14%; (4) the area of construction land geo-environmental suitability zone IV is 611.01 and 598.23 km2,accounting for 19.92% and 19.5% of the total area, respectively,and this result indicates that there is about 20% area not suitable
for buildings in Hangzhou. Table 4 also demonstrates that mosturban areas of Hangzhou are suitable for construction land devel-opment. In addition, the evaluation results are consistent withcurrent land use. This is also confirmation that the current land usestructure is more reasonable in Hangzhou (Zhang et al., 2009). Thedifferences of evaluation results are little between the K-meansclustering and the BP neural network from the above comparison.Further field investigation shows that both methods are effectivefor construction land suitability evaluation in Hangzhou
3.3. Comparison between evaluation results and distribution of geo-
environmental factors
The distribution of different construction land geo-environmen-tal suitability levels is closely related to the distribution of singlegeo-environmental factors. Their consistency reflects the significantinfluence of leading factors, while the difference reflects the com-plementary role of other relative factors. The distribution chart ofthe geo-environmental suitability evaluation results for constructionland is compared with the distribution chart for a single geo-environmental factor, and their relationship is shown in Table 5.
K. Xu et al. / Computers & Geosciences 37 (2011) 992–1002 1001
Table 5 demonstrates that the geo-environmental suitabilityevaluation results for the urban construction land of Hangzhouare consistent with the distribution of geo-environmental condi-tions, particularly highly consistent with geomorphic type, slope,engineering geological lithology, Holocene saturated soft soildepth, stratum steadiness, bearing stratum, groundwater abun-dance, and geological hazard degree; but barely consistent withsite soil type and groundwater salinization. It can be concludedthat the results of the evaluation of Hangzhou urban constructionland geo-environmental suitability based on the BP neural net-work are from the integrative action of leading factors along withother complementary factors.
4. Discussion
Geo-environmental suitability evaluation of urban constructionland is a comprehensive evaluation and involves many influencingfactors that interact with each other. Because of the complicatedspatial variability of various factors and subfactors, processing byhuman beings is arbitrary and subjective, and the work is difficultand time consuming. But the application of GIS, grid analysis,K-means clustering, and the BP neural network in the geo-environmental suitability evaluation of urban construction landcan help reduce human errors in data processing. At the same time,results also showed that the real advantage of the methodsproposed in this paper lies in their capacity to streamline themapping process and to ensure that the results are consistentthroughout (as determined by the physical criteria and theirweighing factors). Factors influencing geo-environmental suitabil-ity evaluation are a complicated system, and classification criteriaof evaluation indices directly impact the evaluation results. There-fore, the classification should be defined by the features of eachstudy area. Different evaluation methods will lead to differentevaluation results even for the same area and with the same factorsselected. As a result, model establishment is the core of the studyand will directly impact the geo-environmental suitability evalua-tion results for the urban construction land of Hangzhou.
The two models established in the study take a grid cell as anevaluation unit, take full advantage of various existing geologicalsurvey results of Hangzhou, and comprehensively consider variousgeo-environmental factors influencing Hangzhou urban construc-tion land use. The two methods have their own unique features.Suitability evaluation based on K-means clustering is simple andfast in calculation, and its key problem is the weight definition ofeach factor and subfactor. But it has shortcomings such as beingvulnerable to the impact of the initial cluster conditions, easilyfalling into the local minimum. Suitability evaluation based on theBP neural network involves few human elements, and its keyproblem is in dataset selection. Compared with the classic max-imum-likelihood method, the biggest advantage of the BP neuralnetwork is that there is no requirement for a normal distribution ofa training sample. At the same time, in the evaluation process forconstruction land, the model does not need to understand therelation between input subfactors and output subfactors, as it can‘‘memorize’’ the information on the sample with its self-learningfunction. The BP neural network model could automatically searchthe relations and give mathematical expression to the suitabilityevaluation according to the data of the training datasets. However,the model does have disadvantages such as a hard-to-decidestructure and being easily subject to local minimum, which makesit difficult to achieve a calculation convergence.
The results of the two evaluation methods are consistent withthe variability of the suitability level, engineering geology, andhydrogeology of the plains region. On the one hand, evaluationcan help make full use of the land resources represented by
environmental conditions, while on the other hand, throughminimizing the restrictions on environmental conditions, evalua-tion can help to optimize the allocation of urban construction,geological resources, and environment. In this way, a safe, reason-able, and economic urban construction can be reached, providinga scientific basis for decision-making in the urban developmentand construction land development of Hangzhou.
However, geo-environmental suitability evaluation factorsinfluencing urban construction land are complicated. Therefore,whatever mathematical methods are adopted for geo-environ-mental suitability evaluation of urban construction land, we mustmake it clear that evaluation factors, factor grading, index assign-ment, and factor weight are critical to the evaluation results.A scientific and objective evaluation conclusion is possible onlywhen each evaluation step and method is properly processed.
Acknowledgments
This work has been supported from the Special Fund for BasicScientific Research of Central Colleges (No. CUGL090232), Key Lab ofBiogeology and Environmental Geology of Ministry of Education(No. BGEG1014), China University of Geosciences, Wuhan; Hang-zhou Land Use Suitability Research, Subproject of Hangzhou UrbanGeology Survey of Welfare Geology Survey Project Funded byNational Geological Bureau (No. 200413000021); and the Fund forGeological Survey of China Geological Survey (No. 1212010880404).The authors thank the anonymous reviewers for providing valuablecomments on the manuscript.
Appendix A. Supplementary material
Supplementary data associated with this article can be foundin the online version at doi:10.1016/j.cageo.2011.03.006.
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