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Paper No. OT-07

Hierarchical Fuzzy Rule based Model for GroundwaterVulnerability and Assessment of Nitrate Pollution Hazard in

Kathmandu BasinDhundi Raj Pathak1, Netra Prakash Bhandary2, Ryuichi Yatabe2

1Engineering Study & Research Centre, Kathmandu, Nepal2Graduate School of Science and Engineering, Ehime University, Matsuyama, Ehime, Japan

Corresponding Author, Email: esturesearch@gmail.com; draj28@yahoo.com

Key wordsGroundwatervulnerability, Nitratehazard, Fuzzy model,DRASTICparameters,Kathmandu

AbstractThis paper presents fuzzy inference system (FIS) as an alternative to theconventional overlay index method for the evaluation of groundwatervulnerability to nitrate pollution in watershed scale within GIS environment. Ahierarchical FIS model has been developed benefiting from expert knowledge-based DRASTIC system to produce groundwater vulnerability maps showingthe likelihood of groundwater pollution due to different hydrogeological factors.A case study relating to groundwater vulnerability assessment in shallow aquiferof Kathmandu basin, Nepal has been accomplished. Spatial distribution map ofnitrate has been developed to show the level of nitrate hazard in the study basin.Nearly 10% of the study area exceeded WHO guidelines of 10 mg/L nitrate-Nvalue and nearly 60% of shallow aquifer has impacted level of nitrate-N, i.e.between 2 and 10 mg/L. These results also indicated that the northern part of theValley and highly permeable alluvial deposits are dominated by very highvulnerability level is also under the threat of high nitrate-N pollution hazard. Asignificant correlation between vulnerability index and nitrate-N concentrationssuggests that the groundwater vulnerability map was consistent with observednitrate-N contamination.

1. IntroductionStrategies for protecting groundwater aquifer from contaminations like nitrate-N rather thandevelopment of new water resources and supply projects may prove to be in many cases theoptimal policy. Removal of nitrate from groundwater aquifer is often technically problematicand costly, and finding alternative sources for water supply is not always possible. So,groundwater aquifer vulnerability to nitrate has become a major concern of planners, decisionmakers, and water managers involved with managing the quality of water in relation to humanhealth. In recognition of the need for effective and efficient methods for protecting groundwaterresources from future contamination, scientists and resource managers have sought to developtechniques for predicting which areas are more likely than others to become contaminated as aresult of activities at or near the land surface (National Research Council, 1993). This concepthas been widely termed to groundwater vulnerability to contamination. To tackle thegroundwater pollution and to protect its quality in a more scientific and efficient way, the manyoverlay index methods are used. The most widely used among these techniques include GOD

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(Foster, 1987), IRISH (Daly and Drew, 1999), AVI (van Stemproot et al., 1993), DRASTIC(Aller et al., 1987), SINTACS (Verba and Zaporozec, 1994) and EPIK technique ((Doerfligerand Zwahlen, 1997). Among them, DRASTIC is widely applied either in its original form ormodified form in various countries (Lynch et al., 1997; Fritch et al., 2000; Ei-Naqa, 2004;Babiker et al., 2005; Rahman, 2008; Pathak et al., 2009; Yin et al., 2013; Duarte et al., 2014;Neshat et al., 2014a, b; Kumar et al., 2014 etc.). DRASTIC acronym stands for the sevenhydrogeological parameters; depth to water, recharge, aquifer media, soil type, topography(slope), impact on the vadose zone media and hydraulic conductivity of the aquifer. However,groundwater vulnerability mapping using overlay index methods are not easy task due toinherent uncertainty and limited input data. Therefore, a fuzzy model has been applied to solvethis problem incorporating the non linear mapping of intrinsic groundwater vulnerabilityconcept benefiting from fuzzy engine and expert knowledge-based DRASTIC parameters(Dixon, 2005; Nobre et al., 2007; Afshar et al., 2007; Pathak and Hirtasuka, 2011; Rezai et al.,2013). But, the exponential increase in rules will be problematic in most cases. In order toovercome the problem of rule explosion, the hierarchical rule techniques in the fuzzy systemdesign is very useful. So, this paper presents fuzzy inference system (FIS) as an alternative tothe conventional overlay index method for the evaluation of groundwater vulnerability tonitrate pollution in watershed scale within Geographic Information System (GIS) environment.A hierarchical FIS model has been developed benefiting from expert knowledge-basedDRASTIC system to produce groundwater vulnerability maps showing the likelihood ofgroundwater pollution due to different hydrogeological factors. A case study relating togroundwater vulnerability assessment in shallow aquifer of Kathmandu basin, Nepal has beenaccomplished. Nitrate-N risk map has been developed by combining groundwater vulnerabilityand nitrate hazard maps to identify areas that currently are at risk.

2. Material and methods2.1 Fuzzy logic approachThe basic concept in fuzzy logic is quite simple; statements are not only true or false butalso represents the degree of truth or degree of falseness for each input. Fuzzy sets are definedby their membership function, which are therefore the core of the entire concept. There aredifferent types of membership functions such as continuous piece-wise linear functions(triangular and trapezoidal shape) and continuous piecewise exponential membership functionssuch as Gauss functions. Fuzzy sets and fuzzy operators are the subjects and verbs of fuzzylogic. The IF-THEN rule statements are used to formulate the conditional statements thatcomprise fuzzy logic. The standard operations correspond to a logical IF-THEN base withAND, OR and NOT operators, for instance IF x is A, and y is B, THEN z is C, where A,B, and C are linguistic values defined by fuzzy sets on the ranges (universes of discourse) X, Yand Z respectively. The IF-part of the rule is called the antecedent or premise, while the THEN-part of the rule is called the consequent or conclusion. Fuzzy inference is the process offormulating the mapping from a given input to an output using fuzzy logic that employs therules upon which decisions are made. The process of fuzzy inference involves: membershipfunctions, logical operations, and IF-THEN rules. Generally, a fuzzy rule based model iscomprised of fuzzification of input variables, application of fuzzy operator in the antecedent

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(degree of fulfillment), implication from antecedent to the consequent (inference), aggregationof the consequents across the rules, and defuzzification.

A large number of input variables results the exponential increase in rules which willbe problematic in the use of single layer fuzzy inference system. In order to overcome theproblem of rule explosion, a multilayer fuzzy inference system (hierarchical rule techniques) inthe fuzzy system design is very useful and so was selected for design in this study. Amethodology outline of hierarchical fuzzy inference system for the evaluation of aquifergroundwater vulnerability index using DRASTIC parameters is shown in Figure 1a. For designof hierarchical fuzzy model, MATLAB(R) fuzzy toolbox was employed. The output of this FISmodel was exported to GIS to develop groundwater vulnerability map which was finallycombined with nitrate hazard map to develop nitrate risk map. The applied methodology isoutlined in Figure 1b.

(a)

(b)Figure 1 Methodology outline of (a) hierarchical fuzzy inference system for the evaluation of aquifer

groundwater vulnerability using DRASTIC parameters, (b) preparation of Nitrate-N risk map

Nitrate-N data inGW wells

Nitrate-N hazardmap

FIS output (GWvulnerabilityindex)

GW vulnerabilitymap

Groundwaternitrate-Nrisk map

Depth towater table

(D)Recharge

(R)

FIS1

Aquifermedia (A)

FIS2

Soil media(S)

FIS3

Topography(T)

FIS4

Impact onvadose zone

(I)

Hydraulicconductivity

(C)

FIS5

FIS6(Vulnerabilityindex)

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The membership functions can be constructed from several basic functions such aspiecewise linear functions, the Gaussian distribution function, the sigmoid curve, quadratic andcubic polynomial curves. The triangular and trapezoidal membership functions are the simplestand have been used in this study due to their modeling flexibility. Each input domain wasdivided into three sub-domains (i.e., Low, Medium, and High) and output domaindivided into five sub-domains (i.e., Very low, Low, Medium, High and Very high).In general, fuzzy rule-based systems benefit from rule bases which mainly are organized usingexperts knowledge. This study employs expert knowledge from the general knowledge of theexperts who developed the DRASTIC system, however one may benefit from knowledge oflocal experts and knowledge obtained from computer simulation to improve the rules andsystem performances. For example, the rules are of the form; If D (depth to groundwater) is L(low) AND R (recharge) is H (high) THEN FIS1 is VH (very high) .

All seven input data layers used in DRASTIC system were generated and/or obtainedfrom its original source as a point, line, or polygon layer. Then, all seven parameterscontributing to groundwater vulnerability were converted from vector (point, line, or polygon)to raster (grid) of 30 m x 30 m grid resolution using the GIS as shown as Figure 2.

Figure 2, Seven input raster layers to compute vulnerability index

All the raster map layers were then converted to ASCII format to feed as inputparameter to FIS model. The Figure 1a clearly reveals that the first two input parameters (depthto water table and Net recharge) are aggregated in first model (FIS1) and the output from firstmodel is aggregated with third input variable (Aquifer type) in the next stage model (FIS2). Inthe subsequent manner, FIS5 is aggregated with that of the last input variable hydraulicconductivity) to obtain the groundwater vulnerability to pollution index from FIS6. Fuzzyinference used here is a minimum Mamdani inference and a central defuzzification method.

3. Results and discussionTo demonstrate the performance of proposed fuzzy models, a case study relating togroundwater vulnerability assessment in shallow aquifer of Kathmandu basin, Nepal has beenaccomplished. The output of hierarchical fuzzy inference model was exported to GIS to

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develop groundwater vulnerability map of shallow aquifer in Kathmandu basin, Nepal asshown as Figure 3a.

(a) (b)Figure 3, (a) Groundwater vulnerability map and (b) Nitrate-N hazard map in shallow aquifer ofKathmandu basin, Nepal

Figure 3a shows the relative degree of groundwater vulnerability to contamination. Ahigh index indicates the capacity of the hydrogeologic environment and the landscape factors toreadily move waterborne contaminants into the groundwater and consequently need to bemanaged more closely. Low index represents groundwater that is better protected fromcontaminant leaching by natural environment. Nitrate-N data sampled from more than 100groundwater sources of shallow aquifer in Kathmandu basin was used to develop nitrate-Nhazard map. Inverse distance weighting (IDW) interpolation technique in GIS environment waschosen to create nitrate-N hazard map of study area. The Figure 3b shows the nitrate-N hazardmap of shallow aquifer of Kathmandu. The results show that the northern part of theKathmandu basin and highly permeable alluvial deposits are dominated by very highvulnerability level is also under the threat of high nitrate-N pollution hazard. Nearly 10% of thestudy area exceeded WHO guidelines of 10 mg/L nitrate-N value and nearly 60% of shallowaquifer have impacted level of nitrate-N, i.e. between 2 and 10 mg/L (Figure 5a).

Because of no universal and clear-cut definition of vulnerability, measurable data todirectly quantify the vulnerability may not be available. However, the spatial distribution ofcontaminant sources like nitrate may be utilised to validate the vulnerability result. Figure 4clearly shows a significant positive correlation between nitrate concentration and thegroundwater vulnerability levels with a coefficient value of 0.52. These results suggest that thegroundwater vulnerability map of shallow groundwater aquifer in Kathmandu basin wasgenerally consistent with observed nitrate contamination near the water table. This result alsoconfirms the validation and reliability of hierarchical FIS model, which reflect an aquifersinherent capacity to become contaminated.

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Figure 4, Relationship between nitrate concentration in wells and groundwater vulnerability inKathmandu basin

(a) (b)Figure 5, (a) Nitrate-N hazard level, (b) Nitrate-N risk map of shallow aquifer of Kathmandubasin

4. ConclusionsThe DRASTIC parameters were introduced to a hierarchical fuzzy inference system in order todevelop groundwater vulnerability map which helps to rank the highly vulnerable area or lowvulnerable area in the shallow groundwater aquifer of Kathmandu basin. The proposedhierarchical fuzzy inference model in this study has become very useful in order to overcomethe problem of exponential increase in rules due to a large number of input variables results. Inaddition to the development of groundwater vulnerability map, spatial distribution map of

R = 0 .52

0 .50 .550 .6

0 .650 .7

0 .750 .8

0 .850 .9

0 .951

0 5 10 15 20 25 30

Vul n

e rab

i l it y

I nd e

x

N itrate -N (m g/L )

99.31

171.54

21.70

0.0020.0040.0060.0080.00100.00120.00140.00160.00180.00200.00

No hazarad zone Hazard zone Very hazard zone

Area (

sq. km

. )

Nitrate-N

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nitrate has been developed to show the level of nitrate hazard in the study basin. Thecombination of groundwater vulnerability and nitrate hazard maps can be used to identify areasthat currently are at risk and help identify areas where groundwater has been affected by humanactivities. A significant positive correlation between nitrate concentration and the groundwatervulnerability levels suggests that the groundwater vulnerability map of shallow groundwateraquifer in Kathmandu basin was generally consistent with observed nitrate contamination nearthe water table.

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