Long-term Forecasts Research Articles

Anticipating prosperity: a systemic analysis of long-term economic trajectories

PurposeThis study introduces an innovative approach to long-term economic forecasting by integrating anticipatory governance (AG) and causal layered analysis methodologies. Focussing on emerging economies, with Vietnam as a case study, we explore potential economic trajectories to 2050.Design/methodology/approachThe research employs a mixed-method approach, combining quantitative economic projections with qualitative scenario building and analysis.FindingsOur findings reveal four distinct future scenarios, ranging from low growth to transformative change. The preferred scenario, characterized by adaptive change, projects a Gross National Income (GNI) per capita of $30,684 by 2050, with significant reductions in agricultural labour and improvements in human development indicators.Originality/valueThis study contributes to theoretical and practical domains by demonstrating the value of integrated foresight methodologies in economic planning. It offers policymakers a comprehensive framework for navigating complex, long-term economic challenges and opportunities. This research underscores the importance of adaptive governance and systemic thinking in achieving sustainable, inclusive economic growth in an increasingly uncertain global environment.

IMPLEMENTATION OF INTERNATIONAL EXPERIENCE OF STATE PROTECTION OF NATIONAL VALUES IN UKRAINE

The author of the article reveals the purpose of implementing the international experience of the system of state protection of national values through the prism of two important trends that should be implemented and and enshrined in the practice of state-building processes in modern Ukraine. One of them stipulates that national values are the central core of the national security system security system, they define the triad of ‘values-interests-goals’. The second trend defines the system of creating mechanisms for legislative support of guaranteeing and protecting of national values, which is embodied in the systemic nature of the state policy on national development and is the actual consolidation of the civilisational civilisational choice of the Ukrainian nation at the level of legal acts. The purpose of the article is to analyse practical aspects of implementation of the state mechanisms for protection of national values in foreign countries and their implementation in the Ukrainian State. The author emphasises that national values of of modern Ukraine should not be subject to political bargaining, speculation, etc. They are are always an element of political struggle. Without this, political processes are impossible, and therefore, national values should be defined and prohibited for political speculation on them, which, by the way, requires the development of an institution of for their criminal law protection; and - to have its own system of mechanisms of legislative protection. It is determined that the first step towards the implementation of the experience of foreign countries in protecting national values should be the introduction of an all-Ukrainian analytical study of the system of national values. It should should be based on long-term and medium-term forecasts of domestic development and the development of the system of world politics, its specific regional and functional subsystems. This means that the full potential of national research institutes in the field of public opinion, civil society, and sociological research should be used to society, and sociological research should be directed within the framework of a single state policy to study and define national values.

A review of load forecasting technology based on artificial intelligence

Abstract. Driven by the "dual carbon" goals and the construction of new-type power systems, the importance of power load forecasting technology has become increasingly prominent. Accurate power load forecasting can not only ensure the safe and stable operation of the power system, but also promote the efficient use of clean energy and reduce carbon emissions. Traditional power load forecasting methods have become insufficient in dealing with the complexity and nonlinear growth of power systems, and the development of artificial intelligence technology has brought new possibilities for power load forecasting. Power load forecasting methods based on machine learning and deep learning have gradually become a hot topic of research due to their powerful nonlinear mapping capabilities and advantages in processing big data. These methods not only perform well in short-term forecasting, but also show potential in medium- and long-term forecasting. This paper discusses the application of different artificial intelligence technologies in power load forecasting by sorting out the development status of related technologies, and provides a reference direction for future research.

Spatial-temporal graph neural networks for groundwater data.

This paper introduces a novel application of spatial-temporal graph neural networks (ST-GNNs) to predict groundwater levels. Groundwater level prediction is inherently complex, influenced by various hydrological, meteorological, and anthropogenic factors. Traditional prediction models often struggle with the nonlinearity and non-stationary characteristics of groundwater data. Our study leverages the capabilities of ST-GNNs to address these challenges in the Overbetuwe area, Netherlands. We utilize a comprehensive dataset encompassing 395 groundwater level time series and auxiliary data such as precipitation, evaporation, river stages, and pumping well data. The graph-based framework of our ST-GNN model facilitates the integration of spatial interconnectivity and temporal dynamics, capturing the complex interactions within the groundwater system. Our modified Multivariate Time Graph Neural Network model shows significant improvements over traditional methods, particularly in handling missing data and forecasting future groundwater levels with minimal bias. The model's performance is rigorously evaluated when trained and applied with both synthetic and measured data, demonstrating superior accuracy and robustness in comparison to traditional numerical models in long-term forecasting. The study's findings highlight the potential of ST-GNNs in environmental modeling, offering a significant step forward in predictive modeling of groundwater levels.

Forecasting future earthquakes with deep neural networks: Application to California

Summary We use the spatial map of the logarithm of past estimated released earthquake energies as input of Fully Convolutional Networks (FCN) to forecast future earthquakes. This model is applied to California and compared with an elaborated version of the Epidemic Type Aftershock Sequence (ETAS) model. Our long-term earthquake forecast simulations show that the FCN model is close to the ETAS model in predicting earthquakes with $M \ge 3.0,\ 4.0,\ {\rm{and\ }}5.0$ according to the Molchan diagram. Moreover, training and implementing the FCN model is 2000∼4000 times faster than calibrating the ETAS model and generating its probabilistic forecasts. The FCN model is straightforward in terms of its neural network structure and feature engineering. It does not require extensive knowledge of statistical seismology or the analysis of earthquake catalog completeness. Using the earthquake catalog with $M \ge 0$ as FCN input can enhance the model's performance in some time-magnitude prediction windows.

Development of a city-level surface ozone forecasting system using deep learning techniques and air quality model: Application in eastern China

Utilizing regional air quality models to accurately forecast surface ozone (O3) concentrations, particularly high concentrations, is essential for protecting public health. However, forecasts of air quality model often deviate from site observations due to the limitation of grid resolution and uncertainties from emission sources, meteorological conditions, and chemical reaction mechanisms. Especially, the underestimation is significant under condition of high O3 concentrations. Moreover, such deviations tend to accumulate as forecast lead time increases, compounding the challenges associated with reliable air quality forecast. In this study, we employed AlexNet architecture, a classical convolutional neural network, combined with multiple variables related to meteorology, chemistry, emission and geography to establish a non-linear relationship between grid-scale input variables and site-scale hourly O3 forecast biases in Eastern China, aiming to realize accurate city-level ozone forecast based on a regional air quality prediction model (i.e., Nested Air Quality Prediction Model System, NAQPMS). By assigning weights to high-bias samples and high-concentration samples within the loss function, the proposed Weighted AlexNet model (W_AlexNet) effectively reduced forecast biases and enhanced its capability to predict O3 pollution levels. Compared to NAQPMS, W_AlexNet model demonstrated a 25.71% improvement in RMSE and a 7.17% increase in IOA averagely for hourly O3 (O3-1h) forecasts across four different lead times (24-h, 48-h, 72-h, and 96-h). Notably, W_AlexNet model alleviated the tendency of NAQPMS to underestimate high concentrations and showed a superior performance in improving O3-1h pollution level forecasts, particularly for the 72-h and 96-h lead times. W_AlexNet model can effectively mitigate the bias accumulation effect over increasing lead times, thereby enhancing the reliability of longer-term forecasts. Thus, the W_AlexNet model serves as a post-processing model that can calibrate forecast biases in air quality prediction models, significantly improving the accuracy of O3 high concentration forecasts and providing more precise early warnings of O3 pollution. This underscores its utility in air quality management.

An Automatic Data-Driven Long-term Rainfall Prediction using Humboldt Squid Optimized Convolutional Residual Attentive Gated Circulation Model in India

<p style="text-align:justify;"><span style="font-family:"Times New Roman","serif";font-size:12pt;line-height:200%;">Precise long-term rainfall forecasting is becoming progressively significant, predominantly in periods of fluctuating weather circumstances. These prophecies are indispensable for several areas, including agronomy, water resource supervision, flood attentiveness, and effluence monitoring. This framework investigates the composite association between climatological data, with an emphasis on the precise estimating of rainfall by finding the effect of temperature disparities on rainfall outlines in diverse states of India. The developed framework undergoes two phases: pre-processing and rainfall forecasting. At first, the Improved min-max normalization (I-MMN) technique is highlighted in the preprocessing stage to eliminate unsolicited missing values from the database. Then, the convolutional residual attentive gated circulation unit (CResAtt-GCU) technique is presented to envisage the amount of rainfall that occurs in various Indian states. Finally, the Humboldt squid optimizer (HBSO) technique is presented to tune the parameters of the developed model to minimize network complications during the training process. The developed framework is processed under the Python platform and the real-time rainfall prediction database collected between 2000-2023 is utilized for the experimentation process. Various computational measures like R2, root mean square error (RMSE), Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE), mean absolute error (MAE), and computation time are evaluated and distinguished from different studies. The overall R2 of 0.993, RMSE of 0.84, KGE of 0.96, NSE of 0.992, MAE of 0.53, and overall CT of 86.66s are obtained by the developed technique against various conventional studies on forecasting the rainfall in different regions of India.</span></p>

Long-term probabilistic forecasts of activity mitigation in English hospitals: a national elicitation exercise providing an outside view based on judgements of experts in support of the New Hospital Programme

ObjectivesThe planning process for a new hospital relies on assumptions about future levels of demand. Typically, such assumptions are characterised by point estimates, the flaw-of-averages, base-rate neglect and overoptimism from...

Long term demand forecasting in Jakarta distribution grid system using fuzzy logic and artificial neural network method

Abstract In order to attain equilibrium between energy supply and demand, reliance on conventional methods for precise long-term electricity demand forecasting is no longer viable. The utilization of artificial intelligence, such as fuzzy logic and artificial neural network (ANN) models, emerges as a prospective solution in the current dynamic scenario. This research explores long-term electricity demand forecasting within the Jakarta distribution grid system, employing various fuzzy logic and ANN approaches including Sugeno, Mamdani, Bayesian Regularization, and the Levenberg algorithm. The analysis incorporates time series data spanning 2016 to 2019, encompassing electricity load demand, economic factors, and demographic variables, processed using MATLAB. The outcomes of the four forecasting methods reveal an average error range of 2 to 3%. The findings indicate that employing fuzzy logic and ANN methods for long-term electricity demand forecasting can yield a forecast error of less than 3%. The study recommends future research enhancements through the inclusion of additional time series data and a more refined system.

Long-term AI prediction of ammonium levels in rivers using transformer and ensemble models

This study provides a cutting-edge machine learning approach to forecast ammonium (NH4+) levels in River Lee London. Ammonium concentrations were predicted over several time intervals using a complete dataset that includes temperature, turbidity, chlorophyll, dissolved oxygen, conductivity, and pH. Our technique captures the intricate connections between environmental conditions and ammonium concentrations using developed algorithms, including Temporal Fusion Transformer (TFT), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) levels versus the important factors, considerably improving prediction accuracy. The novel aspect of this study is the utilisation of the TFT model for multi-horizon forecasting, which offers high accuracy and interpretability in hydrological predictions by combining convolutional components with an attention mechanism. The study also demonstrates the effectiveness of the TFT model in capturing short-term fluctuations while retaining accuracy over long time periods, which is a major difficulty in environmental modelling. The models used, have exceptional forecasting skills, predicting 150, 200, 365, 730, and 1095 days based on daily average and 12, 24 and 30 months based on monthly average. This dual-scale model combines flexibility and resilience, making it an effective tool for forecasting both short- and long-term environmental changes. The RF model excelled in long-term forecasts, sustaining high R-squared (R²) (0.97) values and low root mean square error (RMSE) (0.18), and the second best one was the XGBoost with optimiser with R2 of (0.92) and RMSE of (0.25) with forecasting 1095 days. The results also found that whilst the TFT captured the fluctuations in the short-term, it struggled with the longer-term predictions due to data granularity. The XGBoost model did remarkably well in monthly forecasts up to 12 months, maintaining low RSME. The findings also highlight the necessity of proactive water management techniques to reduce the risk of potential ecological effects, including hypoxia and oxygen depletion. The findings support resource managers in addressing prospective ammonium toxicity concerns such as oxygen depletion and ecological stress.

The impact of effective fractured rock mass properties on development of flow channeling in enhanced geothermal systems

Controlling the distribution of working fluid in an Enhanced Geothermal System (EGS) is crucial to prevent early thermal breakthrough and sustain the initial heat drainage area. Over time, working fluid flow may localize to a small area if fracture permeability increases non-uniformly, and efforts are not made to control the flow distribution. This scenario can potentially lead to mechanical reopening of hydraulic fractures, flow channeling, and thermal short-circuiting. However, current models neglect the nonlinear and inelastic deformation of fractured rock masses on EGS coupled thermo-mechanical response. The objective of this work is to estimate and predict the likelihood of thermal short-circuiting by flow-channeling in an EGS reservoir composed of rock and compliant natural fractures. We utilize three dimensional numerical solutions with reservoir properties based on effective medium theory of fractured rocks to achieve this objective. The results show that presence of natural fractures considerably changes the EGS response to thermal destressing, compared to linear poroelastic models. Flow channeling and short-circuiting, resulting in early thermal breakthrough, are more likely to occur in sparsely fractured reservoirs with stiff and strong natural fractures. Conversely, short-circuiting is unlikely in densely fractured reservoirs where thermal strains are absorbed by natural fracture opening and shear sliding rather than by rock matrix contraction. Estimation of natural fracture density is crucial in long-term forecasting and prediction of EGS power output. Accurate fracture characterization could decisively impact the fate of a project.

PMformer: A novel informer-based model for accurate long-term time series prediction

When applied to long-term time series forecasting, Informer struggles to capture temporal dependencies effectively, leading to suboptimal forecasting accuracy. To address this issue, we propose PMformer, a novel model based on Informer for long-term time series prediction. First, we introduce a probabilistic patch sampling attention mechanism that utilizes a patch-based strategy to compute attention scores within randomly selected sequence patches. This localized approach enhances the model's capability to capture local temporal dependencies, allowing it to better understand and process critical local features in time series while reducing computational complexity. Additionally, we propose a multi-scale scaling sparse attention technique that balances attention distribution by combining coarse- and fine-grained attention scores, thereby improving the model's ability to capture global sequence information. Finally, we design a dilated causal pooling layer and a multilayer perceptual cross self-attention decoder to further enhance the model's prediction accuracy by capturing key information in long-term correlations and precisely focusing on sequences. We conducted experiments on both multivariate and univariate time series forecasting tasks. The results show that PMformer outperforms six baseline models, including PatchTST and FEDformer, in terms of MAE and MSE metrics. This demonstrates its superior ability to capture temporal dependencies, achieving more accurate predictions.

Progressive Supervision via Label Decomposition: An long-term and large-scale wireless traffic forecasting method

Long-term and Large-scale Wireless Traffic Forecasting (LL-WTF) is pivotal for strategic network management and comprehensive planning on a macro scale. However, LL-WTF poses greater challenges than short-term ones due to the pronounced non-stationarity of extended wireless traffic and the vast number of nodes distributed at the city scale. To cope with this, we propose a Progressive Supervision method based on Label Decomposition (PSLD). Specifically, we first introduce a Random Subgraph Sampling (RSS) algorithm designed to sample a tractable subset from large-scale traffic data, thereby enabling efficient network training. Then, PSLD employs label decomposition to obtain multiple easy-to-learn components, which are learned progressively at shallow layers and combined at deep layers to effectively cope with the non-stationary problem raised by LL-WTF tasks. Finally, we compare the proposed method with various state-of-the-art (SOTA) methods on three large-scale WT datasets. Extensive experimental results demonstrate that the proposed PSLD significantly outperforms existing methods, with an average 2%, 4%, and 11% performance improvement on three WT datasets, respectively. In addition, we built an open source library for WT forecasting (WTFlib) to facilitate related research, which contains numerous SOTA methods and provides a strong benchmark. Experiments can be reproduced through https://github.com/Anoise/WTFlib.

Expanded auditor’s reports and voluntary disclosure

Employing a panel sample of 5,090 firm-years from 981 UK firms during the period from 2010 to 2016, we investigate the impact of the implementation of expanded auditor’s reports (EARs) on corporate voluntary disclosure. We find that the likelihood and frequency of voluntary disclosure significantly decrease after the implementation of EARs. In addition, the total number of risks of material misstatement (RMMs) has no impact on voluntary disclosure after the implementation of EARs, while certain RMMs categories (i.e., entity rather than account risks, and firm-specific rather than industry-level risks) display a more pronounced impact on corporate voluntary disclosure. In further analysis, we find that the mandatory adoption of EARs increases the proportion of long-term forecasts, riches the content of voluntary disclosure, and elicits a more positive market response to the announcement of voluntary disclosure. Cross-sectional analyses show that the impact of the implementation of EARs on voluntary disclosure can be magnified by a higher level of media coverage, especially breaking news coverage. Our results remain valid after considering various robustness tests and selection bias concerns

A parallel and multi-scale probabilistic temporal convolutional neural networks for forecasting the key monitoring parameters of gas turbine

As a crucial equipment in the power industry, gas turbines need effective condition monitoring techniques to maintain safe and reliable operations. Fast yet accurate long-term forecasting of the key monitoring parameters of gas turbine is vital for achieving the overall effective condition monitoring. This however poses challenges in terms of both efficacy and efficiency for conventional time series models like recurrent neural networks (RNN), since they run in a sequential manner. To this end, this paper introduces a novel parallel probabilistic time series prediction model. Particularly, the proposed model leverages the parallelism of temporal convolutional neural network (TCN) and exploits the power of latent space in the Bayesian framework. Besides, a multi-scale feature extraction strategy based on dense connections and a lagged observation strategy are presented to improve the model performance. In the comparative study against state-of-the art competitors on four classical system identification benchmarks, the proposed model significantly reduces the training time, while consistently achieving highly accurate predictions and providing appropriate uncertainty quantification. Thereafter, the proposed model is adopted to build a systematic workflow for the fast yet accurate long-term forecasting of the temperature of blade channel of gas turbine. The comprehensive results again highlight the superiority of the proposed model in terms of both the prediction quality and the training efficiency.

The influence of heavy shipping traffic on the structure and dynamics of sea ice in the southwestern Kara Sea

The development of the Northern Sea Route and the beginning of year-round transit shipping require not only the production of new icebreakers and ice-class vessels, but also the development of a specialized hydrometeorological support system for ice shipping. For the analysis of satellite data and the development and validation of ice forecasts, actual field data on the ice cover is required. This data can only be obtained from shipboard observations; however, scientific expeditions are rarely organized during the winter. In order to obtain new data over the area of intensive shipping, two expeditions were organized on board of nuclear icebreakers in the southwestern Kara Sea (“LED-SMP” expeditions) in 2023 and 2024. Specialized hydrometeorological maintenance of ice shipping in the southwestern Kara Sea together with the research expeditions “LED-SMP” carried out in the same place and time on board the nuclear icebreakers revealed the influence of the technogenic factor on the sea ice structure and dynamics was revealed. In winter and spring, two main routes are used for navigation in the southwestern Kara Sea: through the Kara Gate Strait and north of Cape Zhelaniya. In April 2024, a unique situation occurred when, due to the difficult ice conditions east of the Kara Gate Strait, the entire ship traffic was directed north of Cape Zhelaniya. In preparing a long-term ice forecast, it was noted that after the redirection the natural development of ice processes changed. At the beginning of the winter period 2024,

Periodformer: An efficient long-term time series forecasting method based on periodic attention

As Transformer-based models have achieved impressive performance across various time series tasks, Long-Term Series Forecasting (LTSF) has garnered extensive attention in recent years. The intricate complexity of the Attention mechanism leads to pronounced overfitting of time series data, thereby impeding the efficient and accurate forecasting of long-term sequences. To better cope with this, we design a lightweight Period-Attention mechanism (Periodformer), which renovates the aggregation of long-term subseries via explicit periodicity and short-term subseries via built-in proximity. Meanwhile, a gating mechanism is embedded into Periodformer to regulate the influence of the attention module on the prediction results. This gives Periodformer a much more powerful and flexible sequence modeling capability with linear computational complexity, guaranteeing higher prediction performance and shorter runtime on real devices. Experimental results show that Periodformer consistently performs best on six widely used benchmark datasets. Compared with the state-of-the-art methods, the prediction error of Periodformer was reduced by 13% and 26% for multivariate and univariate forecasting, respectively.

Automated monitoring of natural parameters of renewable energy production systems

The paper discusses the urgent issues of the need to automate the monitoring of natural parameters of renewable energy production systems. Monitoring involves the collec- tion and analysis of various parameters of the natural environment that affect the efficiency and environmental friendliness of systems powered by renewable energy. Based on analytical materials and practical work related to techno-economic and ecological studies concerning the assessment of water reserves in indi- vidual wells, the study focused on the development of technologies and technical means for monitoring. It was found that, in practice, hydrogeological research on groundwaters use outdated methods and technical means for measuring and storing data. A local automated system for monitoring well parameters was developed and constructed. It ensures the operation of the hydrothermal heat pump system at the experimental site of the Institute of Renewable Energy of the National Academy of Sciences of Ukraine. This system was tested in our research. The benefits of this study are revealing the need to create an intelligent monitoring system with the possibility of automated decision-making regarding the management of renewable energy generation systems, depending on changes in the am- bient parameters. A network automated system for monitoring natural parameters was developed, consisting of several wireless sensor subsystems that communicate with each other via wireless connection and transmit information to the gateway. The gateway, in turn, is connected to the computer with a graphical interface for interaction with the system. The results of work indicate that the proposed structures for automated water monitoring are simple and easy to implement. Consequently, the study revealed that one of the prospects is the development of automated monitoring systems with intelligent features, capable of making optimal individual decisions regard- ing the management of renewable energy generation systems under variable environmental parameters. As a result, the data obtained from this study have significant scientific and practical implications for advancing the design methodology of geothermal heat pump wells, focusing on long-term forecasts and assessments of their ecological and economic efficiency.

Southern Hemisphere baroclinic activity in seasonal forecasts

Accurate prediction of mid-latitude baroclinic activity is extremely relevant for understanding global climate dynamics and improving long-term weather forecasts. However, current seasonal forecast models struggle to accurately represent the variability of baroclinic activity in the Southern Hemisphere, which may affect their reliability and usefulness. Baroclinic instability in the mid-latitudes is a significant component of the climate system, as it is associated with the meridional transport of a large amount of energy and momentum. Therefore, the ability of the models to correctly predict the properties of the atmospheric circulation in this latitudinal region is a very important requirement. The aim of this study is to estimate the energy of atmospheric phenomena typical of the mid-latitudes, such as baroclinic perturbations, and to understand how seasonal forecasts can be practically used to assess the energy transfer in the atmosphere. We compare the Southern Hemisphere mid-latitude winter variability of the seasonal forecasts of the ECMWF, DWD and Météo France forecasting systems with the ERA5 reanalysis. The analysis is performed by computing the Hayashi spectra of the 500-hPa geopotential height field. Both the reanalysis and the seasonal forecast show a series of peaks in the spectral region of eastward-travelling waves, which corresponds to the high frequency and high wavenumber domain. We quantify the amount of energy released from the atmosphere by calculating the Baroclinic Amplitude Index. The results suggest that seasonal forecasts may not accurately capture the variability of geopotential height power spectra in the Southern Hemisphere, which poses a challenge in correctly distributing the energy over spatial and temporal dimensions. This study will show that this problem is particularly pronounced for wavenumber 4 over a period of 8 days. This misrepresentation likely contributes to the uncertainties in precipitation forecasting, with discrepancies exacerbated by a suboptimal description of baroclinic instability and dynamical components in the models. Our findings highlight the need for an improved representation of baroclinic processes in seasonal forecast models, which could lead to substantial advancements in long-term weather prediction capabilities and in a more complete understanding of climate dynamics.

Time series decomposition of land surface temperature for long-term trend forecasting and impact on nesting sea turtle habitats in the Arabian Gulf

Abstract Improving Land Surface Temperature (LST) modeling is vital for mitigating climate change effects on various ecosystems and marine habitats such as on important sea turtle habitats. Over the past decade, extreme temperatures have likely significantly affected nesting sea turtle habitats in the Arabian Gulf, with predominantly female hatchlings creating an imbalance in the sex ratio. Such shifts have profound implications for these habitats' long-term survival and conservation management. This study leverages statistical machine learning models to measure ongoing temporal variations in LST. We break down the LST time series into trend, seasonal, and noise components using classical decomposition methods like X11, SEATS, and the Seasonal and Trend decomposition using Loess (STL) approach. The long-term trends in LST data are driven by climate change rather than seasonal fluctuations. We employed Neural Network Auto Regression (NNAR), BaggedETS, Exponential Smoothing models, and STL method to project future LST values. We also explored advanced forecasting models like Dynamic Harmonic Regression, TBATS, and SARIMA for comparative performance analysis. Extended warm periods were identified for Abu Ali Island between 2017 and 2018 through several decomposition methods, likely linked to the 2015-2016 El Niño event. We also conducted a Marine Heat Wave (MHW) analysis from 2010-2020, establishing a pronounced impact of the 2015-2016 El Niño on the Arabian Gulf. In nesting beach environments with high LST, marine heatwaves could have a significant impact on sea turtle populations without human intervention such as artificially cooling the nest temperature. SARIMA model showed higher forecasting precision for in-situ weather data while NNAR model demonstrated superior performance with remotely sensed data.