15
AMALGAMATING FUZZY COGNITIVE MAPPING (FCM) AND MACHINE LEARNING TO
ANALYZE CLIMATIC VARIABILITY
Swarnima Singh
Assistant Professor, Department of Geography,
DDU Gorakhpur University Gorakhpur, U.P., India.
Email: swarnima.singh@gmail.com

How to cite this paper:
Singh, Swarnima (2023)
Amalgamating
Fuzzy
Cognitive Mapping (FCM)
and Machine Learning to
Analyze Climatic Variability,
Journal of Global Resources,
Vol. 09 (01)DOI:
10.46587/JGR.2023.v09i01.015

Received: 15 Oct. 2022
Reviewed: 25 Nov. 2022
Final Accepted: 15 Dec. 2022

Abstract: Predictive Mechanisms This hybrid approach may
be central to vulnerability and resilience systems, as well asclimate change analysis. Almost every scientific discipline
deal with prediction modeling, yet at the same time thesubject is often viewed as highly complex consisting of
numerous factors that have contrasting nature. Therefore,
the resultant outcome varies from model to model andprediction may appear to be highly uncertain at times. This
novel approach to integrate prediction models throughmachine learning and Fuzzy Cognitive Mapping (FCM) may
bring nearly accurate results with a hybrid approach inclimate change analysis. In prediction mechanism, thishybrid approach could be pivotal not only for analyzing
climate change but also for the vulnerability and resiliencesystems.
Key words: Fuzzy Cognitive Mapping, Predication, Climate
Variability, Spatial Simulation.

150

Introduction
Despite the advancement in the field of science and technology and the enhanced ability of
mankind to achieve ecological dominance step by step, the integrated use of technology is stillon the way. Therefore, all the stakeholders including academicians, policymakers, scientists,
researchers, and politicians are required to work in public-private partnerships in creating a
way out of predisposition to the negative effect of climate change. This paper aims to provide
a platform for analyzing the heterogeneous concoction of qualitative, semi-quantitative, andquantitative measures aiming to produce an understanding of climate change andsustainability goals. It would probably ease to decrypt responses and perceptions ofindividuals on climate vulnerability and its induced threats in achieving sustainability solutions.
Fuzzy Cognitive Mapping (FCM) is steadily developing as an alternate practice in hybrid
modeling. The significant changes in the statistical distribution of long-term weather conditions
are known as climate change. These climatic processes take over periods ranging from few
decades to millions of years to unveil some pattern. It is caused by factorial variations in its
processes such as; altering wind, precipitation, insolation, temperature, Outgoing Longwave
Radiation (OLR) or albedo, air pressure, radiative forcing, etc., these dynamic factors are
hybrid in nature meaning thereby it incorporates both natural as well as anthropogenic
elements that significantly causes variation in recent pattern. Climate change and induced risk
in its dependent sectors have been widely studied. There is an urgent need to study thehumanistic aspect of climate-induced vulnerability besides the natural aspect of it. The
multifaceted climate-human-environment space vulnerability assessments in the current
generation are optimistically intruding at a micro level rather than just focused on an umbrellacut approach. The first-generation modeling that was limited to the Risk hazard (RH) model
started including both biophysical jeopardy aspects and its resultant potential for loss of lives
and property to the exposed population (Singh and Singh 2021; O’ Brien et al. 2004; Boddiger
2007; Giabbanelli et.al 2019; Singh 1998; Singh and Singh 2014) then after secondgeneration Pressure and Release (PAR) model brought political ecology and economy forhighlighting a vulnerability in prevailing social inequities. But these two generations of models
were little incapable to address issues relating to the system’s aptitude to recover from
distress. It was only recently realized the need for the amalgam of dynamic resilience,
economy, society, and policy concerns in assessing vulnerability stimulated by global
environmental change. This integration of dynamic machine learning and FCM analysis isbased on active indicators and metrics that has been tested to link global system from macro(national) to micro (household) levels to assess vulnerability indices (VI) (Singh and Singh
2020; Eakin and Bojorquez 2008; Hahn et al. 2009; Gray et al., 2019).
Climate Variability in Western Himalayan District: Hamirpur a Case Study
The agrarian economy of Hamirpur district located in the South-West part of Himachal
Pradesh is highly dependent on the topography and climatic conditions. It constitutes thecentral micro-region having a moderately hilly tract where elevation varies from 400 meters to1100 meters, enclosed by the Shivalik range located between 76º 18′ to 76º 44’ E longitudes
and 31º 25′ to 31º 52′ N Latitude. Climatic change is already threatening the present andfuture existence and the significant impacts of these changes have already been observed
through erratic weather phenomena, extinction of species, and unfavorable effects on humanhealth and quality of life. With the consequential changes in the future biosphere, biodiversity,and natural resources the district will confront further more serious issues related to climatechange. Many believe agriculture is the most susceptible sector to climate change and thechanging climate directly affects the main inputs of agricultural production i.e., precipitation

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and temperature. The diverse aspects and altitudes demonstrate considerable variation in the
distribution of temperature and rainfall in Hamirpur, whereas finding out the precise magnitudeof climatic trends has become a daunting task due to inadequate long-term in-situ
observations and complex topographic conditions of the district. The aggregated data fromseven meteorological stations (on annual maximum, minimum, and average temperature andprecipitation have been taken for five decades (1970–2020) to produce hybrid results throughthis geospatial machine learning approach.
DATABASE AND METHODOLOGY
Baseline Data Requirement
The mean monthly maximum and minimum temperature and precipitation together with annual
minima and maxima from networks of metrological stations, for the period 1970–2020 havebeen compiled because these are fundamental instrumental statistics for the analysis ofclimate dynamics in the micro study area. Though it can be noticed that the meteorologicalsystems recording network are noticeably less in number, therefore, for the current ten years/
decade Atmospheric Infrared Sounder (AIRS) satellite, TRMM, and PRECIS data have been
considered to analyze the gap. Consequently, several checks have been made for missing
values in the previous data set to distinguish the regular pattern of regional precipitation andtemperature variation in the district. The annual and seasonal temperatures and precipitation
progression for the winter-months (December-January-February), pre-monsoon (March-AprilMay), monsoon (June-July-August-September) and post-monsoon (October-November)months have been calibrated. These variations have been recorded for the entire district fromall the three regions; the Shivalik-hill, the mid-hill and the high-hill region. To calibrate the pastand current climatic trends two different data sources have been taken into consideration inmachine learning.
Machine Learning Integration of Satellite Data Mechanism
The AIRS data is having high spectral resolution spectrometer on board Aqua satellite with
2,378 bands in the thermal infrared (3.7 to 15.3 μm) and four bands in the visible (0.4 to 7.8μm). These ranges have been precisely elected to allow atmospheric temperature andhumidity in the troposphere with a precision of 0.5°C in 1-kilometer-thick layer and 80 per centaccuracy in the case of humidity in 2 km thick layers. The nadir is scanned in every two
seconds afterward a prompt scan in ⅔rd of the second by appropriating standardization
related data of four independent Cold Space Views (CSVs). These CSVs are Onboard
namely; the blackbody calibrator, the spectral reference source, two views combining aphotometric calibrator for the Visible Spectrum (VS) and Near Infra-Red (NIR) photometer
where each scan line includes 90 IR paths for detailed information. The VS / NIR spatial
resolution is around 2.31 km at nadir, provides 15 channel microwave temperature sounder at
two independent operated modules of Advanced Microwave Sounding Units (AMSU).
Although it requires a numerical model for its authentication and development over the time. In
this study, a new gridded daily temperature and rainfall dataset at 1°×1° latitude and longituderesolution covering 40 monsoon seasons (1970–2020) has been downscaled on the PRECIS
norm and amalgamated with Merged Satellite Gauge rainfall dataset (MSG) through machinelearning has been explained in methodological flowchart (Fig. 1). The MSG data combines
Tropical Rainfall Measuring Mission (TRMM) rainfall estimates of multi-satellite precipitationand gauge information from IMD through its product 2A-53and 3B-42 and AIRS. It has
become suitable to use downscaled data for monthly average rain and temperature parameter
respectively at 0.25x0.25 latitude and longitude with the help of Ground Radar (GR). This152

dataset may provide valuable intra-seasonal studies output for the model development andmicro climate baseline research further.
Need for Fuzzy Cognitive Model (FCM)
Climate change studies are increasingly authenticating the local and indigenous impacts ofclimate variability and locally developed tools for knowledge generation, adaptation, rational
management and exploitation of resources are being reviewed at global to local level’s
governments, inter-government and non-government organizations (Fields 2005; Fowler 2002;
Fussel et al., 2006). Therefore, perceptions-based vulnerability index is required to entangle
the vulnerability with evidence-based decision-making where each stakeholder of society caneffectively participate to reduce the climatic stress. The construction of FCM is based on semiquantification of people’s perceptions where the perception regarding knowledge, information,
values, and relationships are mapped to demonstrate cause-effect relations.
Figure 01: Methodological Framework for Spatio-temporal Climate Change
Point map of
IMD stations

IMD stations data for1970 to 2004

Rainfall gridded
data at 1°×1°

Temperature
gridded data at
1.5°×2°

TRMM 2000
to 2013
Temperature
gridded data at
0.25°×0.25°

AIRS 2000 to2013
Rainfallgridded data at
0.25°x0.25°

Downscaling at
0.25°x0.25°
Contours creation
through ArcGIS

Surface creation
using ArcGIS

Isotherm andisohyet Maps

Surfacecreation using
ArcGIS

Overlay Raster Change
detection map 1970 to 2013

Spatio-temporalchange maps for 1970
- 2013

Fuzzy Cognitive Mapping Algorithms (FCMA)
In fuzzy cognitive mapping the set is used as a map, where μ: A → [varies between 0 and1]
where A belong to any set called the domain (like climate change) and [0, 1] is the impacted orvulnerability range. That is to every element a ϵ A, μ allocates relationship value in theinterval [0, 1]. In fuzzy algorithms it is often showed as relational function in two-dimensionalgraphs. While analysing fuzzy cognitive mapping both the climate variability (agent) A= (a1,a2…..an) and the vulnerable element μ: A → [0 and1], domains consider plotting in spider
diagram or triangle, where it is a resultant of close inputs to close outputs. It can be assumed
that there are n characteristics of changing climate like; c1, c2….. cn, where n is predetermined,
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and let L1, L2, …,Ln be the descriptions related with the livelihood. The connection matrix iscreated and named as “A” of command cn X Ln is acquired. Where c1 will be the initial input
element or condition vector of changing climate is kept in ON (1) condition and all othercomponents in OFF (0) condition (Table 1a and 1b). The condition vector c1 then been passed
through the connection matrix A, then for the conversion of the resultant vector in connection
matrix A with signal function the first two highest values have been put at ON (1) and othervalues to OFF (0) or 1 and 0 respectively to symbolize this progression. With this forward
and backward progression, the resulted vector is multiplied with AT and the maxima yields a
new vector B1. The resultant vector which is product of 1st is been related with the connection
matrix and that vector which gives the highest number of attributes to ON state is chosen as
C2 (Table 1a and 1b). Therefore, in this way the same procedure has been repeated till a fixed
point/ a till the limit cycle is achieved. This progression is prepared to provide separate but
appropriate significance to each vector. For each run of ON and OFF case several hidden
patterns of various vectors have been observed that provided the guidelines to draw
inferences through these hidden patterns that expose the causes of vulnerability. The
following characteristics have been outlined in the table 1a and 1b are associated with theoutcomes of changing climate as connections/points in the local space domain:
Table 1a: Changing Climate Attributes
c1 - Heat waves
c 2 - Cold spells
c3 - Rainfall/precipitation variability
c4 - Warm nights
c5 - Warm daysc6 - Cool nights
c7 - Cool days
c8 - Diurnal temperature range
c9 - Length of Growing Period
c10 –Summer days
c11 - Frost days

c12 - Land use/land cover change
c13 - Fresh water availability
c14 - Shifts in farming systemc15 - Displacement/ shift or Migration of species
c16 - Ecological disturbancec17 - Increment in infectious diseasec18 - Local pollutants Species extinction
c19- Local level disasters; landslides, floods, droughts
c20 - Loss of Biodiversity
c21 - Threatened food supply

Table 1b: Changing Means of support Attributes
Minor Variables (in Percent)
L 1L 2L 3L 4L 5L 6L 7L 8L 9L10L11L12L13L14L15L16L17L18L19L20-

Households having agricultural landHouseholds having cultivated area under irrigation
Households that utilize a natural water source
Households grow multiple crops in a season
Household having surplus production at present
Households sell the product out of total production
Households dependent on family farm for foodHouseholds dependent on livestock for milk and otherAverage agricultural livelihood diversification index
Households where head of household has not attended school
Households not having basic education facility
Average time spend in having medical facility
Households having pucca house types
Households who are dependent on well/ponds for drinking water
Households dependent on forests for NTFPs (honey, medicine etc.)
Household facing plantation impact on agricultural land availability
Percent of households dependent solely on the family farm for food
Average number of months households struggle to find foodWorking population to the total population
Households dependent solely on agriculture as a source of income

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Abbreviation
Minor Variables
HAL
HCAI
HNW
HMCS
HSP_Present
HSoPro_Tpro
DH_FF
DHL_M
AAgri_LD
H_Edu
NB_EduNB_MedPuuca_HPwell
H_NTFPs
Plan_LA
D_FF
Av_Sfood%WP_TP
D_AI

Major
Component
Natural CapitalCategory(NCC)

Social CapitalCategory(SCC)

PhysicalCapitalCategory(PCC)
Financial
CapitalCategory(FCC)

Based on the above 41 attributes of climate change and livelihood vulnerability the relational
matrix ‘A’ is computed and shown in the form of the table to display the fuzzy value ranging
between 0 and 1, where the input vector c1 = (1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0). That is the attribute Heat wave is kept in ON condition and the rest of the nodes in
OFF condition.
c1 = (1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0)
c1A = (0.5 0.6 0.8 0.5 0.9 0.3 0.4 0.1 0.30.5 0.6 0.7 0.9 0.1 0.8 0.7 0.2 0.6 0.8 0.5) and so on
till AT times.

A=

c1
c2
c3
c4
c5
c6
c7

L1
0.5
0.7
0
0.6
0.6
0.1
0.5

L2
0.6
0.2
0.8
0.7
0.3
1
0.5

L3
0.8
0.4
0.4
0.9
0.4
0.6
0.5

L4
0.5
0.5
0.6
0.6
0.6
0.1
0.5

L5
0.9
0.7
1
0.8
0.3
0.5
0.8

L6
0.3
0.5
0.3
0.7
0.9
0.6
0.7

L7
0.4
0.3
0.5
0.2
0.6
0.
0.2

L8
0.1
0.2
0.5
0.6
0.4
1
0.6

L9
0.3
0.1
0
0.5
0.5
0.5
0.6

L10
0.5
0
0.8
0.5
0.9
0.7
1

L11
0.6
0.6
0.1
0.9
0.3
0.5
0.3

L12
0.7
0.3
1
0.5
0.4
0.3
0.5

L13
0.9
0.4
0.6
0.9
0.1
0.2
0.5

L14
0.1
0.2
0.8
0.4
0.5
0.5
0.5

L15
0.8
0.3
0.5
0.1
0.5
0.5
0.5

L16
0.7
0.9
0.6
0.8
0.3
0.5
0.8

L17
0.2
0.6
0.
0.7
0.9
0.6
0.7

L18
0.6
0.4
1
0.2
0.6
0.
0.2

L19
0.8
0.1
0.9
0.6
0.4
1
0.6

L20
0.5
0.7
0.9
0.8
0.3
0.5
0.8

These computed integrated climate change induced paths can be combined anddisplay through spider graph (will be discussed in results).
Climate Change Induced Vulnerability Categories (CCVC)
To calculate composite index of Climate Change Induced Vulnerability Category (CCVC) the
FCM set have been used where the value ranges between -1 to +1, where -1 denotes least
vulnerable, 0 signifies vulnerable and 1 designates highly vulnerable, the calculation has been
done through the following steps as;
Step 1: (reiterate for all variables)
Where,

is productivity of a particular crop has increased with temperature variable, thathas been computed based on 30 years data,
temperature range,

andis observed productivity in thatare minimum and maximum productivity at thattemperature range. In other words, it is (observed - minimum) / (maximum - minimum)
Step 2: (reiterate for all components) if for the temperature component;

where, TI is temperature Index component, the variable; Vob is observed changes, VLU is Landuse changes, VAlbedo is albedo changes with spatio-temporal temperature trend. Where, thetemperature component is having 6 variables and the respective values of Vob… …..…Vn.
Step 3: would follow Climate Change Vulnerability Indices (CCVI) with summation of its allcomponents, the variable values are multiplied by the assign weightages (where,
Vt=temperature, Vr= rainfall, VLU = Land use) that are divided by their respective total weights
as: CCVC =Where CCVC == 0.932

Finally the CCVCs of the district is calculated for initially three primary sectors
dependent community i.e. farmers, fisherman, and agricultural laborers in the district where all
the information have been clubbed together with their respective Vulnerability Categories
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(VCs) to show negative climate impact for all the respective Natural Capital Categories (NCCs),
Physical Capital Categories (PCCs), Human Capital Categories (HCCs), Social Capital
Indices (SCCs) and Financial Capital Indices (FCCs) across region (based on computationfrom Table 1b).
Results and Discussion
The variation in mean maximum and minimum temperatures over the period of 1970 to 2020
has observed over Hamirpur where a net escalation in temperature ranges from 0.86˚C ±0.04˚C (Table 2). The five color categories of seasonal temperature change ranging from lessthan 0.0˚C to more than 0.75˚C of Hamirpur district with a general rise in mean seasonaltemperature in its blocks from 1970 to 2020 explains that the temperature variability has
increased over time.
Table 02: List of 12 Core Climate Indices and the Rate of Change Over 1970 to 2020
ID

Indicator

FD0

Frost days

SD25

Summer days

DTR

Length of Growing
PeriodMaximum Temperatureof Tmax
Maximum Temperature
Tmin
Minimum Temperature
Tmax
Minimum Temperature
Tmin
Diurnal temperature
range

TX10p

Cool nights

TX10p

Cool days

TY90p

Warm nights

TY90p

Warm days

LGP
TXmax
TXmin
TYmax
TYmin

Explanation
Annual count when daily minimum
temperature is <0 ˚CAnnual count when Txmax (daily
maximum)>25˚CAnnual (1st Jan to 31 st Dec in Northern
Hemisphere) count between first span of atleast 6 days with > 5˚CMonthly maximum value of daily maximum
temp
Monthly maximum value of daily minimum
tempMonthly minimum value of daily maximum
temp
Monthly minimum value of daily minimum
tempMonthly mean difference between TX andTN
Percentage of days when TXmax<10thpercentile
Percentage of days when Txmin <10thpercentile
Percentage of days when TYmax>90th
percentile
Percentage of days when TYmin>90th
percentile

Measured
Unit
days

ChangeDays-12.3(-10....5.6)
6.78(2.3…27.6)

Days

9.84
(0.4.…21.6)

˚C
˚C
˚C
˚C

1.2 (-1.0…1.5)
0.86 (1.3…3.1)
1.57(0.25…3.9)

˚C

0.7(-0.4…2.1)-0.65(6.5…0.9)

Days

6.3(2.5…9.5)

Days-5.2(-4…-12.2)

Days

4.8(3.7…13.6)

Days-0.8(-2.0…0.9)

Source: Machine learning results from PRECIS and TRMM

The overall spatio-temporal study of temperature and precipitation has been done tocompute and analyse the past, present and future simulated scenario (i.e., 2020s). Most of themeteorological stations indicated warming and erratic trend in temporal mean surface
temperature and precipitation respectively (Collins and Cooper, 2001; Kumar et al., 2006).
The Regional Climate Model (RCM) prediction shows that the temperature of the study area
has already rose from 0.5 to 1.5°C in eastern and western region respectively between 1970
to 2020 and the monsoon rainfall has decreased by 5 to 12 per cent of the annual rainfall,
whereas the total annual rainfall will increase by 7 per cent by 2020. The maximum andminimum temperature represents inconsistent signal of climatic change, and more precisely
the maximum temperature exhibits higher magnitude for warming whereas the minimum
temperature displays much larger variability such as no change, positive or negative change.
The sudden increment in maximum temperature and variable average precipitation has been
observed during the last and current decade.
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The Scenario Analysis
As soon as the FCMs are done the possible climate change/variability scenario has beendeveloped to analyse the strength and the relationships of various primary sector focus groups
where the variables/attributes of changing climate and livelihood vulnerability and theirrelationships have already been entered into the relational matrix ‘A.’ The two scenarios havebeen drawn; one is the baseline or business as usual and other is the climate change scenario
based on existing anticipations from changing climate. This are probable increase of minimum
summer temperature, changing degree days, shifting or intrusion of new species etc., due toabove climatic perturbations.
Baseline Scenario
It is all dependent on normal precipitation along with the district standard where there are few
events of damage of crop, livestock, and livelihood. It would have been a potential andaccessible system that permits the local population including farmers, fisherman andAgricultural laborer to access resources without any intricacy. In appendage to it there arevery sporadic chances of livestock death, crop failure and non-remittance of recognizable
contribution to household in the form of cash and food (Fig. 2).
Figure 02: Asset Pentagon for Livelihood Groups in Baseline (Present) and under
Climate Change Scenario (1970-2020)

Source: Calculated from Primary Survey, 2014-20.

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Climate Change Scenario
There are 21 selected indicators on which the observed change has been analysed for theentire region. The total number of frost days, summer days, Length of Growing Period (LGP),
maximum and minimum temperature, diurnal temperature range, cool and warm nights anddays trend results exhibit the patterned geographical distribution is reflecting significant
decreases in cold nights and increase in warm night meaning thereby the general warming
trend is predominant in the entire region. The Daily Temperature Range (DTR) is reflecting
consistent decreasing trend of -0.65°C. The annual count of summer days trends whenmaximum temperature is no less than 25˚C (standard deviation 25) have increased by 6.78
days and frost days have decreased by 12.3 days over period through big data and machinelearning. The Length of Growing Period (LGP is extended by 9.84 days. This smaller warmingof day time versus night time extremes is due to the amount of annual precipitation that
decreases drastically as compared to mean 1458.7 for 2000-2020 showing significant changewith latitudinal distribution of temperatures and rainfall. Rainfall is insufficient for cropping, andthere is a high degree of uncertainty as to when it will rain next. There is no harvest fromupland fields, many more livestock will die, less inflow of remittances are universal andbecoming more common climate change scenario.
Figure 03: Changing Rainfall Trend over 43 Years for Prediction Modeling

Conclusion and Suggestions
The basic aim of this paper was to propose a new hybrid model integration to improve theprecision of a real-time estimation. The method of integrating geospatial data and machinelearning is an upcoming field that would overpower the difficulties faced in such multifaceted
and multi-conflicting environments. The analyzed approach has been constructed so that it
can divulge inter-relationships between the climatic inputs of a given dataset and deliver aconcealed variable through this conjunction approach for precise prediction modeling.
By using the FCM model one can say that the priority should be given tointerconnectedness of climate vulnerability. This study has been done to show impartial
results through hybrid geospatial system analysis, where the blame is not put on climatechange without logical investigation. The findings of the study are based on local people’s
response to climate change, where the researcher has tried to establish a micro-levelsustainable framework for varied. The impact was also observed for the loss of productive
agricultural land that has forced many youths and active labor force to go to other districts insearch of employment. This has created labor crisis in their communities and have subjected158

to influence women labor force in order to rescue and rehabilitate their families and livelihood
assets. Thus, the climatic and non-climatic stressors will continue to increase the degradationand pressurize natural resource base that will further exacerbate the society’s vulnerability. It
can only be minimized by integration of public, private and government initiatives.
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