Adaptive Neuro Fuzzy Inference System (ANFIS) for Prediction of Groundwater Quality Index in Matar Taluka and Nadiad Taluka

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1 Internatonal Journal of Scence and Research (IJSR) ISSN (nlne): Index Coperncus Value (2013): 6.14 Impact Factor (2013): Adaptve Neuro Fuzzy Inference System (ANFIS) for Predcton of Groundwater Qualty Index n Taluka and Nadad Taluka Nkunj Ashyan 1, Dr. T. M. V. Suryanarayana 2 1 P.G. Student, Water Resources Engneerng and Management Insttute, Faculty of Technology & Engneerng, The M. S. Unversty of Baroda, Samala 2 Assocate Professor, Water Resources Engneerng and Management Insttute, Faculty of Technology & Engneerng, The M. S. Unversty of Baroda, Samala Abstract: Adaptve Neuro Fuzzy Inference System (ANFIS) approach s employed n the present study to observe ts applcablty on Predcton and Forecastng of Groundwater Qualty Index Predcton n Taluka and Nadad Taluka. As per classfcaton based on Groundwater Qualty Index, Groundwater Qualty of Taluka les between good water to very poor water, whle Groundwater Qualty of Nadad Taluka les between Good Water to Poor Water. ANFIS system s one of the developng powerful tools to predct such heavy constraned problem wth tme seres analyss by hybrd technque. Frst Part of the study s to fnd the GWQI of the Taluka and Nadad Taluka. Second part of the study s to study s to dentfy the Best ANFIS model whch satsfyng two Statstcal measures (RMSE, and R 2 ) durng tranng and valdaton processes for the duraton of 1997 to 2006 of Taluka and Nadad Taluka. Keywords: Groundwater Qualty Index, Taluka, Nadad Taluka, ANFIS. 1. Introducton Water resources have been the most exploted natural system, snce man strode the earth. As a result of ncreasng, cvlzaton, urbanzaton, ndustralzaton and other developmental actvtes, our natural water system s beng polluted by dfferent sources. The pollutants comng as a waste to the water bodes are lkely to create nusance by way of physcal appearance, odour, taste, qualty and render the water harmful for utlty. Ths has resulted n the decrease n the qualty of drnkng water avalable. The ground water qualty s normally characterzed by dfferent physc chemcal characterstcs. These parameters change wdely due to the type of polluton, seasonal fluctuaton, ground water extracton, etc. Montorng of water qualty levels s thus mportant to assess the levels of polluton and also to assess the potental rsk to the envronment. Groundwater qualty ndex s one of the most effectve tools to communcate nformaton on the qualty of water to the concerned ctzens and polcy makers. It, thus, becomes an mportant parameter for the assessment and management of groundwater [2]. The ANFIS s a neuro-fuzzy system, whch uses a feedforward network to search for fuzzy decson rules that perform well on a gven task. Usng a gven nput/output data set, ANFIS creates a fuzzy nference system whose membershp functon parameters are adjusted usng a backpropagaton algorthm alone or combnaton of a back propagaton algorthm wth a least mean squares (LMS) method (hybrd learnng). Ths allows the fuzzy systems to learn from the data beng modeled. ANFIS provdes a method for the fuzzy modelng procedure to learn nformaton from the data set, followed by creaton of the membershp functon parameters that best performs the gven task. The ANFIS can smulate and analyze the mappng relaton between the nput and output data through a learnng algorthm to optmze the parameters of a gven Fuzzy Inference System (FIS) [1]. 2. Study Area Gujarat state s located n the western part of Inda. In ths study, Taluka and Nadad Taluka of Kheda dstrct area s selected. The Kheda dstrct s located (between 72 o 32 to 73 o 37 East longtude and between 22 o 30 to 23 o 18 North lattude) n Gujarat. The Study was conducted n a premonsoon and post-monsoon season of 10 years from 1997 to Total 768 samples from dfferent localtes of Taluka and 1002 samples from dfferent localtes of Nadad Taluka were collected. 3. Methodology 3.1 Groundwater Qualty Index Groundwater qualty ndex s one of the most effectve tools to montor the surface as well as ground water polluton and can be used effcently n the mplementaton of water qualty upgradng programs. It s one of the aggregate ndces that have been accepted as a ratng that reflects the composte nfluence on the overall qualty of numbers of precse water qualty characterstcs [2]. For computng GWQI three steps are followed. In the frst step, each of the all parameters has been assgned a weght (w) accordng to ts relatve mportance n the overall qualty Paper ID: SUB Lcensed Under Creatve Commons Attrbuton CC BY

2 Internatonal Journal of Scence and Research (IJSR) ISSN (nlne): Index Coperncus Value (2013): 6.14 Impact Factor (2013): of water for drnkng purposes (Table-1). The maxmum weght of 4 has been assgned to the parameter TDS, ph and Sulphate due to ts major mportance n water qualty assessment. SAR and Magnesum whch s gven the mnmum weght of 2 as magnesum by tself may not be harmful. In the second step, the relatve weght (W) s computed from the followng equaton: w W (1) n w 1 W s the relatve weght, w s the weght of each parameter and n s the number of parameters. Calculated relatve weght (W) values of each parameter are also gven n Table-1. Table 1: Relatve Weght (W) values of each parameters Parameters Indan Weght Relatve weght Standards (w) (W) ph Chlorde Sulphate Ca Mg SAR TDS Total 21 1 In the thrd step, a qualty ratng scale (q) for each parameter s assgned by dvdng ts concentraton n each water sample by ts respectve standard accordng to the gudelnes lad down n the BIS and the result multpled by 100. Table 2: Groundwater Qualty classfcaton based on GWQI Value GWQI Value Water Qualty <50 Excellent Water Good Water Poor Water Very Poor Water >300 Unsutable for Drnkng 3.2 Adaptve Neuro Fuzzy Inference System (ANFIS) ANFIS s ntegraton of neural networks and fuzzy logc and have the potental to capture the benefts of both these felds n a sng le framework. ANFIS utlzes lngustc nformaton from the fuzzy logc as well learnng capablty of an ANN for automatc fuzzy f-then rule generaton and parameter optmzaton. A conceptual ANFIS conssts of fve components: nputs and output database, a Fuzzy system generator, a Fuzzy Inference System (FIS), and an Adaptve Neural Network. Adaptve Neuro Fuzzy Inference System (ANFIS) s a fuzzy mappng algorthm that s based on Tagak-Sugeno-Kang (TSK) fuzzy nference system. The Sugeno- type Fuzzy Inference System, whch s the combnaton of a FIS and an Adaptve Neural Network, was used n ths study for GWQI Predcton modellng. The optmzaton method used s hybrd learnng algorthms [3], [4]. For a frst-order Sugeno model, a common rule set wth two fuzzy f-then rules s as follows: q C ( )*100 S (2) q s the qualty ratng, C s the concentraton of each chemcal parameter n each water sample n mg/l, S s the Indan drnkng water standard for each chemcal parameter n mg/l accordng to the gudelnes of the BIS. For computng the GWQI, the SI s frst determned for each chemcal parameter, whch s then used to determne the GWQI as per the followng equaton, SI W * q (3) n 1 GWQI SI (4) SI s the sub ndex of th parameter, q s the ratng based on concentraton of th parameter, n s the number of parameters. The computed WQI values are classfed nto fve types, excellent water to water, unsutable for drnkng. Rule 1: If x 1 s A 1 and x 2 s B 1, then f 1 = a 1 x 1 + b 1 x 2 + c 1 Rule 2: If x 1 s A 2 and x 2 s B 2, then f 2 = a 2 x 1 + b 2 x 2 + c 2 x 1 and x 2 are the crsp nputs to the node and A 1, B 1, A 2, B 2 are fuzzy sets, a, b and c ( = 1, 2) are the coeffcents of the frst-order polynomal lnear functons. It s possble to assgn a dfferent weght to each rule based on the structure of the system, where, weghts w1 and w2 are assgned to rules 1 and 2 respectvely. And f = weghted average The ANFIS conssts of fve layers, shown n Fgure 1. The fve layers of model are as follows: Layer1: Each node output n ths layer s fuzzfed by membershp grade of a fuzzy set correspondng to each nput. or (x ) = 1.2 (5) 1, A 1 1, B 2 x2 = 3,4 (6) ( ) x 1 and x 2 are the nputs to node ( = 1, 2 for x 1 and = 3, 4 for x 2 ) and x 1 (or x 2 ) s the nput to the th node and A (or B - 2) s a fuzzy label. Paper ID: SUB Lcensed Under Creatve Commons Attrbuton CC BY

3 Internatonal Journal of Scence and Research (IJSR) ISSN (nlne): Index Coperncus Value (2013): 6.14 Impact Factor (2013): Fgure 1: ANFIS Archtecture Layer 2: Each node output n ths layer represents the frng strength of a rule, whch performs fuzzy, AND operaton. Each node n ths layer, labeled P, s as Table node whch multples ncomng sgnals and sends the product out. w ( x) (y), = 1,2 (7) 2, A B Layer 3: Each node output n ths layer s the normalzed value of layer 2,.e., the normalzed frng strengths. 3, w w w w 1 2, =1,2 (8) Layer 4: Each node output n ths layer s the normalzed value of each fuzzy rule. The nodes n ths layer are adaptve.here ῷ s the output of layer 3, and {a, b, c} are the parameter set. Parameters of ths layer are referred to as consequence or output parameters. 4,1 w f w ( px q y r ) (9) Layer 5: The node output n ths layer s the overall output of the system, whch s the summaton of all comng sgnals. 5,1 w f wf w (10) In ths way the nput vector was fed through the network layer by layer. The two major phases for mplementng the ANFIS for applcatons are the structure dentfcaton phase and the parameter dentfcaton phase. The structure dentfcaton phase nvolves fndng a sutable number of fuzzy rules and fuzzy sets and a proper partton feature space. The parameter dentfcaton phase nvolves the adjustment of the premse and consequence parameters of the system Development of GWQI Predcton Model n ANFIS In ANFIS, the datasets dvded nto tranng and valdaton data, and loaded n ANFIS edtor as tranng and testng data keepng 7 groundwater Qualty parameters ( ph, calcum, magnesum, chlorde, sulphate, total dssolved solds, and Sodum Absorpton Rato )as nput and Groundwater Qualty Index as output of model. The ANFIS edtor GUI, MATLAB s shown n fgure 2 wth loaded data. Fgure 2: ANFIS Edtor GUI In above fgure 2, the o represents the tranng data and represents the testng data. The FIS generated for Subtractve Clusterng functon whch produces accurate outputs values by usng a large number of membershp functon. The FIS s then tran for 10 epoch and 0 error tolerance n order to generate output as Groundwater Qualty Index Value. 4. Results and Analyss 4.1 Groundwater Qualty Index After Analyss and Calculaton of GWQI the followng result s conducted for Taluka and Nadad Taluka. Table 3: Water Qualty Classfcaton based on GWQI Value Taluka Percentage of Water Samples GWQI Pre Post Value Water Qualty Monsoon Monsoon verall <50 Excellent Water Good Water Poor Water Very Poor Water Unsutable for >300 Drnkng As shown n Table 3, the GWQI for 778 Ground water samples ranges from to almost percent of the samples exceeded 100, the upper lmt for drnkng water. About percent of water samples are poor n qualty and percent of water samples are of very poor qualty and should not use drectly for drnkng purpose. As per the classfcaton based on groundwater qualty ndex percent ground water samples shows good qualty of water and 9.90 percent sample shows excellent qualty of ground water. Paper ID: SUB Lcensed Under Creatve Commons Attrbuton CC BY

4 Internatonal Journal of Scence and Research (IJSR) ISSN (nlne): Index Coperncus Value (2013): 6.14 Impact Factor (2013): Table 4: Water Qualty Classfcaton based on GWQI Value Nadad Taluka Percentage of Water Samples GWQI Value Water Qualty Pre Monsoon Post Monsoon verall <50 Excellent Water Good Water Poor Water Very Poor Water Unsutable for >300 Drnkng As shown n Table 4, the GWQI for 1002 Ground water samples ranges from to , almost percent of the samples exceeded 100, the upper lmt for drnkng water. About percent of water samples are poor n qualty and 4.99 percent of water samples are of very poor qualty and should not use drectly for drnkng purpose. As per the classfcaton based on groundwater qualty ndex percent ground water samples shows good qualty of water and percent sample shows excellent qualty of ground water. 4.2 Adaptve Neuro Fuzzy Inference System (ANFIS) Three models are developed to predct Groundwater Qualty of Taluka & Nadad Taluka usng ANFIS. ANFIS Model-1: 60% - 40% ANFIS Model-2: 70% - 30% ANFIS Model-3: 80% - 20% From the above Table 5 hghlghted feld shows that the ANFIS Model-2 havng RMSE and r value of and 1 respectvely durng tranng and RMSE and r value of and 1 durng Valdaton. Hence ANFIS Model-2 s the best model for Taluka for Groundwater Qualty Index Predcton. Whle ANFIS models havng RMSE and r value of and 1 respectvely durng tranng and RMSE and r value of and durng Valdaton, Hence ANFIS Model-3 provdes best result for Nadad Taluka for Groundwater Qualty Index Predcton. Charts Gven below shows correlaton between Predcted GWQI vs. bserved GWQI of ANFIS Model-2 durng tranng and valdaton for Taluka and ANFIS Model-2 durng tranng and valdaton for Nadad Taluka. It s observed that durng tranng and valdaton, Predcted GWQI and bserved GWQI values are hghly correlated n Taluka and Nadad Taluka. After developng the best Groundwater qualty ndex predcton Model usng ANFIS for each staton wth the tranng & valdaton datasets wth 3 combnaton, comparson s carred out to conclude the best ANFIS model for Taluka & Nadad Taluka among all models. Table 5: Best ANFIS Model Developed for Taluka & Nadad Taluka Taluka Phase ANFIS Model-1 Tranng Valdaton Nadad Tranng Valdaton Fgure 3: Correlaton between Predcted GWQI vs. bserved GWQI of ANFIS Model-2 for Taluka durng Tranng Taluka Nadad Phase ANFIS Model-2 Tranng Valdaton Tranng Valdaton Taluka Nadad Phase ANFIS Model-3 Tranng Valdaton Tranng Valdaton Fgure 4: Correlaton between Predcted GWQI vs. bserved GWQI of ANFIS Model-2 for Taluka durng Valdaton Paper ID: SUB Lcensed Under Creatve Commons Attrbuton CC BY

5 Internatonal Journal of Scence and Research (IJSR) ISSN (nlne): Index Coperncus Value (2013): 6.14 Impact Factor (2013): Ths study s done for the and Nadad Taluka and can also do ths study for the ther Taluka or study areas and the result can be compared. References Fgure 5: Correlaton between Predcted GWQI vs. bserved GWQI of ANFIS Model-3 for Nadad Taluka durng Tranng [1] Folorunsho J.., Igus E.., Mu azu M.B. and Garba S. Applcaton of Adaptve Neuro Fuzzy Inference System (Anfs) n Rver Kaduna Dscharge Forecastng, Research Journal of Appled Scences, Engneerng and Technology 4(21): , [2] Mangukya Rupal, Bhattacharya Tanushree and Chakraborty Sukalyan. Qualty Characterzaton of Groundwater usng Water Qualty Index n Surat cty, Gujarat, Inda, Internatonal Research Journal of Envronment Scences, Vol. 1(4), 14-23, November (2012). [3] G. R. Umamaheswar, Dr. D. Kalaman. Adaptve neuro fuzzy nference system for monthly groundwater level predcton n amaravath rver mnor basn, journal of theoretcal and appled nformaton technology, vol. 68 no.3, Author Profle Nkunj Ashyan receved the B.E. degree n cvl engneerng from C.K. Pthawala College of Engneerng Technology, Surat and M.E. degree n Cvl Engneerng (Irrgaton Water management) from Water Resources Engneerng and Management Insttute, Faculty of Technology & Engneerng, The M. S. Unversty of Baroda, Samala n 2011 and 2015, respectvely. Fgure 6: Correlaton between Predcted GWQI vs. bserved GWQI of ANFIS Model-3 for Nadad Taluka durng Valdaton 5. Conclusons and Recommendatons 5.1 Conclusons For Taluka and Nadad Taluka Groundwater Qualty s mproved n pre-monsoon season compared to post-monsoon season and The ANFIS Model-2 havng RMSE and r value of and 1 respectvely durng tranng and RMSE and r value of and 1 durng Valdaton. Hence ANFIS Model-2 s the best model for Taluka for Groundwater Qualty Index Predcton. Whle ANFIS models havng RMSE and r value of and 1 respectvely durng tranng and RMSE and r value of and durng Valdaton, Hence ANFIS Model-3 provdes best result for Nadad Taluka for Groundwater Qualty Index Predcton. Dr. T.M.V. Suryanarayana s born n Vsakhapatnam on 11th February, 1979 and completed B.E.(Cvl-IWM) n May 2001, M.E.(Cvl) n Water Resources Engneerng n November 2002 and Ph.D. n Cvl Engneerng n May 2007 from The M.S. Unversty of Baroda, Vadodara, Gujarat, Inda. He s servng as Assocate Professor and recognzed Ph.D. Gude n Water Resources Engneerng and Management Insttute, Faculty of Technology and Engneerng, The M. S. Unversty of Baroda. He has 12 years of teachng and Research Experence. Hs areas of research nclude peratons Research, Hydrologc Modelng, Conjunctve Use, Hydraulcs of Sedment Transport, Sol and Water Conservaton, Reservor peraton, Soft Computng Technques, Clmate Change. He has been nvted for delverng Expert Lectures at Varous Natonal Level Insttutes and also as a speaker on programmes related to Awareness on Water Management. He has obtaned Two Best Paper Awards at dfferent Natonal level conferences and Two Best Poster Awards at dfferent Natonal Level Events. He has under hs credt around Sx Dozen Research Papers publshed n varous Internatonal/Natonal Journals/ Semnars/ Conferences/ Symposums. 5.2 Recommendatons The ANFIS Tool can be used for other groundwater qualty parameters predcton and results can be compared. Paper ID: SUB Lcensed Under Creatve Commons Attrbuton CC BY