Climate Change Prediction and Analysis Using Machine Learning

E. Sindhuja, M. Shaheda Begum, U. Shanthi, E. Neha and Y. Madhurima

Department of Computer Science and Engineering, Ravindra College of Engineering for Women, Kurnool, Andhra

Pradesh, India

Keywords: Climate Change, Machine Learning, Extreme Weather, Data Analysis, AI‑Physics Models.

Abstract: Climate change poses a significant threat to ecosystems, economies, and human societies, necessitating

accurate prediction and analysis for effective mitigation and adaptation strategies. Machine Learning (ML)

has emerged as a powerful tool in climate science, offering enhanced predictive capabilities by analyzing vast

datasets and identifying complex patterns in climate-related variables. This paper explores the application of

ML techniques, including Deep Neural Networks (DNNs), Support Vector Machines (SVMs), and Long

Short-Term Memory (LSTM) networks, in areas such as climate modeling, extreme weather event prediction,

carbon emission monitoring, and drought forecasting. Additionally, we discuss the challenges associated with

data quality, computational requirements, and model interpretability. Future directions emphasize the

integration of hybrid AI-physics models, real-time climate monitoring, and AI-driven policy

recommendations to enhance climate resilience.

1 INTRODUCTION

Climate change is one of the most pressing global

challenges, with far-reaching consequences on the

environment, economy, and human livelihoods.

Some clear indications of climate change are the

rising temperature of the planet, the increase in the

frequency of extreme weather events, melting of ice

caps, and the shifting precipitation patterns. Accurate

prediction and analysis of these changes will inform

mitigation strategies, improve preparedness for

disasters, and facilitate effective policies. Traditional

climate models, or General Circulation Models

(GCMs), make use of physics-based simulations to

predict climate patterns. But this has always been

impeded by computational costs, uncertainty with

estimates of parameters, and interactions in the

climate that are nonlinear and too complex. On the

other hand, Machine Learning (ML) is proving a

game-changer for climate change-funded prediction

and analysis due to its capability to engulf large

amount of environmental data, pattern recognition

and gave improved predictive skill. Different sorts of

Machine Learning approaches are DNNs, SVMs,

LSTM networks that analyse historical climate data,

identify patterns, and generate real-time situations.

Data net, the large language model for the climate

used as the basis of this research, has been utilized in

a number of applications from temperature and

precipitation forecasting to extreme weather event

prediction, carbon emission monitoring, and water

resource management.

From this papers perspective, we explore the use

of ML to predicting climate change, its potential

uses, benefits, barriers. Data will continue to play an

important role in climate science, and similar to

recent advances in fusion research where ML has

been integrated with traditional physics-based

models, we argue how ML can also contribute to

climate prediction accuracy and contributing to data-

driven climate-specific policy development. Using

ML for climate analysis provides researchers and

policy-makers with a better understanding of

Environmental changes, helping to take measures to

mitigate climate risks and build resilience against

future uncertainties.

2 RESEARCH METHODOLOGY

2.1 Research Area

This study utilizes a systematic review research

methodology to analyse the influence of Machine

Learning (ML) on climate change prediction and

analysis. Here is a breakdown of the process: Data

Sindhuja, E., Begum, M. S., Shanthi, U., Neha, E. and Madhurima, Y.

Climate Change Prediction and Analysis Using Machine Learning.

DOI: 10.5220/0013899100004919

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 3, pages

401-406

ISBN: 978-989-758-777-1

401

Collection, Data Pre-processing, Model Selection,

Algorithm Implementation, and Model Evaluation.

2.1.1 Data Collection

• And some of the climate data used from

NASA, NOAA (National Oceanic and

Atmospheric Administration), IPCC

(Intergovernmental Panel on Climate

Change) and ECMWF (European Centre for

Medium-Range Weather Forecasts).

• The datasets cover temperature records,

precipitation patterns, CO₂ emissions, sea

level rise, extreme weather events.

• Even real-time environmental monitoring

using remote sensing data collected from

satellites and IoT sensor networks.

2.1.2 Data Pre-Processing

• A lesson in Data Preparation: Deal with

missing values, noise, and inconsistencies

in climate datasets.

• Feature Scaling and Normalization: Making

sure we have the same scale for machine

learning models.

• Dimensionality Reduction: Techniques like

Principal Component Analysis (PCA) and

Autoencoders are utilized to identify

relevant features.

2.1.3 Model Selection and Implementation

• Different ML models tested for climate

change prediction are as follows:

• Supervised Learning: Decision Trees, SVM,

Random Forests

• Deep Learning: CNNs for has been used in

image-based analysis of climate and LSTMs

for time-series forecasting

• Unsupervised Learning– K-Means

Clustering to classify climate zone

• Hybrid AI-Physics Models: Integrating ML

with classical physics-based climate models

2.1.4 Model Training and Evaluation

• these models are trained with historical

climate datasets and cross-validated.

• To evaluate the accuracy of the model,

performance metrics like Mean Squared

Error (MSE), R-Squared (R²), and Root

Mean Square Error (RMSE) are used.

• The models are further tested for robustness

against real-time predictions and actual

climate data.

2.1.5 Interpretation and Policy Implications

• The analysis will yield beneficial knowledge

regarding the climate patterns.

• We configure AI-informed climate

predictions into policy recommendations.

2.2 Research Area

This research mainly targets different fields of

climate science where ML can make prediction and

analysis better. Key research areas include:

2.2.1 Climate Change Prediction and

Modeling

• Application of ML in temperature,

precipitation, and CO₂ emission forecasting.

• Enhancing General Circulation Models

(GCMs) using AI techniques.

2.2.2 Extreme Weather Event Prediction

• Using ML to forecast hurricanes, floods,

heatwaves, and droughts.

• Real-time monitoring for disaster

preparedness and risk assessment.

2.2.3 Air Quality and Carbon Emission

Monitoring

• ML-driven satellite image analysis for

tracking air pollution.

• Predicting trends in greenhouse gas

emissions for regulatory policies.

2.2.4 Water Resource Management and

Drought Prediction

• AI models for optimizing irrigation planning

and water conservation.

• Predicting long-term drought patterns based

on climate variables.

2.2.5 Ice Sheet Melting and Sea Level Rise

Estimation

• Deep Learning models for glacier and polar

ice cap monitoring.

• Assessing future coastal flooding risks due

to rising sea levels.

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2.2.6 Integration of ML with IoT and

Remote Sensing

• Using IoT-based sensor networks for real-

time climate tracking.

• Combining ML with satellite imagery and

GIS (Geographic Information Systems) for

enhanced spatial climate analysis.

3 LITERATURE REVIEW

3.1 Machine Learning for Weather and

Climate Prediction

Author(s): Hamza Hassani, Sarah E. Greene,

James D. Murphy.

Abstract: The use of Machine Learning (ML)

techniques in forecasting weather and climate is

discussed in this paper. It covers different models,

including deep neural networks, decision trees, and

ensemble learning, to predict temperature,

precipitation, and critical weather events. It indicates

that this method combines ML and physics-based

models to improve climate simulations, while citing

challenges like data quality, high computational

requirements, and model interpretability.

3.2 AI-Driven Climate Models:

Improving Accuracy in Climate

Change Projections

Author(s): Michael R. Thompson, Linda K. Jones.

Abstract: Artificial Intelligence in Climate Modeling:

A Deep Learning Approach to Climate Modeling

Abstract. Authors show using historical climate data

to train neural networks outperformed older models

based on simulations. It also highlights hybrid AI-

physics models' potential to help narrow climate

projections.

3.3 Deep Learning for Drought

Prediction Using Remote Sensing

Data

Author(s): Daniel W. Carter, Emily B. Shaw.

Abstract: This paper analyses the application of deep

learning models for drought prediction in particular

deep learning models, namely convolutional neural

networks (CNNs) and recurrent neural networks

(RNNs). It uses remotely sensed satellite data to

calculate soil moisture, accumulated rainfall and

vegetation indices. The findings show that AI-based

drought models outperform the accuracy of

statistical models.

3.4 Predicting Extreme Weather

Events Using Machine Learning

Techniques

Author(s): Kevin A. Roberts, Sophia M. Lee.

Abstract: This research explores the application of

supervised learning methods, such as Support Vector

Machines (SVM) and Random Forests, for predicting

hurricanes, floods, and heatwaves. The study uses

meteorological data and highlights how machine

learning can improve early warning systems for

disaster preparedness.

3.5 Machine Learning Applications in

Air Pollution Forecasting

Author(s): John P. Reynolds, Maria D. Torres.

Abstract: This paper presents a comprehensive

review of ML techniques used to predict air pollution

levels and track greenhouse gas emissions. It

evaluates various approaches, including regression

models, deep learning, and reinforcement learning, to

enhance the accuracy of pollution forecasts. The

study also discusses the implications of ML-based air

quality monitoring for environmental policy-making.

4 EXISTING SYSTEM

The traditional methods for predicting and analyzing

climate change depend upon physics-based climate

models, statistical forecasting, and empirical data

analysis. Some of them have been in use for decades;

however, they lag behind in terms of accuracy,

computation efficiency, and adaptability to real-time

environmental changes.

Existing climate prediction systems are primarily

based on General Circulation Models (GCMs), which

simulate atmospheric and oceanic processes

according to mathematical equations. These models

encompass considerations such as greenhouse gas

emissions, land surface changes, and solar radiation,

Climate Change Prediction and Analysis Using Machine Learning

403

and are used for predicting future trends of the

climate. However, in actual practice, they are

computationally very expensive and additionally

have difficulty performing localized predictions,

especially for extreme cases.

Statistical climate modeling is another common

approach in which regression-based methods

examine historical climate variability and forecast

future climate trends. The models provide insights,

but they typically ignore non-linear relationships and

complex interactions between different climate

variables.

Climate parameters, including temperature,

humidity, and sea level changes, are monitored

through remote sensing technologies and satellite

data to by meteorological organizations. And even

though these systems do provide high-resolution data,

interpretation is performed using manual analysis,

which takes an order of magnitude more time and is

also susceptible to a manifestation of human error.

In addition, the Numerical Weather Prediction

(NWP) models are physics-based simulation used in

traditional weather forecast models for short-term

weather conditions. However, these models are less

accurate for long-term climate change predictions,

due to uncertainties in initial conditions as well as

chaotic atmospheric behaviour.

The past prediction systems of climate are

sufficient to portray the climate changes but they are

unable to adapt, not real time processing and also

unable to handle large datasets. These challenges lead

to Machine Learning-based approaches that can help

in achieving better accuracy, improved computational

efficiency, and dynamic prediction resorting to

updated climate data.

5 PROPOSED SYSTEM

We present a methodology which implements this

using Machine Learning (ML) and Artificial

Intelligence (AI) to enable exploration of

climate

change patterns in new and innovative ways. ML-

based have a remarkable advantage over traditional

models, as they can accept an enormous amount

environmental data and uncover complex patterns,

further increasing accuracy P The system uses deep

learning techniques, neural networks, and real-time

data analysis to achieve accurate, adaptive climate

prediction.

Table 1 provides an overview of the various

components and operations that make up the

proposed system which include data collection, pre-

processing, feature extraction, model training and

real-time analysis. To train the predictive model, it

uses satellite imagery, remote sensing data, data from

meteorological sensors and historical datasets from

the climate ourselves. We employ state of the art

Deep Learning techniques such as CNNs, RNNs,

and LSTM networks to capture spatial and temporal

climate patterns.

For a better prognosis regarding the outcome of

climate modeling, it will have to implement hybrid

AI-Physics designs that combine AI with

conventional models for climate. These hybrid

models serve to eliminate the uncertainties that have

historically been linked with weather prediction

models and can be adjusted according to real-time

environmental changes. Ensemble Learning methods

(Random forest, Gradient boosting, XG Boost)

improve the robustness of the models by getting

multiple predictions and obtaining a more confident

result.

Architect.

The other aspect of the proposed system is its ability

to predict extreme weather such as hurricanes,

floods, and drought. Using reinforcement learning

and anomaly detection algorithms, it recognizes early

indicators of potential climate disasters, enabling

people to take preventative steps to prepare for and

mitigate disasters.

There is additionally the T system, which aspires

toward cloud-based deployment and IoT and real-

time climate monitoring based on constant analysis

of the data stream from IoT weather stations as well

as from satellite feeds to inform about climate

fluctuations in a timely manner. Results are displayed

using interactive panels and GIS-based mapping,

enabling policymakers and environmental

researchers to make informed decisions. Figure 1

show the Architect.

The proposed system suggests an approach which

overcomes limitations of traditional climate models

by providing higher accuracy, real-time adaptability,

and better computational efficiency. Through the use

of AI-based climate analytics, the system engages in

sustainable environment management, climate

resilience planning, and disaster risk reduction.

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Figure 1: Architect.

6 CONCLUSIONS

We focused on usage of machine learning (ML) and

artificial Intelligence (AI) in prediction and analysis

of climate change in this work. While conventional

models like General Circulation Models (GCMs) and

Numerical Weather Prediction (NWP) have

contributed significantly to our understanding of

climate systems, they are resource-intensive, time-

consuming, and face challenges in modeling non-

linear climate dynamics. The evidence-based ML

systems we propose will enhance the accuracy and

efficiency of climate predictions through integration

of real-time processing of inputs from the

hippopotamus AI-physics models developed using

two deep learning algorithms.

The innovative use of Neural Networks (CNNs,

RNNs and LSTMs), Ensemble Learning (Random

Forest, XGBoost) and Reinforcement Learning

enables the mentioned system to accurately identify

climate trends, extreme weather patterns and identify

anomalies in environmental data. Real-time

monitoring and predictive analysis through the

amalgamation of IoT, satellite imagery and cloud

computing allow for faster response to climate risks,

and better decision-making at the political and

environmental research level.

These results show that using AI in climate

models improves forecasting accuracy, works with

less computational overhead, and produces adaptive

climate insights. These developments support

sustainable environmental planning, disaster

preparedness, and international climate resilience

initiatives. With climate change being one of the most

pressing issues around, more work can be done on

improving AI models, data integration, and applying

the platform to carbon footprint, renewable energy,

and climate mitigation solutions identification as

well.

To sum up, Machine Learning revolutionizes the

practice of climate science by offering alternative

solutions to combat climate variability, predicting

extreme weather conditions, and changes in the long-

run environment. The ultimately proposed system can

be seen as a powerful, scalable framework for climate

prediction and analysis that enables a more

sustainable and data-driven approach to addressing

climate change.

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