A Unified Big Data and AI‑Driven Predictive Framework for

Multi‑Risk Climate Pattern Modeling and Environmental Hazard

Forecasting

E. Raghavendrakumar

, V. Kamalakar

, C. Selvakumar

, P. Vignesh

, V. Divya

and Keerthana R.

Department of Physics, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil

Nadu, India

Assistant Professor, Department of Information Technology, J.J.College of Engineering and Technology, Tiruchirappalli,

Tamil Nadu, India

Department of Management Studies, Nandha Engineering College, Vaikkalmedu, Erode - 638052, Tamil Nadu, India

Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad‑500043, Telangana, India

Department of ECE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Keywords: Climate Modeling, Big Data Analytics, Environmental Forecasting, Predictive Framework, Machine

Learning.

Abstract: More frequent and severe climate-related events require forward-looking systems that can simulate complex

environmental interactions. In this paper, we present a one-spot big data and AI-enabled prediction framework

that is comprehensively developed for simulating the climate change and predicting wide range of its

associated environmental hazards such as floods, heatwaves, and droughts. Using diverse datasets including

satellite images, weather data and sensors from the IoT networks, the system applies machine learning and

deep neural networks to detect trends, predict the future and send alerts on risks early. The method is in

contrast with current practices, which are often limited by specific climate zone or cover only a limited extent

of the variables, making it scalable and applicable for different climatic zones. We validate the performance

of the system using real-time data and show that the predictions are more accurate, comprehensible and

contain more policy information that can be integrated into climate resilience policies than previous methods.

1 INTRODUCTION

There is now an increasing urgency that climate

change is becoming one of the greatest global

challenges of the 21st century, which has direct

consequences in the emergence of extreme weather

events, sea level rise, precipitation patterns and the

increase in natural disasters and hazards. While the

complexity and unpredictable nature of these

phenomena increase, a pressing need exist for

intelligent systems being able to understand and

predict climate actions with high accuracy. Historical

models have been challenged to integrate the data and

do not scale and adapt in real-time as needed. The

explosion in environmental data from satellites,

sensor networks, remote monitoring and the like

offers a once-in-a-generation chance to revolutionize

the way cities are built and climate is predicted. In this

regard, Big Data analysis empowered by the power of

artificial intelligence tools provides a powerful means

to generating dynamic climate system models and to

predicting environmental risk. When huge and

disparate data-sets have been effectively synthesised

and analysed, predictive analytics brings new insights

into play, revealing patterns, simulating reactions to

causal factors and warning of potential disasters. This

paper introduces a holistic predictive framework,

which utilizes big data and AI to integrate climate

science and actionable intelligence and thus to

support decision-making in environmental planning,

risk reduction, and policy making.

2 PROBLEM STATEMENT

In spite of the availability of large environmental data

sets and the increasing urgency of managing climate

induced risks, predictions under current models often

Raghavendrakumar, E., Kamalakar, V., Selvakumar, C., Vignesh, P., Divya, V. and R., K.

A Uniﬁed Big Data and AI-Driven Predictive Framework for Multi-Risk Climate Pattern Modeling and Environmental Hazard Forecasting.

DOI: 10.5220/0013866000004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages

363-370

ISBN: 978-989-758-777-1

363

lack the ability to succinctly characterize the multi-

scale, highly variable nature of climate change. The

models have several shortcomings, such as low

applicability, no integration with different sorts of

data, and unsatisfactory prediction of multi-

dimensional environmental hazards. Furthermore,

much of the existing work is region-specific, too

computationally-expensive, or not developed to

generate timely, interpretable information which is

essential for timely, proactive decision-making. This

represents a fundamental lack of scalable predictive

analytics engine for pressure downscaling that can

bridge big data and machine learning to produce

actionable intelligence in terms of, for example,

reliable predictions, disaster preparedness, and

dynamic global environmental risk assessment.

3 LITERATURESURVEY

5+ Recent developments of climate science have

witnessed increasingly on big data analytics and

artificial intelligence with a promise of better and

faster predictions of environmental risk. Beucler et

al. (2021) investigated climate-invariant machine

learning models which show promising results for

generalized weather pattern analysis, but so far have

not reached scales large enough to be available as

open datasets. Jacques-Dumas et al. (2021)

investigated a deep learning approach for extreme

heatwave prediction, highlighting the strength of

neural networks to capture high-impact events. Yet

such methods tend to neglect how systems to which

the considered network belongs is integrated within

larger datasets, crucial for long-term prediction. The

growing significance of AI in the simulation of

extreme climate events is further noted in Nature

Communications (2025), where it is claimed that

deep neural networks hold the potential to decipher

complicated atmospheric patterns.

Some research has tried to connect climate

resilience and predictive analytics. Neuroject (2025)

and ResearchGate (2025) offer conceptual means to

exploit AI in climate resilience, however, they lack

real-time implementation proof or scalability.

Technological Forecasting and Social Change

(2025) is an overview of sustainable technology, with

no close examination of predictive systems. On the

contrary, Energy Informatics (2024) provides an

overview of big data trends, but with little about

practical model evaluation.

Attempts to predict environmental risk in

particular sectors such as the oil and gas industry are

highlighted elsewhere in a @ResearchGate (2025)

article that leverages big data analytics for

sustainability analysis. It extends previous water

resources assessments by including more industrial

sectors but is less broadly applicable across climate

regimes and water uses. Studies such as Information

& Management (2021) and Environmental Science

and Pollution Research (2025) offer valuable insight

into the application of AI to climate-related

problems, but often focus on single independent

variables or limited regional data sets. Also,

Sustainable Cities and Society (2025) also focuses on

urban data infrastructures but do not further move

toward large-scale environmental risk modeling.

A broader vision of climate modeling, weather

and climate prediction is expressed in Frontiers in

Environmental Science (2021), as is the early promise

of big data in climate research, thereby pointing to the

necessity of new frameworks that embed AI methods.

IISD (2025) focuses on policy considerations and

long-term risks, but does not have the predictive

functionality necessary to act proactively.

Other relevant works, such as ResearchGate

(2023) and Presight AI (2023), study the intersection

of climate modeling with AI but are essentially

strategic in nature and do not validate any kind of

model. IoT Times (2024) and TechTarget (2025)

spotlight new technologies that are pragmatic to the

field, but their contributions are more trend-oriented

and less evidence-based. Market Databy Global

Market Insights (2025) forecasts an exponential

increase in AI-driven climate modeling, but this is

still lacking empirical evidence. Axios (2025),

Financial Times (2024), and Scientific American

(2025) cover the topic indicating journalistic interest

in AI’s potential for climate, with minimal technical

sublety.

Technically more focused stories are seen in MIT

Technology Review (2024) on AI predicting disasters

and Nature (2023), perhaps ironically, outlining AI as

a savior for climate research, despite the nature of its

content. Lastly, Brookings (2025) links big data

analytics and climate adaptation policy, but does not

feature an integrated predictive modeling framework.

Taken together, these studies highlight a significant

void in research in creating a large-scale, AI-

embedded big data framework for a precise modeling

of complex climate change patterns and for predicting

multi-hazards with environmental risk. In this paper,

a deficient gap between systolic-diastolic phase

screening and detection & comparison-based

diagnosis has been made up with by using the unified,

real-time prediction system that avoids the

shortcomings of many related works.

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4 METHODOLOGY

The overarching aim is to make a better prognosis on

environmental risks with high precision and

adaptability by developing a unified predictive

analytics framework using big data processing,

machine learning and climate modelling. The figure

1 shows the Predictive Analytics Framework for

Climate Risk Forecasting. The table 1 shows the

Table 1: Climate Data Sources and Attributes. The

process starts with obtaining heterogeneous datasets

from various sources such as satellite images,

historic meteorological records, remote sensing

service as well as real-time feeds from Internet of

Things (IoT) environmental sensors. Such datasets

are intrinsically large, diverse, and unstructured, and

processing, normalizing, and integrating them is only

feasible using scalable big data technologies.

Distributed data processing with APACHE Hadoop

and APACHE Spark are used to efficiently store and

process such terabyte-scale climate data.

It is followed by feature extraction step where

specific climate variables including oscillation of the

temperature, the intensity of rain, the direction of the

wind, the pressure of the atmosphere, and the

moisture of the soil are extracted automatically.

Temporal and spatial correlations are preserved

during the procedure to preserve the well-founded

context for precise pattern recognition. Dimension

reduction techniques such as PCA, t-SNE are used to

increase the efficiency of the feature space and #the

Jpage and reduce computational burden/without

affecting the predictive integrity of the data.

Table 1: Climate Data Sources and Attributes.

Data Source

Type

Provider/Platform Key Attributes Captured Update

Frequency

Format

Satellite

Imagery

NASA MODIS,

ESA Sentinel

Vegetation index,

surface temp, moisture

Daily GeoTIFF

IoT Sensor

Networks

Government,

OpenWeathe

Rainfall, humidity,

temperature, wind speed

Real-Time /

Hourly

CSV, JSON

Meteorological

Data

NOAA, IMD Historical climate

records, pressure levels

Hourly / Daily NetCDF

Remote Sensing

Drones

Local Agencies Soil conditions, land

temperature

Event-Based JPEG, CSV

Figure 1: Predictive Analytics Framework for Climate Risk Forecasting.

A Uniﬁed Big Data and AI-Driven Predictive Framework for Multi-Risk Climate Pattern Modeling and Environmental Hazard Forecasting

365

Table 2: Feature Set and Relevance for Prediction.

Feature Name Source Type Relevance to Prediction

Temperature IoT, Satellite Continuous

High – core input for

heatwave/drought

Rainfall

Meteorologic

Continuous

High – essential for flood

rediction

Vegetation Index

Satellite

(NDVI)

Continuous

Medium – used for

drought detection

Wind Speed IoT Sensors Continuous

Low – indirect factor in

storm modeling

Soil Moisture

Remote

Sensing

Continuous

High – drought and flood

analysis

The approach avoids error-prone and time-

consuming manual tuning by employing a complex

hybrid ensemble of machine learning (ML) and deep

learning (DL) models that are designed to capture the

complex behavior of environmental systems. to

capture long-range temporal dependencies in time

sequences, which are applicable for long-term climate

patterns prediction. The table 2 shows the Table 2:

Feature Set and Relevance for Prediction. CNNs are

used to analyze satellite imagery and geospatial data

spatially. These are accompanied by Gradient

Boosting Machines (GBMs) and Random Forests to

improve model stability and mitigate overfitting,

especially with structured datasets.

Table 3: Machine Learning Models Used and Performance Metrics.

Model

Name

Algorithm Type

Accurac

y (%)

Precision (%) Recall (%) F1 Score (%)

LSTM

Deep Learning

(

RNN

)

91.4 92.0 89.6 90.7

CNN Deep Learning 89.7 90.3 87.4 88.8

Random

Forest

Ensemble Learning 86.2 85.5 82.3 83.8

GradientB

oosting

Ensemble Learning 87.5 88.2 84.7 86.4

To facilitate generalization across climatic and

hazard types, the model is trained and validated at

multiple locations corresponding to areas in the world

susceptible to different types of environmental risks

(e.g., floods, droughts, and cyclones). Cross-

validation methods and hyperparameter tuning are

used to minimize model-related performance metrics

e.g., accuracy, recall, F1-score, and mean absolute

error. Furthermore, the model utilises explainable AI

techniques, such as SHAP (SHapley Additive

exPlanations) and LIME (Local Interpretable Model-

agnostic Explanations), to achieve transparency in

results, which enables stakeholders and decision-

makers to easily interpret underlying factors of each

prediction.

The final step is to apply this trained model to a

monitoring and forecasting system. The table 3 shows

the Machine Learning Models Used and Performance

Metrics. Embedded into cloud infrastructure, it

supports online data ingest and dynamic model

update.

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Figure 2: Climate Feature Correlation Heatmap.

Warning mechanisms are implemented in order to

inform potential users about forthcoming

environmental hazards according to predefined thres-

holds and probability values. The figure 2 shows the

Climate Feature Correlation Heatmap A feedback

mechanism is built in the proposed system to add

new data and user feedback, allowing the system for

adaptive learning of predicting performance.

Figure 3: Model Accuracy Comparison.

Overall, this methodology provides a scalable,

explainable, and data-driven solution to climate

modeling, capable of forecasting multi-risk

environmental hazards and supporting timely,

informed decision-making for climate adaptation and

disaster management efforts.

5 RESULT AND DISCUSSION

The application of our big data-driven predictive

analytics framework resulted in remarkable enhanced

accuracy and reliability of forecast environmental

risk than conventional climate models achieved. A

combination of real-time IoT sensor-generated

streams, the satellite-based observatory datasets and

historical meteorological datasets allowed the engine

to show its ability to detect patterns and predict the

anomalies in climatic trends such as floods, droughts,

and extreme weather temperature across several

geographical scales.

The LSTM and CNN hybrid model obtained a

significant classification accuracy rate over 91% on

multi-class classification of climate risks in

experimental evaluation. This is a dramatic

improvement over conventional statistical models

that tend to level off at less than 80% accuracy

because they cannot include complex nonlinear

interactions among climate data. Such capability of

the LSTM to bite into a long sequence of temporal

data lead to successful forecast of delayed or

seasonal patterns, while CNN was responsible for

spatial detection (especially in satellite imagery

examination) that led to the system melting down

areas of interest into a finer grid. Moreover, ensemble

methods, such as GBM, helped in minimizing the bias

and variance, and overall robustness of the model.

Regarding computation time, the parallel

processing functionality of Apache Spark

significantly decreases the time for data pre-

processing and model training, due to its distributed

data handling. Work that had previously taken hours

would instead be getting done in minutes,

demonstrating the system was fast enough for near

real-time forecasting. This was important for issuing

early environmental warnings, especially in cases

where such warning would prevent harm and save

vulnerable populations. The scalability of the

framework was also evaluated by simulating high

data volume streams and the system was found to be

stable and capable of providing predictions under the

additional load, proving its potential to be

implemented into large-scale climate monitoring

solutions.

Regionally, model validation was also performed

by comparing to data from three climatically distinct

zones; a salt marsh prone coastal zone, an arid

drought sensitive zone, and a heat wave impacted

temperate region. The baseline models were

consistently outperformed by our framework in all

regions. In flood prediction for example the model

obtained 94% precision and 91% recall, thus

A Uniﬁed Big Data and AI-Driven Predictive Framework for Multi-Risk Climate Pattern Modeling and Environmental Hazard Forecasting

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decreasing the number of false positives able to raise

an unnecessary alarm. In drought forecasting, it

could be useful to fill the gap of the long period by

the ability to early detect with DSSs which water

stress indicator (for the Mediterranean, up to 4 weeks

before) using the trend of Rao (1987) index with

anomalies of the past cumulated rainfall and relative

amount of loss for each depth of soil moisture.

Figure 4: Environmental Risk Prediction by Region.

The interpretability of the system was one of most

important results of the framework. Explanations

were provided for each prediction using SHAP

values and LIME visualizations. the figure 4 shows

the Environmental Risk Prediction by Region. This

played a massive role in establishing trust in domain

experts and policymakers as the model was able to

explain predictions on the basis of contributing

factors, like lower rainfall, rate of high temperature

spikes, low vegetation indices, and so on. These

findings not only improved interpretation, but also

offered tangible knowledge that might be applicable

for planning of mitigation activities, resource

allocation, and updating of regional climate policies.

The adaptability of the framework was also tested

with the retraining using recent data, and finally it

was found that the model indeed had a learn- ing

capability and responsiveness to the changing

environmental patterns. The table 4 shows the

Environmental Events Predicted by the Framework.

This flexibility is particularly important in the face of

climate change, in which fixed models rapidly

become outdated in light of changing baseline

conditions. The feedback loop provided an integrated

capability for the system to learn to increase its

accuracy through repeated exposure to inputs of new

data and events.

Table 4: Environmental Events Predicted by the Framework.

Event Type Region Tested

Lead Time

(hrs)

Prediction Confidence

(%)

False Alarm Rate (%)

Flood

Coastal – Tamil

Nadu

72 93.2 4.1

Drought Rajasthan, India 168 90.5 5.6

Heatwave Central Europe 48 92.0 3.3

Cold Wave Northern Canada 36 88.7 6.4

Cyclone

Warning

Bay of Bengal 96 91.8 4.8

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Figure 5: Shap Values for Key Climate Features.

In spite of these promising results, some

limitations were found. The performance of the

system could be influenced due to lack of data in the

distant areas with poor sensor coverage. The figure 5

shows the SHAP Values for Key Climate Features.

Furthermore, the quality of satellite images on

cloudy days influenced the precision of CNN-based

spatial predictions. The table 5 shows the

Explainable AI Insights from SHAP and LIME These

deficits highlight the need for continued data

improvements and infrastructure investment that

enables wide-spread environmental surveillance.

However, the collective results confirm the

effectiveness of the proposed framework to model

the climate change patterns and predict the

environmental risks in a scalable, accurate, and

explainable way.

Table 5: Explainable Ai Insights from Shap and Lime.

Event Type Top Feature (SHAP) Contribution (%) LIME Explanation Result

Flood Rainfall Level 45.2

High rainfall linked to

low-pressure zones

Drought Soil Moisture Index 49.1

Low moisture triggers

water stress

atterns

Heatwave Land Surface Temp 42.7

Sharp rise in surface temp

indicates ris

Cold Wave Air Pressure Drop 38.9

Rapid pressure drops

recede event

Cyclone Wind Speed + Pressure 44.5

Combined anomalies

initiate cyclone path

It links the general knowledge of theoretical

climate science with practical decision making, and is

a powerful resource for governments, disaster

management organizations, and climate-focused

organizations. The figure 6 shows the Real-Time

Forecast Dashboard Snapshot (Simulated). By

providing the alerting mechanism for real-time and

explainable insights, our framework is well adapted

for aggressive disaster management, planning

climate adaptation, and sustaining infrastructure.

In summary, this study shows that combining big

data and AI approaches can achieve significant

advances for climate prediction systems. Able to

predict and interpret at scale, this approach raises the

bar for environmental analytics, paving the way for

new forms of intelligent climate resilience.

Figure 6: Real-Time Forecast Dashboard Snapshot

(Simulated).

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369

6 CONCLUSIONS

This work offers a rare example of a well-rounded

and smart climate modelling and hazard prediction

using big data analytics and AI. The proposed

framework demonstrates great improvements in

comparison to conventional models, providing a

scalable and flexible interpretation of simulated and

multi-sourced observed climate data. With the help of

state-of-the-art machine learning tools like LSTM

and CNNs, the model is able to capture patterns, make

predictions of several environmental hazards, and

provide early warning system outputs with high

reliability. Built with real-time performance and

explainable AI capabilities, the platform increases

transparency and informed decision-making for

stakeholders responsible for climate resilience,

disaster response, and policy planning. The consistent

field performance of this system in various climatic

zones also indicates its generality for global

application. With climate change generating shifting

risks and threats, this study offers a forward-thinking

remedy that does more than build frontiers against

floods but also provides actionable intelligence for

communities and governments to adapt, respond, and

create a more resilient world.

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