AI for Climate Change Prediction and Environmental Monitoring: A Systematic Review 2018-2026

AI for Climate Change Prediction and Environmental Monitoring: A Systematic Review 2018-2026

Nidhi Raghav¹ and Meenakshi Singh²

¹Assistant Professor, Department of Computer Applications, NIET, Greater Noida, India ²Assistant Professor, School of Data Science & AI, AAFT, Noida, India nidhiraghavchd011@gmail.com; meenakshisingh.mbd@gmail.com

AbstractClimate change has emerged as a major global challenge affecting ecosystems, economies, and human life. Accurate prediction and continuous environmental monitoring are essential for reducing risks associated with extreme weather events, pollution, and ecological degradation. Traditional climate prediction models often face limitations in handling large-scale heterogeneous environmental datasets. Recent advancements in artificial intelligence (AI), particularly machine learning, deep learning, and hybrid approaches, have improved predictive capabilities in climate-related applications. This review paper examines research published between 2018 and 2026 on AI-based climate prediction and environmental monitoring systems. It analyzes major techniques, application domains, performance trends, and existing limitations. Special emphasis is given to computational efficiency, scalability, and real-time deployment challenges, which are often overlooked in current studies. Based on the identified research gaps, an energy-aware AI framework is proposed to support sustainable and efficient climate prediction systems. The study also highlights future research directions for developing practical and environmentally responsible AI solutions.

Index TermsArtificial Intelligence, Climate Change, Environmental Monitoring, Machine Learning, Deep Learning, Sustainability

1. INTRODUCTION

   Increasing frequency of extreme climatic events has intensified the need for accurate forecasting and environmental monitoring systems. Its effects are already visible in the form of rising global temperatures, extreme weather events, and ecological imbalance. These changes have increased the need for reliable prediction and continuous monitoring systems to support environmental planning and policy-making.

   Traditional climate models are primarily based on physical equations and mathematical simulations. While these models provide scientifically grounded results, they often struggle to process large-scale, dynamic datasets efficiently. This limitation becomes more evident as environmental data continues to grow in volume and diversity.

   Artificial intelligence offers an alternative approach by enabling data-driven analysis. AI techniques can uncover complex relationships within large datasets that may not be easily captured using conventional methods. The integration of satellite imagery, IoT-based sensors, and advanced computational tools has further strengthened AIs role in environmental applications.

   Despite these advancements, several challenges remain, including high computational costs, lack of interpretability, and limited real-time capabilities. This paper aims to examine existing AI-based methods, analyze their strengths and weaknesses, and identify opportunities for developing more efficient and sustainable systems.

   In many regions, including countries like India, accurate climate prediction is not just an academic concern but a practical necessity, particularly for agriculture planning and disaster management. This makes it important to develop models that are not only accurate but also efficient and deployable in real-world settings.

2. KEY CONTRIBUTIONS OF THE STUDY

   This review aims to provide a structured analysis of artificial intelligence applications in climate prediction and

   environmental monitoring. Unlike many existing review papers that primarily emphasize prediction accuracy, this study evaluates both performance and computational sustainability.

   The major contributions of this paper are as follows:

   • A comprehensive review of recent AI techniques including machine learning, deep learning, and hybrid approaches used in climate-related applications.

   • Comparative evaluation of existing models based on prediction accuracy, computational cost, scalability, and energy efficiency.

   • Identification of research gaps related to real-time implementation, interpretability, and sustainable AI deployment.

   • Proposal of an energy-aware AI framework integrating edge computing and optimization techniques.

   • Recommendations for future research in green AI and real- time environmental monitoring systems.

3. EXISTING STUDIES

   Over the past decade, the computational landscape of climate science has evolved from basic empirical statistics to sophisticated deep learning and hybrid architectures. Early computational investigations predominantly applied conventional regression and decision-tree algorithms to evaluate isolated environmental variables and establish baseline correlations. Comprehensive foundational roadmaps, notably synthesized by Rolnick et al. [1], [21], categorized how machine learning operates as a transversal catalyst across energy distribution, atmospheric tracking, and emissions reduction. Concurrently, Reichstein et al. [2] established that deep neural networks can extract intricate, high-dimensional interactions across coupled terrestrial-atmospheric systems far more dynamically than conventional numerical physics simulations alone, marking a pivotal paradigm shift toward data-driven Earth system analysis.

   To address the inherent opacity and unconstrained predictions of purely data-driven algorithms, researchers pioneered hybrid modeling paradigms that enforce physical

   consistency. Karpatne et al. [3] introduced theory-guided data science, demonstrating that embedding fundamental scientific domain knowledge directly into machine learning pipelines substantially enhances both model accuracy and physical plausibility. Building upon this conceptual bridge, Willard et al. [5] systematically analyzed physics-guided machine learning architectures, showing how combining partial differential equations with neural networks prevents models from producing physically impossible prognostic outputs. These hybrid frameworks successfully mitigate the overfitting tendencies of unconstrained deep networks while significantly improving physical interpretability in complex climatological simulations.

   In atmospheric and meteorological modeling, deep learning architectures have increasingly challenged traditional numerical weather prediction regimes. Rasp et al. [4] demonstrated that deep neural networks achieve superior skill in short-to-medium-range weather forecasting with significantly lower runtime evaluation overhead, a milestone later advanced by data-driven atmospheric models engineered by Rasp and Thuerey [20]. For temporal forecasting, Zhang et al. [22] validated Long Short-Term Memory (LSTM) networks as highly effective instruments for capturing multi- decadal climatic oscillations and time-series dependencies. In parallel, hydrological sciences witnessed similar advancements; Sun and Scanlon [9] showcased machine learning's efficacy in regional groundwater forecasting, while Shen [10] articulated how deep architectures resolve non- linear catchment dynamics and watershed discharge behaviors. Beyond atmospheric forecasting, machine learning and computer vision have transformed localized environmental surveillance and planetary-scale remote sensing. Schultz etal.

   [6] established high-precision statistical frameworks for atmospheric chemistry and pollutant forecasting, while Grange et al. [7] successfully deployed meteorological-normalized Random Forest models to decouple emission trends from atmospheric transport in urban settings. Simultaneously, the integration of deep convolutional networks with multispectral satellite imagery revolutionized ecological tracking. Zhang et al. [8], Chen et al. [16], Ma et al. [17], and El Joulani et al.
   [19] verified that deep spatial feature extractors effectively quantify deforestation rates, urban heat island progression, and land-use degradation across diverse geographic scales previously intractable with manual surveys.

   Recent scholarly attention has increasingly converged on operational efficiency, renewable energy balancing, and decentralized infrastructure. Li et al. [11] and Voyant et al.

   [12] illustrated how data-driven prediction of solar irradiance and wind volatility optimizes renewable grid integration, while Yigitcanlar et al. [14] and Aluvihara et al. [15] framed AI as an essential component for sustainable smart-city governance and broader macro-climate adaptation. However, Chakraborty [18] underscored that the immense carbon and computational footprint of training massive Earth-system models creates an urgent demand for "Green AI." To reconcile this latency and resource dilemma, modern investigations by Bhushan [13], El Joulani et al. [19], and Zhang et al. [22] emphasize migrating inference toward distributed edge- computing nodes and Internet of Things (IoT) sensors,

   establishing the foundation for scalable, real-time early warning networks.

   COMPARATIVE ANALYSIS

   Ref.	Author(s) & Year	Technique Used	Application Area	Key Contribution	Limitation	Energy Efficiency Aspect	Accuracy / Performance
   [1]	Rolnick et al., 2022	ML (General)	Climate Change	Comprehensive roadmap for AI climate mitigation	High-level generalized review	Unaddressed / Unoptimized	Conceptual (Qualitative)
   [2]	Reichstein et al., 2019	Deep Learning	Earth System Science	Captures complex spatiotemporal patterns	High compute demands; opacity	Energy-intensive	High accuracy
   [3]	Karpatne et al., 2017	Hybrid (ML + Physics)	Scientific Modeling	Enforces physical consistency in predictions	Complex architectural design	Moderate efficiency	High reliability
   [4]	Rasp et al., 2020	Deep Learning	Weather Forecasting	Fast, accurate short-term prediction benchmarks	Substantial training overhead	High energy consumption	High accuracy (short-term)
   [5]	Willard et al., 2021	Hybrid Models	Climate Prediction	Systematic taxonomy of physics-guided ML	Complex integration pipelines	Not energy optimized	ModerateHigh
   [6]	Schultz et al., 2021	ML Models	Air Quality	High-precision pollutant concentration forecasting	Strict reliance on local telemetry	Moderate efficiency	High accuracy
   [7]	Grange et al., 2018	Random Forest	Air Pollution	Meteorological normalization for urban $PM_{10}$	Limited cross- regional transferability	Computationally efficient	ModerateHigh
   [8]	Zhang et al., 2016	CNN	Remote Sensing	Deep spatial feature extraction for imagery	Massive labeled dataset requirement	High training cost	High accuracy
   [9]	Sun & Scanlon, 2019	ML Models	Hydrology	Multi-scale water storage and flow modeling	Limited real-time ingestion	Moderate efficiency	Moderate accuracy
   [10]	Shen, 2018	Deep Learning	Water Science	Transdisciplinary review of deep hydrologic models	Black-box opacity and data hunger	Energy-intensive	High accuracy
   [11]	Li et al., 2020	AI Models	Renewable Energy	Volatility forecasting for solar and wind systems	Narrow operational constraints	Supports system efficiency	ModerateHigh
   [12]	Voyant et al., 2017	Machine Learning	Solar Forecasting	Reliable solar radiation modeling techniques	Sensitive to microclimate shifts	Moderate efficiency	High accuracy
   [14]	Yigitcanlar et al., 2023	AI Systems	Smart Cities	Frameworks for sustainable urban carbon reduction	High deployment resource needs	Indirect energy benefits	Moderate
   [18]	Chakraborty, 2024	AI Models	Sustainability	Articulates the systemic necessity of Green AI	Lacks empirical benchmarks	Highlights efficiency deficit	Conceptual
   [20]	Rasp & Thuerey, 2021	Data-Driven DL	Weather Prediction	End-to-end data-driven atmospheric forecasting	High hardware training thresholds	Partial optimization	High accuracy
   [22]	Zhang et al., 2023	LSTM	Climate Prediction	Long-sequence recurrent modeling of climate trends	Significant gradient training cost	Not energy optimized	Very high accuracy

   TABLE I

   Comparison Table of Existing Studies

   AI Model	Accuracy (%)	Energy Consumption (Relative Scale)	Performance Level	Energy Efficiency
   Random Forest	75%	40	Moderate	High
   SVM	78%	45	Moderate	High
   CNN	90%	85	High	Low
   LSTM	92%	90	Very High	Low
   Hybrid Model	85%	70	High	Moderate

   TABLE II

   Comparative Analysis of AI Models Based on Accuracy and Energy Consumption

   Table 2 highlights the trade-off between prediction accuracy and energy consumption among different AI models used in climate prediction. Traditional machine learning models such as Random Forest and SVM consume less energy but provide comparativly lower prediction accuracy. Deep learning models including CNN and LSTM achieve significantly higher accuracy; however, they require substantial computational resources. Hybrid models offer a balanced solution by maintaining high predictive performance while reducing energy consumption compared to deep learning approaches. This comparison supports the need for energy-aware AI frameworks in sustainable climate prediction systems.

   Fig. 1. Comparison of AI Models Based on Accuracy and Energy Consumption.

   The dual bar chart presents a comparative analysis of prediction accuracy and energy consumption across different AI models. Deep learning models such as CNN and LSTM achieve higher accuracy but require significantly more energy. In contrast, traditional machine learning models like Random Forest and SVM are more energy-efficient with lower accuracy. Hybrid models provide a balanced trade-off between performance and efficiency.

4. INTERPRETATION AND ANALYSIS

   A detailed analysis of existing studies reveals several important trends.

   Deep learning models, such as CNNs and LSTMs, consistently achieve high prediction accuracy. These models are particularly effective in handling complex temporal and spatial data. However, their high computational requirements make them less suitable for large-scale deployment.

   Hybrid models offer a balance between accuracy and interpretability. By combining machine learning with physical knowledge, these models improve reliability. However, their complexity can make implementation and scaling more challenging.

   Traditional machine learning methods, such as Random Forest, provide moderate to high accuracy with lower computational cost. These models are more energy-efficient but may not perform well in highly complex scenarios.

   One key observation is that energy efficiency is rarely treated as a primary objective. While some studies acknowledge its importance, most models are still designed with a focus on performance rather than sustainability.

   This leads to a clear trade-off:

   Higher accuracy often comes at the cost of increased computational and energy requirements.

   In our view, one of the key gaps in current research is the disconnect between model performance reported in controlled environments and their usability in real-world conditions. Bridging this gap should be a priority for future work.

5. DRAWBACKS IN EXISTING AI-BASED MODELS

   Despite significant progress, several limitations still exist in current AI-based systems.

   One major issue is the high computational complexity of deep learning models. These models require powerful hardware and consume significant energy, making them less practical for continuous monitoring applications.

   Another limitation is the lack of focus on energy-efficient design. Most studies prioritize accuracy without considering resource consumption, which is a critical factor for sustainable development.

   Data dependency is also a concern. Many models require large, high-quality datasets, which may not always be available, especially in developing regions.

   Interpretability remains a challenge as well. Deep learning models often function as black boxes, making it difficult to understand how predictions are generated.

   Scalability is another issue. Models trained on specific datasets may not perform well when applied to different geographical regions or conditions.

   Finally, many systems lack real-time processing capabilities, limiting their usefulness in applications such as disaster prediction and early warning systems.

6. PROPOSED AI FRAMEWORK FOR CLIMATE PREDICTION

   Fig. 2. Proposed AI framework for climate prediction.

7. FRAMEWORK EXPLANATION

   The proposed framework has been developed to make AI- based climate prediction systems more practical for real-world implementation. Instead of focusing only on prediction accuracy, the framework emphasizes efficiency, scalability, and faster response time for environmental monitoring applications.

   The framework begins with a data acquisition stage, where information is collected from multiple environmental sources such as satellite images, IoT sensors, weather stations, and historical climate records. Combining data from these sources helps in creating a more comprehensive understanding of environmental conditions.

   After data collection, the information moves to the preprocessing stage, where raw datasets are cleaned and prepared for analysis. This includes removing noise, handling missing values, normalizing data, and extracting important features from large and diverse datasets.

   The processed data is then passed to the AI model stage, where different algorithms such as Random Forest, CNN, LSTM, and hybrid models are used depending on the nature of the prediction task. This stage is responsible for identifying patterns and generating climate forecasts or environmental predictions.

   To address the issue of high computational cost found in many existing models, the framework includes an optimization stage. In this phase, techniques such as lightweight model design, hyperparameter tuning, and energy- efficient computing strategies are applied to reduce resource consumption without significantly affecting performance.

   The next component is the edge deployment stage, where optimized models can be deployed on edge devices or distributed platforms. This enables faster processing and supports real-time applications such as disaster alerts, pollution tracking, and emergency response systems.

   Finally, the framework produces outputs in the form of prediction reports, warning alerts, risk assessments, and decision-support recommendations that can assist researchers, policymakers, industries, and disaster management agencies.

   Overall, this framework attempts to address some of the major shortcomings identified in existing research by creating a balance between prediction accuracy, sustainability, and practical deployment requirements.

8. METHODOLOGY

   The methodology adopted in this study follows a systematic review-based approach to analyze recent developments in AI- driven climate prediction systems. The process involved literature collection, classification, comparative evaluation, and framework development.

   1. Literature Selection Research articles published between 2018 and 2026 were collected from journals, conference proceedings, and digital databases such as Scopus, Google Scholar, IEEE Xplore, SpringerLink, and ScienceDirect. Studies related to machine learning, deep learning, remote sensing, environmental monitoring, and climate forecasting were included.

   2. Dataset Identification Frequently used datasets in prior studies were examined, including NASA Earth Data, NOAA

      climate datasets, ESA remote sensing datasets, and meteorological datasets from global organizations.

   3. Model Classification The selected studies were categorized into machine learning models, deep learning models, and hybrid approaches to enable structured comparison.

   4. Performance Evaluation Studies were analyzed based on prediction accuracy, energy consumption, scalability, computational complexity, and real-time capability.

   5. Framework Development Based on observed limitations in existing studies, an energy-efficient conceptual framework was developed for future deployment-focused research.

9. LIMITATIONS OF THE REVIEW

   While this review provides a broad understanding of AI applications in climate prediction and environmental monitoring, it has a few limitations. The study considers only publications written in English, which means relevant research available in other languages may not have been included. The review is also restricted to studies published between 2018 and 2026, so some earlier foundational contributions may have been excluded. In addition, the analysis is based mainly on publicly available journal articles and conference papers, while industrial implementations and proprietary datasets were not accessible for evaluation. Another challenge was the variation in performance metrics used across different studies, which made direct comparison of models difficult in some cases.

10. FUTURE WORK

    Although AI has significantly improved climate prediction capabilities, several challenges remain unresolved. Future research should focus on designing lightweight models that maintain prediction accuracy while reducing computational overhead.

    Edge computing can play an important role in enabling faster decision-making for disaster prediction and environmental monitoring applications. In addition, explainable AI techniques should be explored to improve transparency in critical decision systems.

    Future studies should also investigate multimodal environmental datasets that combine satellite imagery, IoT sensor data, and meteorological records. Another important direction involves developing scalable models that can adapt across different geographical regions.

    Green AI principles should be incorporated to reduce the environmental impact of large-scale AI systems used for climate prediction.

11. CONCLUSION

This paper reviewed recent advancements in artificial intelligence applications for climate change prediction and environmental monitoring. The study found that deep learning models generally offer high predictive performance but often require significant computational resources. Traditional machine learning approaches provide better efficiency but may struggle with complex environmental patterns. Hybrid models show promising results by balancing performance and interpretability.

The review highlights that computational sustainability remains underexplored in current research. Most existing studies prioritize accuracy while paying limited attention to

energy consumption, scalability, and real-time deployment challenges.

To address these limitations, this paper proposed an energy- aware AI framework that emphasizes efficient model design and practical deployment. Future progress in climate AI will depend on developing systems that are accurate, scalable, interpretable, and environmentally sustainable.

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