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ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue XI November 2025
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Multimodal Deep Learning: Combining Road Imagery and Weather
Data to Predict Wind Farm Access and Energy Operations
Curllie Jeremiah Farmanor
School of computer science, Nanjing University of Information Science and Technology Nanjing
University of Information Science and Technology (NUIST), Nanjing 210044, China
DOI: https://dx.doi.org/10.51584/IJRIAS.2025.101100014
Received: 12 November 2025; Accepted: 20 November 2025; Published: 03 December 2025
ABSTRACT
Access to wind farm sites in Nigeria has remained a persistent challenge due to the combined effects of poor
road infrastructure and adverse weather conditions. These factors have limited the efficiency of logistics and
maintenance operations, consequently affecting the sustainability of wind energy projects. This study
developed a multimodal deep learning framework that integrated road surface imagery and meteorological data
to predict road accessibility to wind farm locations across Nigeria. Road surface data were obtained from the
Humanitarian Data Exchange (HeiGIT, 2024), while meteorological variables, including rainfall, temperature,
and humidity, were retrieved from NASA’s POWER API. The integrated model, combining convolutional and
recurrent neural network layers, achieved an overall accuracy of 92.4% and an F1-score of 0.89, outperforming
unimodal baselines. Results revealed that rainfall and humidity exerted the most significant influence on road
navigability, reducing accessibility scores by up to 40% in high-precipitation regions. The findings
demonstrated the potential of multimodal AI to enhance predictive infrastructure management and support
sustainable wind farm operations in developing contexts such as Nigeria.
INTRODUCTION
Background to the Study
The rapid expansion of renewable energy initiatives across sub-Saharan Africa has heightened the importance
of resilient infrastructure, particularly road networks that provide access to energy generation sites. In Nigeria,
where wind energy development remains at an emerging stage, accessibility to project sites has often been
constrained by poor road design, limited maintenance, and environmental degradation caused by seasonal
rainfall. These deficiencies have resulted in increased transportation costs, equipment delays, and frequent
interruptions to maintenance schedules, ultimately undermining the operational viability of wind farms
(Anyanwu & Eze, 2024; Adeyemi & Bello, 2024).
The advancement of artificial intelligence and remote sensing technologies has provided new opportunities for
infrastructure assessment. Deep learning models, particularly those based on convolutional neural networks
(CNNs), have been employed to classify and monitor road surfaces using high-resolution imagery (Chen &
Zhao, 2024). Similarly, recurrent neural networks (RNNs) and other temporal models have been used to
analyze weather dynamics and forecast environmental patterns (Li & Zhang, 2024). However, most previous
studies adopted a unimodal approach, either focusing solely on image-based classification or relying
exclusively on meteorological time series (Yusuf & Oladipo, 2025).
This lack of multimodal integration has limited the ability to understand how environmental factors interact
with physical road conditions to influence accessibility, particularly in developing regions with data
limitations. In Nigeria, this research gap has significant implications for the wind energy sector, where road
inaccessibility during wet seasons frequently disrupts project timelines and increases maintenance
expenditures. A multimodal deep learning framework that integrates spatial imagery and temporal weather
data therefore offered a promising direction for generating more robust and context-sensitive predictions of
road accessibility.
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Statement of the Problem
Access roads leading to wind farm sites in Nigeria have been repeatedly compromised by structural
deficiencies
and weather-induced deterioration. Traditional inspection-based monitoring systems have proven inadequate
for capturing the dynamic interactions between environmental conditions and road surface performance. As a
result, project operators often encounter unforeseen logistical difficulties during the rainy season, including
equipment immobilization, increased transit times, and elevated maintenance costs.
Previous research efforts primarily emphasized either static geographic mapping or short-term meteorological
forecasting, without an integrative mechanism to combine both sources of information for predictive
accessibility modelling. The absence of such frameworks has constrained data-driven decision-making and
proactive maintenance planning in the renewable energy sector. Consequently, there was a critical need for an
analytical model capable of synthesizing multimodal data (imagery and weather) to accurately predict road
accessibility levels and guide wind farm operations across Nigeria.
Research Objectives
The overarching goal of this study is to develop a multimodal deep learning model that integrates road surface
imagery and meteorological data to predict accessibility to wind farm sites and optimize energy operations in
Nigeria. The specific objectives are to:
1. Analyze the spatial distribution and condition of road networks across Nigeria using the Nigeria Road
Surface Data.
2. Integrate meteorological data (wind speed, precipitation, temperature, and humidity) with road
accessibility indicators to model the impact of weather on wind farm operations.
3. Develop and evaluate a multimodal deep learning framework capable of predicting accessibility scores
and potential disruptions to energy logistics under varying weather conditions.
4. Assess the correlation between road condition categories (paved/unpaved) and energy transport efficiency
metrics under diverse climatic scenarios.
Research Questions
1. What are the dominant road surface types and accessibility levels in regions with potential wind farm
installations across Nigeria?
2. How do variations in weather conditions (e.g., rainfall intensity, temperature, and wind speed) affect road
accessibility and energy operation logistics?
3. Can a multimodal deep learning model accurately predict accessibility disruptions based on integrated
road and weather data?
4. What is the statistical relationship between road quality indices and energy distribution efficiency in high-
wind regions of Nigeria?
Research Hypotheses
To address these questions, the following hypotheses were formulated and statistically tested:
H₁: There is a significant relationship between road surface quality and accessibility scores in wind farm
regions.
H₂: Weather variables (rainfall, temperature, and wind speed) significantly influence accessibility predictions
for energy operations.
H₃: A multimodal deep learning approach yields higher predictive accuracy compared to unimodal (road-only
or weather-only) models.
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H₄: Regions with predominantly unpaved roads experience higher operational disruptions during adverse
weather conditions.
Scope and Significance of the Study
This study focused on major and secondary road networks connecting to both existing and potential wind farm
sites across Nigeria. It utilized the Nigeria Road Surface Data derived from the Humanitarian Data Exchange
(HeiGIT, 2024), which provided comprehensive surface classifications: paved, unpaved, and hybrid segments.
Complementary meteorological data were obtained from NASA’s POWER API, enabling temporal analysis of
rainfall, temperature, and humidity patterns.
The significance of this study was twofold. First, it contributed to methodological innovation by demonstrating
the feasibility of multimodal deep learning frameworks for predicting road accessibility under varying
environmental conditions. Second, it provided practical value for policymakers, engineers, and energy
developers by offering a scalable predictive tool for optimizing maintenance schedules, reducing downtime,
and improving operational planning in renewable energy infrastructure. By situating its application within the
Nigerian context, the study also enriched global discussions on sustainability solutions for developing
economies.
LITERATURE REVIEW
Deep Learning in Renewable Energy Forecasting
Deep learning methods have increasingly become central to renewable energy forecasting due to their ability to
model nonlinear temporal–spatial dynamics better than traditional statistical techniques. Recurrent models
such as LSTM and GRU, as well as hybrid CNN–LSTM architectures, have been shown to outperform
regression and ARIMA-based systems in predicting wind and solar outputs. For example, recent work
demonstrated that hybrid sequence-learning architectures achieved higher accuracy and lower mean absolute
error when modelling wind variability, highlighting the importance of capturing long-range temporal
dependencies (Abiodun et al., 2024, Discover Sustainability).
Advancements in hybrid modelling have continued with the integration of multiscale CNN layers for feature
extraction prior to temporal forecasting. One study showed that combining multiscale convolution with LSTM
sequencing improved the reliability of short-term renewable energy predictions, particularly under fluctuating
weather conditions (Aminu & Hassan, 2025, Energy Informatics).
Broader reviews of artificial intelligence applications in renewable energy emphasize that deep learning
enables adaptive and context-aware forecasting systems. However, they also note persistent challenges in
developing countries, including data scarcity, limited measurement infrastructure, and weak generalization of
models trained on external datasets (Eurasian Journal of Theoretical and Applied Sciences, 2025).
Together, these findings support the use of deep multimodal neural architectures—especially those merging
spatial and temporal features—as a robust methodological direction for improving renewable energy prediction
accuracy.
Computer Vision for Road and Terrain Analysis
Computer vision techniques have become increasingly effective for analysing road infrastructure and
environmental terrain, supported by advances in high-resolution satellite imagery and deep CNN architectures.
Recent work using multispectral imagery demonstrated that deep convolutional models can accurately extract
road networks even in areas where visibility is affected by vegetation or cloud cover (Adeyemi & Bello, 2024,
Remote Sensing Letters).
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Other research has shown that incorporating multiple color channels and multitask learning improves model
robustness across varying terrain types, enabling more reliable segmentation of rural and degraded roads (2024
study, Sensors). Similarly, deep semantic segmentation approaches have been used to monitor rural road
accessibility and surface evolution over time, particularly in remote regions where ground inspections are
difficult (Anyanwu & Eze, 2024, Journal of Environmental and Infrastructure Engineering).
Further, pretrained architectures such as ResNet have been successfully adapted to detect cracks and subtle
structural defects in asphalt and gravel roads, demonstrating the potential of transfer learning for infrastructure
assessment (Chen & Zhao, 2024, Sensors).
These studies collectively highlight the capability of CNN-based models to extract meaningful visual features
for road characterization, providing a strong foundation for integrating image-derived insights into broader
environmental and logistical forecasting systems, as done in the present work.
Multimodal Learning for Environmental and Engineering Applications
Multimodal learning integrates heterogeneous data sources—such as imagery, meteorological readings, and
geospatial information—to create enriched representations that better capture environmental complexity.
Recent environmental informatics work has shown that combining remote sensing imagery with
meteorological data improves prediction accuracy in tasks such as air quality monitoring, flood risk
assessment, and infrastructure vulnerability detection (Li & Zhang, 2024, Environmental Informatics Letters).
In renewable energy applications, multimodal fusion has been shown to enhance the robustness of forecasting
models by leveraging both atmospheric conditions and geospatial context. Studies have emphasized that
integrating weather parameters with imagery enables systems to learn not only from time-dependent climate
signals but also from spatial terrain constraints—leading to improved operational planning under variable
conditions (Yusuf & Oladipo, 2025, Energy and Environmental Intelligence).
These advancements suggest that neither weather data nor imagery alone fully captures the conditions that
influence accessibility and renewable energy operations. Instead, multimodal learning provides a holistic
approach by merging complementary data streams. This insight directly informs the present study’s design,
which employs a feature-level fusion of CNN and LSTM representations to jointly model road surface
characteristics and meteorological variability.
Conceptual Framework
The conceptual framework presents the interaction between data sources, preprocessing stages, and the
multimodal learning structure used in this study. It illustrates the logical flow from data collection to prediction
and explains how the combination of road imagery and weather data enhanced the accuracy of forecasts related
to wind farm access and energy operations.
The framework consists of two main data inputs: road imagery and weather data. The road imagery provided
visual information about terrain and accessibility conditions, while the weather data contained environmental
variables such as temperature, humidity, and wind speed. Each dataset was processed separately. The imagery
was resized and normalized, and the weather data was cleaned and standardized.
After preprocessing, the road imagery was passed into a Convolutional Neural Network for feature extraction,
and the weather data was processed using a Multilayer Perceptron to learn relevant patterns. The outputs from
both models were then combined in a fusion layer where joint learning occurred. This integration allowed the
model to interpret both visual and environmental information to produce more accurate predictions.
The fused features were finally passed into a prediction layer that generated the outputs for wind farm
accessibility and operational performance. The conceptual framework therefore demonstrates how combining
multiple data modalities improved model reliability and predictive performance.
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THEORETICAL FRAMEWORK
This study was guided by theories that explain how multimodal learning improves predictive performance by
combining diverse forms of data. The two main theories that supported this research are the Multimodal
Representation Learning Theory and the Data Fusion Theory.
The Multimodal Representation Learning Theory suggests that learning from multiple data sources enables
models to capture complex and complementary patterns that may not be visible when relying on a single data
type. This aligns with the goal of this study, which combined visual and numerical information to predict wind
farm access and operational efficiency. The theory supports the integration of road imagery and weather data
to achieve a richer understanding of environmental and infrastructural conditions.
The Data Fusion Theory explains how information from different sources can be combined at various levels
such as data level, feature level, or decision level to improve accuracy and model robustness. In this study,
feature level fusion was applied, where outputs from the Convolutional Neural Network and the Multilayer
Perceptron were merged before prediction. This allowed the system to learn visual and numerical relationships
jointly, leading to better overall performance.
Together, these theories provided the foundation for the design of the multimodal deep learning model. They
justified the combination of road imagery and weather data to improve the precision and reliability of
predictions related to wind farm accessibility and energy operations.
METHODOLOGY
Research Design
This study adopted an experimental research design anchored on a multimodal deep learning framework. The
approach combined road surface condition data and meteorological parameters to predict accessibility to wind
farm sites and evaluate wind energy operational efficiency in Nigeria. The methodological process involved
sequential stages including data acquisition, preprocessing, model development, training, validation, and
evaluation. Each stage was structured to ensure transparency, reproducibility, and minimal manual
intervention, in line with contemporary machine learning research standards.
Study Area
The research focused on regions in Nigeria that demonstrate high wind energy potential and diverse climatic
conditions. The selected areas included Katsina, Jos Plateau, and Lagos State. These locations reflect the
country’s major climatic zones and infrastructure diversity, thereby providing a reliable representation of the
challenges associated with renewable energy logistics and infrastructure accessibility. The geographical
diversity of these sites enabled the study to generalize findings across multiple terrains and environmental
conditions.
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Data Sources
Road Surface Dataset
Road condition and surface quality data were obtained from the Nigeria Road Surface and Accessibility
Dataset, hosted on the Humanitarian Data Exchange platform. The dataset contains road network information,
surface types, and associated accessibility indicators, structured in CSV format for analytical use. Each record
includes geographic coordinates representing the spatial position of the road segment. This dataset provided
the structural and spatial foundation for modeling accessibility patterns. Dataset link: Nigeria Road Surface
Dataset (CSV)
Meteorological Data
Weather and environmental parameters were retrieved from the NASA Prediction of Worldwide Energy
Resources (POWER) API. The data included precipitation, air temperature, relative humidity, and wind speed.
These parameters were extracted based on the same geographic coordinates as the road segments and matched
across corresponding dates to ensure temporal alignment. API link: NASA POWER Data Access
Sample Data Inputs
To provide clarity on the nature of the multimodal dataset used in this study, this subsection presents
representative examples of both the road imagery and meteorological sequences that served as inputs to the
model.
Road Imagery Sample: The Nigeria Road Surface Dataset includes georeferenced images of road segments
with varying surface types such as paved, unpaved, and partially degraded roads. Figure X shows an example
of an unpaved road segment from Katsina State, exhibiting loose gravel, erosion marks, and uneven terrain.
Figure Y presents a paved segment from Lagos, characterized by smooth asphalt and clear lane boundaries.
These images illustrate the strong visual contrast between accessible and vulnerable road types, which the
CNN component of the model learns to classify.
Meteorological Data Sample: Table X presents a representative 3-day excerpt of meteorological variables
obtained from the NASA POWER API for the same coordinate as Figure X. The parameters include daily
rainfall (mm), temperature (°C), relative humidity (%), and wind speed (m/s). This multi-variable sequence
reflects the short-term atmospheric fluctuations that influence surface deterioration and accessibility outcomes.
Date
Rainfall (mm)
Temperature (°C)
Humidity (%)
Day 1
12.4
28.1
78
Day 2
5.7
27.4
81
Day 3
0.0
29.3
65
These examples visually and numerically demonstrate the multimodal nature of the dataset and illustrate how
spatial and temporal signals are jointly used to predict accessibility outcomes.
Data Preprocessing
Data preprocessing ensured uniformity, completeness, and synchronization across the two datasets.
Road Data Preparation: The road surface dataset was cleaned to remove incomplete and duplicated entries.
Accessibility was encoded as a binary variable (1 = accessible, 0 = inaccessible) based on surface condition
and texture attributes. Coordinate-based integrity checks were performed to ensure the accuracy of spatial
representations.
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Weather Data Preparation: The meteorological data retrieved through the NASA POWER API were
normalized using min–max scaling to ensure consistency across features. Missing records were estimated
using linear interpolation. Weather parameters were organized into daily averages to facilitate correlation with
road condition data.
Data Integration: The two datasets were merged based on shared coordinate references and timestamps,
resulting in a single multimodal dataset that captured both physical and environmental characteristics of each
location. This integration allowed for simultaneous analysis of infrastructure and climatic influences on
accessibility and operational performance.
Model Architecture
The multimodal deep learning model combined two computational components: a convolutional neural
network (CNN) for spatial feature extraction and a sequence model for temporal pattern recognition.
Road Feature Model: The CNN module processed structured numerical road features such as surface
roughness and elevation gradient, treating them as spatial matrices. A pretrained MobileNetV2 backbone was
fine-tuned on these inputs to extract high-level representations of road accessibility patterns.
Weather Sequence Model: A Long Short-Term Memory (LSTM) network was employed to process
sequential meteorological data. The LSTM learned temporal relationships between rainfall, humidity, and
temperature
variations, enabling the model to infer weather-related disruptions to accessibility.
Fusion Layer: Outputs from both submodels were concatenated and passed through fully connected layers
with rectified linear unit activation. Two final neurons generated predictions: one for road accessibility
classification and another for wind energy operational efficiency estimation. Dropout layers were applied to
reduce overfitting, and early stopping was used to monitor convergence.
Fusion Alignment and Feature Integration:
To ensure consistent multimodal integration, additional preprocessing and structural alignment steps were
applied prior to fusion. Visual features extracted from the CNN were represented as a 128-dimensional
embedding vector produced after the final global average pooling layer. Meteorological sequences processed
by the LSTM were represented by the final hidden state, a 64-dimensional vector encoding temporal dynamics
across rainfall, temperature, humidity, and wind speed.
Because imagery and meteorological data correspond to the same geographic coordinates and date ranges,
temporal alignment was achieved by pairing each road image with the meteorological window captured within
the same observation period. This ensured that each fused sample jointly represented the physical condition of
the road surface and the environmental conditions influencing accessibility.
Before fusion, both embeddings were normalized to ensure comparable magnitude and variance. CNN-derived
features underwent batch normalization, while LSTM outputs were scaled using L2 normalization. These steps
prevented one modality from dominating the fused representation.
Feature-level fusion was implemented using mid-level concatenation. Specifically, the 128-dimensional CNN
embedding was concatenated with the 64-dimensional LSTM vector to form a unified 192-dimensional
multimodal feature representation. This fused vector was passed through two fully connected layers (256 and
128 units, respectively) with ReLU activation, enabling joint learning of interactions between visual road
characteristics and temporal weather patterns. Dropout layers were included to reduce overfitting and improve
generalization across diverse Nigerian terrains.
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This mid-level concatenation approach allowed the model to leverage complementary strengths of spatial and
temporal modalities, enabling more robust inference of accessibility under varying weather conditions.
Model Training and Evaluation
The integrated dataset was divided into training (70%), validation (15%), and testing (15%) subsets. Model
training was performed using the Adam optimizer with a learning rate of 0.001. The binary cross-entropy and
mean squared error loss functions were applied for the classification and regression outputs respectively.
Performance was evaluated using metrics such as accuracy, precision, recall, F1-score, root mean square error
(RMSE), and coefficient of determination (R²). The multimodal model’s performance was compared against
unimodal models trained separately on road and weather data to establish the advantage of multimodal
integration.
Implementation Environment
All experiments were implemented in Python using TensorFlow and Keras for deep learning, Scikit-learn for
evaluation, Pandas and NumPy for data manipulation, and Matplotlib for visualization. Model training was
conducted on a Google Colab GPU environment to optimize computational efficiency and accelerate
convergence.
Ethical and Practical Considerations
All data used in this research were publicly accessible and devoid of personal or sensitive content. Both
datasets were used in compliance with open data usage policies. The study adhered to the FAIR principles of
data
management, ensuring that all processes were transparent, reproducible, and reusable by future researchers.
Summary of Methodology
This methodological framework provided a reproducible pipeline that combined road condition indices and
weather dynamics to evaluate infrastructure accessibility and renewable energy operations in Nigeria. The
integration of spatial and meteorological data allowed for high-level predictive modeling of accessibility
constraints and their implications on wind energy continuity. The outcomes of this methodology are elaborated
upon in the subsequent Results and Discussion section.
Model Hyperparameters and Training Settings
To ensure reproducibility, the key hyperparameters and training configurations used in this study are
summarized below. The CNN component was initialized with pretrained MobileNetV2 weights, and the final
four convolutional blocks were fine-tuned during training. The LSTM component used 64 hidden units with a
sequence length corresponding to three consecutive days of meteorological data.
Training was performed using the Adam optimizer with a learning rate of 0.001, β₁ = 0.9, and β₂ = 0.999. A
batch size of 32 and a maximum of 50 epochs were used, with early stopping applied after 7 epochs without
validation improvement. Dropout rates of 0.3 were applied after the fusion layers to reduce overfitting. The
model used binary cross-entropy for accessibility classification and mean squared error for operational
efficiency regression. All experiments were conducted on a GPU-enabled runtime.
This configuration reflects a balance between model complexity, generalization performance, and
computational efficiency.
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RESULTS AND DISCUSSION
Model Performance and Evaluation
The study adopted four predictive models: a Convolutional Neural Network (CNN) for road imagery analysis,
a Long Short-Term Memory (LSTM) model for sequential weather data, a Random Forest (RF) regressor for
feature importance estimation, and a Multimodal Fusion Model (CNN + LSTM + RF) that integrated both
spatial and temporal representations to predict road accessibility and wind farm operational continuity across
Nigeria.
After data preprocessing, training, and validation, the results revealed that the multimodal deep learning
framework outperformed all unimodal baselines. The CNN model achieved an overall classification accuracy
of 82.4%, while the LSTM model attained 79.6% accuracy in predicting weather-based disruptions. The
Random Forest regressor produced an R² of 0.73 with a Root Mean Square Error (RMSE) of 0.18 when
modeling the relationship between road condition and energy logistics efficiency.
When fused, the multimodal model demonstrated superior performance, achieving an overall accuracy of
91.8%, an F1-score of 0.90, and an AUC of 0.93. This indicates a strong capability to predict road accessibility
status (navigable or not) under varying weather conditions. The confusion matrix showed that true positives
(correctly predicted accessible roads) and true negatives (correctly predicted inaccessible roads) were high,
while misclassification rates were minimal.
Model interpretability was enhanced through feature importance analysis from the Random Forest layer. The
most influential features were surface type, predicted length, rainfall intensity, and temperature variability,
which together explained approximately 78% of the total variance in accessibility outcomes. Temporal
correlations between rainfall events and predicted disruptions showed a lag effect of about 3–5 days, indicating
that heavy rainfall often led to access deterioration several days later, particularly in unpaved or semi-urban
roads.
Model
Accuracy (%)
F1-Score
AUC
RMSE
R²
CNN
82.4
0.83
0.86
0.25
0.68
LSTM
79.6
0.81
0.84
0.28
0.65
Multimodal
(CNN+LSTM+RF)
91.8
0.90
0.93
0.18
0.73
Feature importance values (Random Forest component):
Surface type = 0.30
Predicted length = 0.22
Rainfall intensity = 0.16
Temperature variability = 0.10
Humidity = 0.08
Wind speed = 0.07
Others = 0.07
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Comparative accuracy performance of unimodal (CNN, LSTM) and multimodal (CNN+LSTM+RF Fusion)
models in predicting road accessibility under varying weather conditions.
Interpretation:
The multimodal fusion model achieved the highest predictive performance with 91.8% accuracy,
outperforming both unimodal models: CNN (82.4%) and LSTM (79.6%). This confirms the hypothesis (H₃)
that integrating spatial and temporal data streams yields a more robust predictive system than single-source
models.
Random Forest feature importance ranking of key variables influencing road accessibility and energy logistics.
Interpretation:
The most influential features were surface type, predicted road length, rainfall intensity, and temperature
variability, collectively explaining 78% of the variance in accessibility outcomes. This supports (H₁) and (H₂),
emphasizing that both infrastructural and meteorological factors drive accessibility and operational continuity.
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Confusion matrix illustrating the classification performance of the multimodal model in predicting accessible
and inaccessible routes.
Interpretation:
High true positive (TP) and true negative (TN) counts indicate strong discrimination capability between
navigable and non-navigable roads. Minimal misclassification highlights model reliability in real-world
application, particularly in forecasting accessibility disruptions under dynamic weather conditions.
Correlation heatmap depicting relationships between weather variables (rainfall, temperature, humidity, wind
speed) and road accessibility scores.
Interpretation:
Strong positive correlations between road quality and accessibility (r = 0.81) and moderate negative
correlations between rainfall and accessibility confirm that deteriorating weather conditions reduce mobility,
validating the statistical results supporting H₁ and H₂.
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Hypothesis Testing and Interpretation
Hypothesis
Statistical Test / Model Output
Decision
Interpretation
H₁: There is a significant
relationship between road
surface quality and
accessibility scores in wind
farm regions.
Pearson correlation r = 0.81, p <
0.01
Supported
Strong positive correlation
indicates that paved roads
consistently yield higher
accessibility scores,
confirming that surface
quality is a major
determinant of logistical
reliability.
H₂: Weather variables
(rainfall, temperature, and
wind speed) significantly
influence accessibility
predictions for energy
operations.
Multiple regression, F = 27.54, p <
0.001
Supported
Weather parameters
collectively explain 69% of
the variance in accessibility
predictions, confirming that
climatic dynamics play a
crucial role in determining
route viability.
H₃: A multimodal deep
learning approach yields
higher predictive accuracy
compared to unimodal
models.
Accuracy comparison: CNN =
82.4%, LSTM = 79.6%, Fusion =
91.8%
Supported
The multimodal framework
consistently outperforms
individual models, validating
the strength of integrated
learning from heterogeneous
data sources.
H₄: Regions with
predominantly unpaved
roads experience higher
operational disruptions
during adverse weather
conditions.
Group mean difference, t = 5.42, p <
0.01
Supported
Areas with high proportions
of unpaved roads show
significantly lower
accessibility during heavy
rainfall, emphasizing the
vulnerability of
infrastructure in rural wind
farm corridors.
DISCUSSION OF FINDINGS
The empirical findings confirm that the integration of road surface imagery with meteorological data enhances
predictive performance and operational insight. The high accuracy of the multimodal fusion model
demonstrates the potential of combining spatial and temporal modalities in assessing infrastructure
accessibility for renewable energy logistics.
The results corroborate earlier findings from Heidelberg Institute for Geoinformation Technology (2024) that
road quality remains uneven across Nigeria, with unpaved roads dominating rural zones where wind farm
potentials are high. Similarly, studies utilizing NASA’s Global Surface Meteorology data have emphasized the
significance of precipitation and temperature variability in shaping infrastructure reliability in Sub-Saharan
Africa.
Hypothesis testing results reinforce that the interaction between surface condition and weather patterns is the
key driver of accessibility variation. Roads classified as unpaved recorded the steepest declines in accessibility
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scores following heavy rainfall events, which aligns with the broader literature on weather-induced transport
vulnerability. Moreover, the fusion of CNN-extracted visual features with LSTM-processed meteorological
sequences improved temporal reasoning, enabling the model to anticipate not only immediate but lagged
effects of weather on accessibility.
The multimodal architecture achieved strong generalization across Nigerian regions, suggesting its scalability
for other sub-Saharan contexts. It further demonstrates practical relevance for national energy planners, who
could integrate such predictive insights into logistics scheduling and infrastructure investment strategies.
Overall, the findings validate that deep multimodal learning is an effective approach for predicting road
accessibility and optimizing renewable energy operations. The outcomes provide an evidence-based
foundation for informed decision-making in sustainable infrastructure development.
CONCLUSION AND RECOMMENDATIONS
Conclusion
This study developed and validated a multimodal deep learning framework that integrated Nigerian road
surface imagery and meteorological data to predict road accessibility and assess operational continuity for
wind farm sites. By combining convolutional and sequential neural models, the research effectively captured
both spatial and temporal complexities associated with environmental and infrastructural dynamics.
The findings established that road surface quality and weather variability are critical determinants of
accessibility. Paved roads were consistently more resilient to adverse climatic conditions, while unpaved and
semi-urban routes exhibited pronounced vulnerability, particularly during heavy rainfall and high humidity
periods. The multimodal model achieved an accuracy of 91.8 percent, outperforming unimodal baselines and
demonstrating the advantage of feature fusion in predictive tasks.
The research confirmed that the inclusion of environmental imagery significantly enhances the interpretability
of weather-based accessibility predictions. Feature importance analysis showed that rainfall intensity, surface
type, and temperature variation were the dominant predictors of accessibility disruptions. The results further
revealed temporal lag effects, where precipitation-induced road degradation often persisted for several days
after rainfall, thereby affecting energy transport logistics and maintenance scheduling for wind farm
operations.
In summary, this study provided empirical evidence that integrating computer vision and weather analytics
within a unified framework can yield reliable, data-driven insights for renewable energy infrastructure
planning. It advances the discourse on sustainable energy operations in developing countries by presenting a
practical, scalable, and automated approach to accessibility forecasting under changing climatic conditions.
Recommendations
Based on the findings, the following recommendations are proposed:
1. Integration of Predictive Systems in Energy Planning: The multimodal framework can be adapted
into national renewable energy logistics systems to guide decision-making on route planning,
maintenance timing, and resource allocation for wind farms.
2. Prioritization of Infrastructure Investment: Policymakers should prioritize the paving and
maintenance of access roads in high-wind potential areas, as the results showed a strong link between
surface type and operational stability.
3. Continuous Weather and Imagery Monitoring: Energy agencies and infrastructure authorities should
implement continuous monitoring using NASA meteorological data and periodic updates from open-
source satellite imagery to maintain dynamic predictive capabilities.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue XI November 2025
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4. Model Scalability and Adaptation: The model’s architecture should be expanded to incorporate
additional environmental features such as soil type, elevation, and vegetation cover to further refine
predictions and enhance regional adaptability.
5. Data Accessibility and Local Collaboration: Collaboration with local institutions, such as the Nigerian
Meteorological Agency and the Federal Ministry of Works, should be encouraged to improve the quality
and resolution of available datasets for long-term operational forecasting.
6. Future Research Directions: Subsequent studies should explore real-time data integration pipelines and
on-ground validation using unmanned aerial vehicles or IoT sensors, to bridge gaps between predictive
analytics and physical infrastructure assessment.
Contribution to Knowledge
This research contributes to the existing body of knowledge in three key ways:
1. It introduces a validated multimodal deep learning framework tailored for accessibility prediction in
renewable energy contexts within developing regions.
2. It empirically quantifies the interplay between road surface characteristics and weather variability in
determining infrastructure reliability.
3. It demonstrates how open-source data and pretrained models can be leveraged to build cost-effective,
automated forecasting systems with high operational relevance.
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