Grid-Based Assessment of Groundwater Potential Using GIS

Zhang Zhijun

, He Qinjie

, Ma Guorui

and Ma Shihao

Xining Comprehensive Survey Center for Natural Resources, China Geological Survey, Xining, Qinghai,810000, China

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan,

Hubei, 430079, China

School of Science, Hubei University of Technology, Wuhan 430068, China

Keywords: GIS, Groundwater, Grid-based Analysis, Entropy Weight Method, Water Supply.

Abstract: In view of the problem that the weight determination of evaluation indexes in the current groundwater

potential assessment was too much affected by human, analyzed the various factors related to the groundwater,

constructed a grid-based evaluation index system for groundwater potential assessment, and an assessment

model of groundwater potential using GIS based on the evaluation index system, grid-based analysis, entropy

weight method combined with artificial experience, Gamma transformation, and natural breakpoint

classification method was proposed. The grid-based assessment of groundwater potential in "One Belt And

One Road" region was completed, and the favorable areas for water supply were delineated. The results

showed that the low, middle and high potential areas of groundwater in the study area account for about

36.00%, 62.30% and 1.70%, and the high potential area was strongly correlated with the location of spring

points and water-bearing faults. This evaluation method combined objective weights and subjective

experience to reduce the dependence of artificial experience.

1 INTRODUCTION

Water is a precious resource for human survival and

an important strategic material for national

development. Groundwater is an important part of

water resources, widely distributed, relatively stable

changes, good water quality, groundwater enrichment

assessment, can provide a reference for the rational

use of water resources, while meeting the demand for

water, to avoid further deterioration of the ecological

environment, to achieve sustainable economic and

social development. (Chen Fei et al., 2020; Yifei Bai

et al., 2019; Bin Xu et al., 2018)

The traditional hydrogeological survey method is

mainly based on field survey, which is inefficient and

difficult to meet the needs of large-scale rapid water

finding. Geographic information system technology

(GIS) can quickly integrate and analyze large

amounts of data, greatly saving manpower and

material resources, and improving the efficiency of

hydrological work (Cao jianfeng et al., 2006; Saro,

Lee et al., 2012; Hema et al., 2017; Demeke et al.,

2019). In the assessment of groundwater enrichment,

the determination of the weights of each influencing

factor is a very critical issue, and the commonly used

methods are roughly divided into subjective

empowerment method (such as expert scoring

method, hierarchical analysis method) and objective

empowerment method (such as principal component

analysis method, entropy weight method, similarity

coefficient method, coefficient of variation method).

(Dong Yanhui et al., 2017; Dou Bingchen et al.,

2015; Guo Xiaoci et al., 2006) Hema and Subramani

(Hema et al., 2017) used topographic maps and

LANDSAT TM images to take into account

geomorphology, linear density, soil, land use/land

cover, river network density, slope and other factors,

and used weighted index overlay analysis to draw a

groundwater potential zoning map of the study area.

Pinto(Pinto et al., 2017)determines the groundwater

potential area of comoros basin by using the analytic

hierarchy process (AHP) based on river network

density, land use, linear density, topography, rainfall,

slope, soil, lithology and other factors. Lee (Lee et al.,

2012) selected 15 factors related to groundwater and

collected data from 44 well locations. Using artificial

neural network, he constructed a groundwater

production potential model for the surrounding area

of Pohang city, South Korea. Pradhan (Pradhan et al.,

2021) investigated 145 spring sites to characterize

Zhijun, Z., Qinjie, H., Guorui, M. and Shihao, M.

Grid-Based Assessment of Groundwater Potential Using GIS.

DOI: 10.5220/0012036200003536

In Proceedings of the 3rd International Symposium on Water, Ecology and Environment (ISWEE 2022), pages 319-325

ISBN: 978-989-758-639-2; ISSN: 2975-9439

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

319

groundwater potential, and selected 10 factors to

build a deep neural network to explore the

groundwater potential area in the Himalayas of

Nepal. The subjective empowerment method requires

researchers to have rich professional knowledge and

practical experience, which is greatly influenced by

human subjectivity, and the hydrogeological

conditions vary greatly in different research areas,

and it is impossible to use the same weight model to

evaluate the potential of groundwater richness in

different regions, while the objective empowerment

method is more dependent on the sample, there is no

business experience as a guide, and the weight is

easily distorted.

Aiming at the above problems, based on GIS

technology, this paper will construct a groundwater

enrichment assessment index system, establish a

groundwater enrichment personality network

assessment model that integrates grid data analysis,

entropy weight method combined with artificial

experience, Gamma transformation and natural

breakpoint classification method, evaluates the

groundwater enrichment in the research area, and

compares the assessment results with the results

obtained by the expert scoring method and the results

without reference to artificial experience. The

research flow is shown in Figure 1.

Figure 1: The research flow.

2 OVERVIEW AND DATA

SOURCES OF THE STUDY

AREA

The scope of the study area is (33°N ~ 36°N, 73.5°E

~ 78°E), and the standard division is carried out

according to the 1:250,000 scale in the National

Standard for division and Numbering of basic Scale

Topographic Maps (GB/T 13989-2012).

2.1 Overview of the Study Area

The research area includes I43C001002,

I43C001003, I43C001004, I43C002003,

I43C002004 and I43C003004, as shown in Figure 2.

The study area spans three countries, China, Pakistan

and India, and is an important area for the

implementation of "One Belt and One Road" strategy.

It is mainly plateau and mountain, most of which are

above 4000 meters above sea level, with numerous

rivers and perennial snow in the mountains, which are

an important source of groundwater supply in the

area. (Ding Jianli et al., 2018)

Figure 2: Geographical location of the study area.

2.2 The Data Source

The data used in this paper mainly include the

following three types :(1) hydrogeological vector

files, obtained by visual interpretation of remote

sensing images by professionals and provided by

xining natural resources comprehensive survey center

of China geological survey, including lithology data,

water-bearing fracture data, water- bearing fold data,

spring point data and overflow zone data; (2) OSM

(OpenStreetMap) data, using the river system vector

data and road vector data;(3) DEM (Digital Elevation

ISWEE 2022 - International Symposium on Water, Ecology and Environment

320

Model) data reflect the topographic information of the

study area.

2.3 Evaluation Index Analysis of

Groundwater Enrichment

Based on the field investigation results and

hydrogeological knowledge, the characteristics of

each index and its influence on groundwater are

analyzed.

 Lithology

Rock-soil voids are the storage space and

transport channel of groundwater. The voids

developed by different lithology are different, and the

capacity of water storage and transport is also

different. There are 19 different lithologies in the

study area. According to the field investigation

results, the lithology indexes are divided into positive

lithology indexes and negative lithology indexes.

There are pores in the quaternary unconsolidated

layer, which affect the retention, transport and

discharge of groundwater. (Zhang Renquan et al.,

1980)

 Water-bearing fracture, Water-bearing fold

The fault zone is usually rich in water, and the

water-conducting fracture is of great significance to

the storage and transport of groundwater. Along the

axis of fold, structural fissures are developed and

karst is strong, which is beneficial to groundwater

collection. High-yield Wells and springs are often

related to large linear bodies, intersection points of

linear bodies and corresponding structural

characteristics, and the occurrence conditions of

groundwater in dense linear structures are better.

(Quiel, F. et al., 2006)

 Spring, Overflow zone

Springs and overflow zones are the natural ways

of groundwater outpouring, which are of great

significance to the determination of water-rich

(water-carrying capacity) of strata and aquifer or

water-repellent layer. In the study area, spring points

and spring groups in the form of groundwater

discharge are relatively developed and widely

distributed. The overflow zone is drained in a linear

manner. The overall distribution of the overflow zone

in the study area is sparse, mainly located in the

middle and northeast of the study area. (Yan Yunpeng

et al., 2016)

 Surface water

In the study area, precipitation often occurs in the

form of snow and ice in the mountains, and meltwater

of snow and ice becomes the main recharge method

of surface rivers (Li Penghui et al., 2020), and

eventually becomes a large amount of infiltration and

transformation into groundwater. Surface water

becomes an important or even the only recharge

source of groundwater, and its distribution has a great

impact on the occurrence, recharge and discharge of

groundwater (Wang X F. et al., 2011).

3 NETWORK ASSESSMENT

MODEL OF GROUNDWATER

ENRICHMENT CHARACTER

BASED ON GIS TECHNOLOGY

Geographic grid is a grid formed by dividing the

earth's surface according to certain mathematical

rules. (Ma Ting et al., 2009) With the development of

geographic information technology, spatial analysis

based on grid management technology is becoming

an important technical means in the field of resource

management, playing a crucial role in the optimal

allocation of natural resources. (Li F Z. et al., 2019)

3.1 Establishment of Grid and

Groundwater Enrichment

Evaluation Index System

The study area was divided into regular grids.

Considering the large research scope and sparse

distribution of each indicator factor, the size of the

selected grids was 3km×3km, and a total of 10212

grids were finally obtained.

Based on the divided grid, grid the source data of

the research area:

(1) For point data, the number of points falling

into each grid is counted as the value of each grid,

such as spring point;

(2) For linear data, the ratio of the length of the

line segment in each grid to the area of the grid was

calculated as the value of the grid, such as water-

bearing faults, water-bearing folds, overflow zones

and water systems;

(3) For planar data, the ratio of the area of

evaluation indexes in each grid to the area of the grid

is the value of the grid, such as quaternary loose bed,

lithologic positive index and lithologic negative

index. The finally established groundwater

enrichment evaluation index system in the study area

is shown in Figure 3.

Grid-Based Assessment of Groundwater Potential Using GIS

321

Figure 3: Index system of groundwater enrichment

evaluation in the study area.

3.2 Entropy Weight Method Combined

with Artificial Experience

With n evaluation objects and M evaluation

indicators, the main steps of entropy weight method

are as follows :(Lian, S. et al., 2016)

 Standardized

Assuming that the p’th index value of the q’th

object is

, and then standardized to

represents the vector of the q’th index, then:

()

() ()

min

max min

pq q

−

(Positive indicators) (1)

()

() ()

max

max min

qpq

−

(Negative indicators) (2)

 Calculate the proportion of item q’th in the p’th

grid:

()

1, 2,...,

pqm



(3)

 Calculate the information entropy of each

index. According to the definition of

information entropy, the information entropy

of item q’th is:

()

()( )

ln , 1/ ln 1, 2,...,

qpqpq

cp pc nq m

=− × = =



(4)

 Calculate the entropy weight. The entropy

weight of item q’th is:

()

−



(5)

For this study, there are 10212 grids and 10

indicators, namely n=10212, m=10. The evaluation

index system of groundwater enrichment in the study

area is a two-level index system. In order to avoid

weight distortion, the weight of the second-level

index to the first-level index will be determined by

entropy weight method, and the weight of the first-

level index to the target layer will be guided by

artificial experience. The weight of the final

secondary index is:

qqRq

www=

(6)

Where,

is the weight of the second-level index

to the first-level index, and

is the weight of the

first-level index to the target layer. The final

evaluation value of groundwater enrichment of each

grid is:

qqpq



(7)

3.3 Gamma Transform

As the overall brightness of the image is too dark and

pixel values are mainly concentrated in the dark pixel

region, image enhancement is required. In the

experiment, a common image enhancement method,

Gamma transform, was selected to make the output

gray value and the input gray value show an

exponential relationship:

out in

VAV

(8)

Where

is the input value,

out

is the output

value, and

is a constant. When

, the bright

pixel region is stretched and the dark pixel region is

compressed; When

, the dark pixel region is

stretched and the bright pixel region is compressed.

When

, linear stretching will be done. (Lin

Wenpeng et al., 2015)

3.4 Classification of Natural

Breakpoints

In order to better display the evaluation results,

natural breakpoint classification (Jenks, G. et al.,

2016; Xu Guiyang et al., 2020), a commonly used

classification method, is used to classify the output

images after enhancement. It believes that there are

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322

some natural turning points or breakpoints in the

value of a phenomenon, and these irregular

classification limits can divide the data into groups

with similar properties. The classification principle of

the natural breakpoint method is to compare the

variance sum of all classification schemes iteratively,

and the smallest is the optimal result.

4 RESULTS ANALYSIS

The network assessment model of groundwater

enrichment character was constructed by Python and

ArcGIS. According to the evaluation index system of

groundwater enrichment, the data results of 10 grid

indexes were obtained. According to the entropy

weight method combined with artificial experience,

the information entropy and entropy weight of each

evaluation index to the first-level index are

calculated, as well as the final relative weight of each

evaluation index.

According to the result of entropy weight, in terms

of contribution rate to groundwater enrichment in the

study area, water-bearing faults, water-bearing folds,

springs and overflow zones are more important than

lithology and surface water, which is consistent with

the weight of first-level indicators given by experts.

The final relative weight and entropy weight were

respectively used to calculate the groundwater

enrichment score of the study area. Gamma

transformation was performed on the scoring image

by selecting Gamma = 0.2, and the transformed image

was stretched to. The natural breakpoint method was

used to classify the groundwater enrichment in the

final study area by selecting 5 grades, and the

assessment results of groundwater enrichment in the

final study area are shown in Figure 4 (a), and the

assessment results without reference to manual

experience are shown in Figure 4 (b). Among them,

the water-rich potential of grades 1 to 5 increases

successively. The results of expert scoring method

were taken as the reference and comparison results

provided by Xining Comprehensive Natural

Resources Survey Center of China Geological

Survey, as shown in Figure 4 (c). The histogram of

regional proportion was drawn with Grade 1 as low

potential area, Grade 2, 3 and 4 as medium potential

area, and Grade 5 as high potential area, as shown in

Figure 4. By observing Figure 4, it can be obtained:

 The location distribution of water-rich high-

potential areas and water-rich low-potential

areas obtained by the evaluation model in this

study corresponded roughly with that of the

corresponding regions in the reference image,

and the proportion of low, medium and high

potential areas was basically consistent with

that in the reference image. After the addition

of manual experience, the evaluation results

were improved and closer to the reference

results.

Table 1: Weight table of each evaluation index.

Evaluation index system of groundwater enrichment character

network

First

level

indicat

ors

Weight Second level indicators

The

information

entropy

Entropy

weight

Final relative

weight

The

litholo

0.1

Lithologic positive index 0.950177 0.0170941 0.00170941

Lithologic negative index 0.986357 0.00468076 0.000468076

Quaternary loose soils 0.916361 0.028696 0.0028696

Water-

bearing

fractur

0.2

Water-bearing zone fracture 0.575037 0.145803 0.0291606

Water-bearing zone general

fracture

0.713141 0.0984198 0.01968396

Water-bearing zone small

fracture

0.57495 0.145833 0.0291666

Water

cut fol

0.2 Water cut fold 0.485504 0.176521 0.0353042

Spring 0.2 Spring 0.577541 0.144944 0.0289888

Ground

water

spill

0.2 Groundwater spill belt 0.439394 0.192341 0.0384682

The

surface

wate

0.1 The surface water 0.866895 0.0456675 0.00456675

Grid-Based Assessment of Groundwater Potential Using GIS

323

(a) Results of this method

(b) Results without reference to manual

erience

Figure 4: Evaluation results of groundwater enrichment in the study area.

 About 36.00% of the area is assessed as low

potential groundwater rich area, mainly

distributed in the southwest and north of

I43C001004.

 About 62.30% of the area is assessed as

groundwater rich potential area.

 About 1.70% of the area is assessed as the

water-rich and high-potential area of

underground water, which is distributed in the

northeast of I43C001004, southwest of

I43C002003, southwest of I43C002004, and

central of I43C003004. The regions with strong

correlation with the location of spring point and

distribution of water-bearing faults have higher

water-rich potential of groundwater.

5 CONCLUSIONS

 The entropy weight of item q’th is: In view of

the present groundwater enrichment evaluation

index weight determining by man's subjective

factors affect too much problem, design a

subnetted groundwater enrichment character

evaluation method based on GIS technology,

based on index system is constructed, grid

handling analysis, combined with artificial

experience of entropy method, and natural

Gamma transform breakpoint subnetted

groundwater enrichment character evaluation

model of classification, The assessment of

groundwater enrichment characteristics in the

Belt and Road area has been completed.

 This study evaluating method to get the high

potential of aqueous area, low potential area

and reference images in the corresponding

region roughly corresponding to the location of

the distribution, low, medium and high

potential areas of reference images and is in

line with, the evaluation method to the relative

weight of the objective and subjective

experience guidance, realization of a wide

range of groundwater enrichment evaluations.

 About 36.00% of the area is assessed as low

potential area with rich groundwater, about

62.30% as medium potential area, and about

1.70% as high potential area. The high potential

area has a strong correlation with the location

of spring point and water-bearing fracture.

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324

ACKNOWLEDGEMENTS

This paper was made possible by the Geological

Survey Project of China Geological Survey

(DD20191016). The statements made herein are

solely the responsibility of the authors

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