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Application of artificial neural network in metropolitan landscape
 

Application of artificial neural network in metropolitan landscape

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    Application of artificial neural network in metropolitan landscape Application of artificial neural network in metropolitan landscape Document Transcript

    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME623APPLICATION OF ARTIFICIAL NEURAL NETWORK INMETROPOLITAN LANDSCAPE WATER EMINENCE ASSESSMENTManoj B. Karathiya1, Deven J. Patel2,Parag C. Shukla31(MCA Department, AITS, Rajkot, India)2(MCA, Department, AITS, Rajkot, India)3(MCA, Department, AITS, Rajkot, India)ABSTRACTWater eminence model is a helpful utensil to assess the prospect state of river waterthrough assessment of real pollution loading or singular management options. Based on thehuman brain physiology research, artificial neural network (ANN) which replicates thestructure and mechanism of the human brain is a type of dynamic information processingsystem that finally accomplishes positive functions of the human brain. Now a day, there arehundreds of neural network approaches and also Back Propagation neural network is one ofthe most frequently used techniques in current days, whose application in environmentaleminence assessment of environmental science field was presented here. Back Propagationneural network was used to create the landscape water eminence assessment system. Herealso presented an optimization method of Back Propagation artificial neural network, whichgenerally used MATLAB programming whose toolbox afforded a function of BackPropagation network to avoid complex mathematical calculations and cumbersome codeeditor. On the base of sampling and examining of water eminence constantly, artificial neuralnetwork was used here to create landscape water assessment model in order to estimate thelandscape water eminence neutrally and speedily.Keywords: Back Propagation artificial neural network, landscape water eminenceassessment, MATLABINTERNATIONAL JOURNAL OF COMPUTER ENGINEERING& TECHNOLOGY (IJCET)ISSN 0976 – 6367(Print)ISSN 0976 – 6375(Online)Volume 4, Issue 2, March – April (2013), pp. 623-631© IAEME: www.iaeme.com/ijcet.aspJournal Impact Factor (2013): 6.1302 (Calculated by GISI)www.jifactor.comIJCET© I A E M E
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME624INTRODUCTIONWith the growth of city as well as peoples increasing necessities on environmentalworth, the Landscape Rivers and canals play a significant role in civilizing and beautifyingliving environment. Yet, these landscape rivers and canals, which are commonly static orclosed slow-flow water bodies, are simple to be polluted for being small water areas, havinglittle environmental capacity and small self-purification capacity. By research proves thatmore than 90% of the landscape water bodies in Gujarat are subject to varying degrees ofpollution. Hence, protection, subrogation and supplementary of landscape water has becomea significant question in many cities particularly the cities which be short of water critically.Landscape River of 33 km long studied, after the conclusion of the landscape river,the superposition impact of the non-point source pollution in surface runoff and sewagedischarge of the individual enterprises result to serious water eminence corrosion, and at thesame time, low environmental capacity and ecological carrying capacity of landscapes alsogreatly reduces landscape function. Hence, it is essential to build an inclusive assessment ofthe polluted landscape water body, and take the assessment into daily management to afford abasis for landscape river pollution control and water eminence management.Now a day, between all the water eminence assessment approaches, the classicapproaches are single-factor evaluation method, multi-factor key evaluation method, fuzzymathematical evaluation method, gray system evaluation method, analytic hierarchy process(AHP), matter-element analysis, artificial neural network evaluation method and recentlyproposed approaches such as water quality identification key method, gray fuzzy clusteringevaluation method. Each has their individual compensations and drawbacks. For example, theresult of key assessment is frequently incompatible with the data of water eminenceoperational examined, and parameters of different key formula are frequently dissimilar too.The drawback of gray assessment is small motion. With complex assessment process andpoor operability, Fuzzy assessment results are not equivalent. And AHP has troubles of lowresolution and illogical assessment results. Hence, Back Propagation artificial neural networkbecomes one of the mainly important and widely used models due to its particularcompensations.Now a day, between all the water eminence assessment approaches, the classicapproaches are single-factor evaluation method, multi-factor key evaluation method, fuzzymathematical evaluation method, gray system evaluation method, analytic hierarchy process(AHP), matter-element analysis, artificial neural network evaluation method and recentlyproposed approaches such as water quality identification key method, gray fuzzy clusteringevaluation method. Each has their individual compensations and drawbacks. For example, theresult of key assessment is frequently incompatible with the data of water eminenceoperational examined, and parameters of different key formula are frequently dissimilar too.The drawback of gray assessment is small motion. With complex assessment process andpoor operability, Fuzzy assessment results are not equivalent. And AHP has troubles of lowresolution and illogical assessment results. Hence, Back Propagation artificial neural networkbecomes one of the mainly important and widely used models due to its particularcompensations.Back Propagation artificial neural network initially can improve computing speed ofalgorithm, secondly it is conductive to overcoming the prejudice brought about otheralgorithms of water eminence assessment, and thirdly it can resolve the non-linear complexrelationship problems of research object.
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME625Back Propagation artificial neural network initially can improve computing speed ofalgorithm, secondly it is conductive to overcoming the prejudice brought about otheralgorithms of water eminence assessment, and thirdly it can resolve the non-linear complexrelationship problems of research object.I. WATER EMINENCE EXAMINING AND DETERMINATION OF THEASSESSMENT POINTERSLANDSCAPE WATER EMINENCE EXAMINING1. CHOICE OF SAMPLING SITESSix sampling sites are chosen on the base of field research survey and the layoutprinciple of sampling combining the need of contents. On the upstream river, there is arainfall pumping location. As a major pollution source of the landscape river, pumpinglocation mostly discharges flood season rainwater into the rivers as well as the wateraccumulated in pipeline. Thus sampling site one is chosen on the outlet section of the rainfallpumping location, and two to six sampling points are correspondingly preferred along waterflow direction. Among them, sampling site two is preferred near an industrial outfall, pointthree and four are selected close to each other, in order to learn the role of aquatic plants onthe degradability of pollutants, and section three is selected on the reach with lush aquaticplants, while section four is on the reach fundamentally lacking aquatic. Also site two, fiveand six are selected on the downstream river. Examining on these Sampling site is carriedconstantly.2. INVESTIGATION AND MONITORING WATER EMINENCE KEYSAccording to Surface Water Environment eminence Standard of Gujarat, Indiacombined with the actual pollution situation of the landscape river as well as the analysismonitoring and experimental conditions, eight water eminence keys including watertemperature, dissolved oxygen (DO), pH, total nitrogen, ammonia nitrogen, total phosphorus,and chemical oxygen demand (COD), and chloride are monitored. All the keys are examinedaccording to the standard method of India. Table 1 shows part of the monitoring data.Table – 1 Standard Monitoring results of the landscape river water eminence in November2012SamplingSiteDissolveOxygen(Mg / Ltr)NH3-N(Mg / Ltr)Chloride(Mg / Ltr)ChemicalOxygenDemand(Mg / Ltr)TN(Mg / Ltr)TP(Mg / Ltr)1 4.30 6.16 442 16 3.67 0.602 3.58 3.92 464 24 2.98 0.523 3.47 6.16 802 70 4.26 0.574 2.28 1.12 542 40 3.19 0.515 7.38 0.28 162 4 4.55 0.596 3.28 5.04 536 28 2.79 0.51
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME626ASSESMENT CRITERIA AND DETERMINATION OF THE ASSESMENT KEYSIndia surface water environmental eminence standard as Table 2 shown is used as thelandscape water eminence assessment criteria. Complementary to the standard, it can be seenthat among all the data examined at the six sampling sites, the concentration of TN, COD,NH3-N do exceed the criteria for class V. Generally speaking, when the concentration of totalphosphorus and inorganic nitrogen in water body reaches 0.02mg/L and 0.3mg/Lrespectively, it marks that water is eutrophicated. Therefore, four water eminence keysincluding DO, NH3-N, Chloride, COD, TN, COD, and TP are selected as water eminenceassessment keys.According to the Indian surface water environmental eminence standard and pollutionsituation of the landscape river, assessment criteria with four keys of five grades is projectedfor calculating the landscape water eminence, as shown in Table 2.Table – 2 Assessment criteria of landscape water pollution degree table type techniquesEvaluation IndexIndexDissolveOxygen(Mg / Ltr)NH3-N(Mg / Ltr)TN(Mg / Ltr)TP(Mg / Ltr)ClassificationIndexI 15 0.5 0.5 0.1II 20 1.0 1.0 0.2III 20 1.5 1.5 0.3IV 30 2.0 2.0 0.4V 40 2.5 2.5 0.5II. ESTABLISHING LANDSCAPE WATER EMINENCE ASSESMENT MODELWITH BACK PROPAGATION NETWORKCREATING THE METHODBack Propagation artificial neural network is one of the most important andcommonly used models. The most well-known design characteristic of Back Propagationartificial neural network is that the network weight is accustomed constantly through makingthe quadratic total of the error between the network model output weight and the consistencysample output to reach the preferred.Back Propagation artificial neural network is a feed forward neural network which hasan input layer, one or more hidden layers and one output layer. The connection betweenneurons in the adjacent layers is similar and non-connected. Apart from for input layer, there
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME627exists non-linear connection between the input layer and output layer in each neuron. Thefollowing S-function, as in equation 1 is taken, which is frequently used in Back Propagationneural network.)/)(exp(11)(01 θθ+−−=xxf(1)The middle subject of Back Propagation algorithm is adjusting the weight of thenetwork to the total error smallest. Algorithm of Back Propagation model learning methodconsists of positive propagation and back propagation. Through repeated positive propagationand back propagation, neurons weights and thresholds of layers are customized constantly todecrease the error function till the error function is no longer sinking. Heart of the learningprocess is to struggle for the least value of the error function, which guides sample setconstantly. Before accomplishment the smallest error, each training time, the right of thevalue of the error function changes along the steepest fall direction, finally converges to theleast point, and after that saves information of connection weights achieved from the trainingsamples and bias value, in order to deal with the unidentified samples. Determining thenetwork structure is formative the network layer number and neurons number of differentlayers. presently, there are still no standardized calculating formulas for number of hiddenlayers and neurons, while many national or international scholars make a number of empiricalformulas, such as “2N+1” proposed by Hecht-Nielsen, in which N represents node in theinput neurons.The computation steps of Back Propagation neural network can be explained as follows:(1) Initialize the network weights. Weight initialization mode is for the 1 / K, where K isassociated to the joint point of all the former layer node number.(2) Train data set. The signal is broadcasted through the network to calculate the startinglevels from the first lay of each node output until the output of output layer of each node areacquired.(3) Determine error value of each node of the output layer.(4) Determine error value of each node of the preceding layers (for S-function) which isachieved by error back-propagation through the layer by layer, until error value of each nodeof each layer is designed.(5) Adjust weight and threshold value.(6) Determine the error function value, until it arrives at the programmed least error value.(7) Save weights and thresholds resulting from training in order to process the input of thepredicted samples, thus the output of the forecasted samples can be considered.Model launching is the key to the achievement of the network training. Because theBack Propagation network itself has a few limitations, optimizations are made on the networkduring model founding process as follows. Firstly, the hidden layers and the hidden layerneuron nodes that four input layer nodes, namely four keys of water eminence assessment aredetermined; one hidden layer of nine initial nodes; one output layer of five nodes, that is tosay, there are five grades of the water eminence assessment. The network is exposed inFigure 1. Secondly, pretreatment on the sampling data, normalizing the training sample setand the desired target output with PREMNMX of MATLAB are necessary. And last, the
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME628structure of the network is completed. Function “Newff” is used to create Back Propagationnetwork; function “logsig” and “purelin” are neurons transfer functions of each layer; andfunction “trainlm” is the network training function.Fig. 1 Neural network structure diagram of landscape water eminence assessmentNETWORK PREPARATIONTaking the five water eminence grading standards in Table 2 as training samples, thefive nodes preferred outputs of the consequent output layer are shown in Table 3.Table 3 Expectation output of output layer nodeI II III IV V1 0 0 0 00 1 0 0 00 0 1 0 00 0 0 1 00 0 0 0 1Back Propagation network training process curve of trainlm (network training)function is exposed in Figure 2. Take the training coefficient rate η as 0.01, and trainconstantly on the computer until the error total of all training samples is no more than therequired value(ε ), here ε =0.001. After that stop learning and save the weights and thresholdsamong layers derived from training. Standard sample output is exposed in Table 4.
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, MarchFig. 2 Back Propagation network training process curve of training functionTable 4I II0.9858 0.00090.0008 1.00040.0002 0.00070.0009 0.00110.0005 0.0005ASSESSMENT RESULTS AND STUDYTaking the average of the examining COD, ammonia nitrogen, total nitrogen, andtotal phosphorus at the six sampling locations of the landscape river (as shown in Table I) asmodel input variables, assessment results are shown in TableTable 5 Water eminence evaluation network results of landscapeSamplingsitesI II1 0.0598 0.09112 0.0299 0.03793 0.0257 0.054174 0.0229 0.07125 0.1115 0.11096 0.1176 0.0167According to Table 5, all the top weights of network output between the six samplingsites are in Grade V water eminence, and the weight in Grade Ithan it in Grade V. Therefore the landscape river is measured polluted dangerously because itInternational Journal of Computer Engineering and Technology (IJCET), ISSN 09766375(Online) Volume 4, Issue 2, March – April (2013), © IAEME629Back Propagation network training process curve of training functionTable 4 Standard Sample OutputII III IV V0.0009 0.0035 0.0025 0.00271.0004 0.0003 0.0087 0.00060.0007 0.9811 0.0057 0.00080.0011 0.0040 0.9922 0.00280.0005 0..0011 0.0022 0.9828ASSESSMENT RESULTS AND STUDYTaking the average of the examining COD, ammonia nitrogen, total nitrogen, andtotal phosphorus at the six sampling locations of the landscape river (as shown in Table I) asmodel input variables, assessment results are shown in Table 5.eminence evaluation network results of landscapeII III IV V0.0911 0.0639 0.0576 1.50070.0379 0.1411 0.0841 1.45990.05417 0.1245 0.0745 1.37000.0712 0.0999 0.0628 1.36550.1109 0.1145 0.0511 1.15110.0167 0.0139 0.0433 1.0111, all the top weights of network output between the six samplingsites are in Grade V water eminence, and the weight in Grade I、II、III、IV is extreme lessthe landscape river is measured polluted dangerously because itInternational Journal of Computer Engineering and Technology (IJCET), ISSN 0976-April (2013), © IAEMEBack Propagation network training process curve of training functionTaking the average of the examining COD, ammonia nitrogen, total nitrogen, andtotal phosphorus at the six sampling locations of the landscape river (as shown in Table I) asControlLevelVVVVVV, all the top weights of network output between the six samplingIV is extreme lessthe landscape river is measured polluted dangerously because it
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME630doesn’t meet the requisite of the landscape water eminence as an entire. In universal case,landscape water eminence at least wants to reach Grade V standard. In addition, with thereduction of the highest weight of the five sampling sites from point 1 to 5, it shows thatwater eminence regularly improves along the stream path of river. The weights of point 1 and2 are very similar, which are mainly attributed to the comparable water eminence in the twopoints. Point 1 is chosen next to the rainfall pumping site which discharged flood seasonrainwater into the landscape water body, and there may be enterprise pollution in point 2. Itcan be accomplished that the water plants are helpful to the improvement of river watereminence throughout evaluating water eminence of point 3 and 4. The water eminence ofpoint 5 and 6 which are taken in the river downstream is superior to that of other points butnot yet meets the standards. On the whole, the landscape river studied is dangerously pollutedbecause of the high weight of Grade V among all the sampling sites. And the in general oflandscape water eminence does not reach the standard.In addition, gray fuzzy clustering assessment technique is also used to assess thislandscape water eminence in my research, and the conclusion is that the results of these twotechniques are similar.III. CONCLUSIONWater pollution problems have become a more and more prominent issue whichdangerously irritating the survival and expansion of civilization. Hence, the difficulty ofwater environment is no longer narrowed to a particular area or a period of time, but hasbecome global, cross-century focus. Now a day, urban landscape water management will alsobecome a research hotspot. In order to use and protect water bodies more successfully,creating comprehensive assessment on water eminence is required.In order to reflect the present condition of water eminence precisely, appreciate andclutch the force of water pollution and water environment trend, and ultimately protect andprovide a scientific basis for water resources planning and management, water eminenceassessment is made based on environmental monitoring information. Water eminenceassessment is a qualitative and quantitative assessment on water eminence and environmentwhich according to certain assessment criteria and techniques. Complete water eminenceassessment approaches counting single-factor assessment technique, multi-factor keyassessment technique, fuzzy mathematical assessment technique, gray system assessmenttechnique, analytic hierarchy process, matter-element analysis, artificial neural networkassessment technique as well as recently proposed such as water eminence classification keytechnique, gray fuzzy clustering assessment technique are the typical approaches at present.Artificial neural network is a highly non-linear mapping, which can study a mappingbetween a big numbers of models. Using artificial neural network for river water eminenceassessment, issues caused by other techniques, such as fuzzy comprehensive assessment andgray clustering techniques can be successfully avoided, such as man-made influence inweight assignment and purpose of membership function.The neural networks, throughout learning and training to control system internalvariation instead of using mathematical equations to state the application connection amongthe input and output, is well suited to compact with non-linear water environmental problems.In addition, the application of this technique is easy and appropriate for the dailymanagement of urban water eminence.
    • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME631REFERENCES[1] N.C. Kankal, M.M. Indurkar, S.K. Gudadhe and S.R. Wate, Water Quality Index ofSurface Water Bodies of Gujarat, India, Asian Journal of Experimental Science, Vol. 26,No. 1, 2012; 39-48[2] PARIKH, A. N. ANDMANKODI, P. C, WATER QUALITY ASSESSMENT OFHARNI POND OF VADODARA (GUJARAT), Electronic Journal of EnvironmentalSciences Vol. 4, 55-59 (2011)[3] X. Xie and Z. Lu, “Restoration and Maintenance Technology of Landscape Water inResidential Quarter,” Housing science, Vol. 15, A6, pp. 46-48, 2004[4] B. S. Sun, J. L. Shan and Q. Shao, “Environmental Analysis and Monitoring Theory andTechnology,” Beijing, China: Chemical Industry, 2007.[5] D. Shindell, “Estimating the potential for twenty-first century sudden climate change,philosophical Transactions of the Royal Society,” Mathematical Physical andEngineering Science, Vol. 13, A24, pp. 267-269, 2005.[6] L. Wang, W. L. Kang and J. Li, “Urban Landscape Water Bioremediation Technology,”Anhui Agricultural Science, Vol. 36, A6 pp. 1569-1572, 2008[7] Y. L. Sun, “Analysis of Methods of Water Environment Quality Assessment,” ShanxiChemical Industry, Vol. 27, A5, pp.65-67, 2007.[8] WeiXuan Hu, “The Application of Artificial Neural Network in Wastewater Treatment”,IEEE, 978-1-61284-486-2/11[9] Dr. P. Mariappan, “Wastewater Management in a Dwelling House- A Case Study”,International Journal of Civil Engineering & Technology (IJCIET), Volume 3, Issue 2,2012, pp. 16 - 24, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316.[10] R Radhakrishanan and A Praveen, “Sustainability Perceptions on Wastewater TreatmentOperations in Urban Areas of Developing World”, International Journal of CivilEngineering & Technology (IJCIET), Volume 3, Issue 1, 2012, pp. 45 - 61, ISSN Print:0976 – 6308, ISSN Online: 0976 – 6316.