Acquiring time-series data in life sciences, such as soil, hydrological, and climatic measurements, presents significant challenges because of the susceptibility of real sensors to damage and malfunction. These issues often lead to incomplete, inconsistent, or missing data, where traditional machine learning and deep learning models struggle to handle efectively. To address this, the paper introduces a robust approach leveraging generative adversarial networks (GANs) to improve data reliability. GANs are used to generate synthetic data that fill in gaps and correct inconsistencies, resulting in a more complete and accurate dataset. The method involves training the GAN on the existing dataset to learn its fundamental patterns and subsequently producing new data that align with these patterns. The efectiveness of the proposed pipeline is validated through extensive set of experiments across various life-science datasets. The results demonstrate significant improvements in error metrics, including reduced mean absolute error (MAE) and root mean square error (RMSE), alongside increased R² scores. These findings highlight the enhanced accuracy and reliability of the pipeline compared to conventional approaches.
Scientific data is crucial in advancing our understanding of
natural phenomena and driving innovations across various
ifelds. It encompasses a range of data types, from categorical
data requiring straightforward statistical methods to
complex time-series data necessitating sophisticated analytical
approaches and advanced artificial intelligence (AI)
techniques. A significant subset of scientific data is life science
data, which often involves time series measurements such
as soil moisture levels and temperature fluctuations. These
measurements are highly sensitive to climatic variations and
external factors. Accurate monitoring, analysis, and
prediction of these parameters are essential for environmental
preservation, agricultural management, and climate change
mitigation. However, collecting and analyzing time series
data in life sciences presents challenges due to sensor issues
such as noise, errors, and sensor drift, which complicate
data collection. Enhancing data quality involves addressing
these issues to improve reliability [
To overcome these data collection and analysis challenges,
advanced techniques such as Generative Adversarial
Networks (GANs) can be utilized. GANs create synthetic
datasets that simulate real-world conditions, reducing the need
for extensive physical data collection. Data augmentation
using GANs improves the spatial and temporal resolution of
environmental research data, providing a more
comprehensive view of the monitored environment and enhancing the
performance of ML models with more diverse training
samples [
To sum up, the followings are the main contributions of the paper: 1. Introducing an efective pipeline for analysis and processing sparse temporal life science data. 2. Investigating the performance of traditional machine learning and deep learning models in understanding climate-soil interactions, and 3. Applying GANs in life science data analysis.
The field of soil-climate interactions and life science data analysis encompasses many complex concepts and methodologies. Establishing a robust foundation for this work, this section presents a thorough examination of the basic principles, ideas, and approaches that are pertinent to the argument.
Time series analysis, as described in [
Imputation techniques are crucial for estimating and
substituting missing values to facilitate comprehensive data
analysis. There are several ways to deal with missing values,
such as cubic and linear imputation, Gaussian imputation,
and K-Nearest Neighbors (KNN) imputation. Cubic and
linear imputation methods introduced by [
Machine learning utilizes a range of models to examine
data and make predictions about future events. This section
presents two often used models: Linear Regression and
Random Forest [
Deep Neural Network (DNN) [
The training of GANs involves a minimax game between the generator and the discriminator: 1. Discriminator Training: The discriminator is updated to maximize the probability of correctly classifying real and fake data. The loss function for the discriminator is given by:
︁( = −
E∼ data()[log ()]
+ E∼ ()[log(1 − (()))]︁) [
= − E∼ ()[log (())] [
min max (, ) = E∼ data()[log ()]
+ E∼ ()[log(1 − (()))] [
Key advancements include the optimization of deep learning
models using GANs and the Sailfish Optimization Algorithm
(SOA) for soil moisture prediction [
This paper builds on these advancements by incorporating advanced imputation techniques to preprocess datasets before applying GANs, ensuring the generation of highquality synthetic data. By integrating various deep learning and machine learning models, including GANs, DNNs, SNNs, and CNNs. This work further aims to develop highly accurate and reliable prediction models for soil moisture and temperature. This comprehensive strategy results in enhanced prediction models that exhibit high levels of accuracy and reliability. 4.
In this section, we outline the proposed approach, which consists of the following main tasks, as shown in Figure 1: Exploratory Data Analysis (EDA), imputing missing data, training models on the completed datasets, generating synthetic data using GANs, and evaluating the efectiveness of
In the following we are going to provide more details for
EDA is an essential process for understanding the organization, integrity, and attributes of the information. The first step involves identifying any missing values present in the dataset. This stage is crucial as it establishes the magnitude of the missing data and guides the approach for addressing these models. each task.
(EDA) it. The number of missing values in each row and column is computed to assess the degree of data incompleteness. Hence, comprehending the dispersion and quantity of missing data aids in determining the appropriate imputation methods to guarantee the integrity and use of the dataset.
After identifying missing data, the subsequent action is to impute these absent values. Three diferent algorithms: KNN, Cubic and Gaussian which are explained in section (2.2) are employed and later compared to pick the best method for further model training.
A statistical study is conducted to evaluate the eficacy
of each imputation strategy following the imputing of
missing data. By comparing the imputed data with the
original data, the Mean Absolute Error (MAE) and Root Mean
Squared Error (RMSE) ofer insights on both the imputed
values precision and fluctuation. Whereas RMSE is
susceptible to outliers and assigns greater weight to higher errors,
MAE calculates the average size of prediction errors. These
measures aid in assessing how efectively the imputation
techniques maintain the distribution and structure of the
underlying data [
When analyzing the relationship between soil and the environment over time, it is essential to choose suitable models that can accurately represent the intricate dynamics and inter-dependencies included in the data. The model selection procedure was guided by the need to achieve a balance between predicted accuracy, interpretability, and the capability to handle many types of data patterns. The following models—Random Forest, Linear Regression, Simple Neural Network, Deep Neural Network, and Convolutional Network—were chosen because of their suitability for training time-series data and are described in section (2.2).
The pre-processed data obtained through imputation in subsection (4.2) and EDA in subsection (4.1) is used as training and validation dataset for further training Models.
The outcomes shown in subsections (5.4) analyze the eficacy of ML and DL models when utilized with real-world datasets. The findings suggest that both ML and DL models have subpar performance.
The inadequate performance highlights the need for other methods to improve the accuracy and resilience of the model. An encouraging strategy is the use of Generative Adversarial Networks as explained in subsection (2.2).
The procedures for data processing, GAN model training, and assessment are outlined in subsection (2.2). The primary objective is to produce artificial data samples using GANs, using diverse input characteristics obtained from several sources as training data. After generating the synthetic data, ML and deep learning DL models are trained and assessed using this data to verify its fidelity to the original dataset and results are described in section (5.6). The technique entails a meticulous selection of synthetic data, guided by statistical measurements, to guarantee its resemblance to the original data.
This section summarizes the results obtained from the analysis and modeling conducted in this research. The sections are arranged in a systematic manner to comprehensively address the following topics: exploration of the dataset, comparison of imputation methods, application and evaluation of machine learning and deep learning models on real data, generation and assessment of synthetic data using GANs, and comparative analysis of model performance on real versus synthetic data.
To validate the performance of the proposed approach,
we use three diferent datasets from the open data
provided by biodiversity exploratory information system
(BExIS)1) [
Dataset ds_set1.csv ds_set2.csv ds_set3.csv
Start Date 05-01-2008 03-01-2009 09-02-2020
End Date 06-09-2010 04-09-2011 04-09-2024
No. Features No. tuples 26 801 26 801 26 801
The climatic features specified in Table (2) serve as input variables for machine learning (ML) and deep learning (DL) models. These features encompass a range of meteorological parameters, including temperature, precipitation, wind attributes, and duration of sunshine. They ofer a thorough comprehension of the climate dynamics across various altitudes and temporal dimensions.
Soil parameter Ts_05 Ts_10 Ts_20 Ts_50 SM_10 SM_20
Description Soil temperature in 5 cm Soil temperature in 10 cm Soil temperature in 20 cm Soil temperature in 50 cm Soil moisture in 10 cm
Soil moisture in 20 cm
The soil features listed in Table (3) are used as output features for the ML and DL models. These features include soil temperature and soil moisture at various depths. By understanding the relationship between climate inputs and soil outputs, the models can predict soil conditions based on climatic variations, which is essential for applications in agriculture, environmental monitoring, and land management.
In the domain of actual data, sparsity is a prevalent problem, as shown by the abundance of NaN (Not a Number) values. The presence of these gaps in the data may often be ascribed to sensor malfunctions or other dificulties related to data collecting. The analysis, shown in Figure 2, indicates that the columns SD Olivier, WD, WV, and WV gust do not have data and should be excluded. Figure 3 demonstrates that a considerable proportion of rows have over 50% missing data.
Various imputation approaches may be used to address missing data. To assess the eficacy of these techniques, we may compare the mean absolute error (MAE) and the root mean square error (RMSE) values obtained from the original and imputed datasets. The performance of three imputation approaches, namely Cubic, K-nearest neighbors (KNN), and Gaussian, was evaluated in three datasets (set1, set2, set3), as shown in Figure 4. 1–7
The examination of various machine learning models on the dataset Figure 5 uncovers a consistent trend where the output feature SM_10 displays significantly higher MSE and MAE, as well as notably lower 2 scores in comparison to other target variables (Ts_05, Ts_10, Ts_20, Ts_50). This suggests that the SM_10 input feature has the most impact, leading to poor prediction performance and poor model ift. On the other hand, alternative target variables exhibit reduced error rates and increased 2 scores, indicating superior model performance and predictive accuracy. Despite experimenting with several hyperparameters in the Random Forest model, no substantial improvements were seen. Similar trends were observed in all three datasets. Similar results were also visible with the deep learning models on all three datasets as described by Figure 6.
The figures provided provide a comparison examination of performance metrics between the original data and the data produced by a GAN for three datasets (ds_set1, ds_set2, ds_set3). Each image consists of two subplots: one displaying the mean values and the other representing the standard deviations. When examining the subplots that compare the average values, it becomes evident that the produced data closely resembles the original data in virtually all aspects. This suggests that the GAN successfully captures the fundamental distribution of the original dataset. The strong agreement seen across several hyperparameter configurations, as shown in Figures 7, 8, and 9, highlights the GAN model’s ability to faithfully reproduce the average values of the original dataset. Similarly, the analysis of the subplots comparing the standard deviations shows that the generated data closely resemble the variability of the original data. However, there are some deviations in certain features, indicating that while the GAN performs well overall, reproducing specific features accurately may be more challenging.
The evaluation also considers the efects of several hyperparameters, which are represented by various markers and colors. These combinations consist of learning rates (0.0002, 0.003, 0.001) and loss functions (mean squared error, binary cross-entropy).
The assessment of deep learning models on three datasets (Figures 10, 11, and 12) reveals significant improvements when using GAN-generated synthetic data in comparison to the original data. Notable observations consist of: • Mean Squared Error: Models trained on synthetic data consistently provide decreased MSE values for all target variables, with significant enhancements seen for SM_10. • Mean Absolute Error: The use of GAN-generated data leads to a notable decrease in MAE, especially for the SM_10 target variable. • 2: Higher 2 scores show more explanatory power for models trained on synthetic data, particularly improving the prediction performance for SM_10.
The results emphasize the efectiveness of using synthetic data produced by GANs to improve the accuracy and reliability of models, especially in predicting the SM_10 variable.
Similar observations were observed using ML models trained on the synthetic data. The assessment of machine learning models in three datasets (Figures 13, 14, and 15) reveals substantial improvements when using synthetic data generated by GAN compared to the original data. Notable observations consist of: • Mean Squared Error: Models trained on synthetic data consistently exhibit decreased MSE values for
In this paper we investigated the analysis of time series datasets collected with the life science domain. We demonstrate the efect of KNN-based imputation techniques and show how KNN imputation consistently outperforms other methods, making it the optimal choice for addressing missing data in this scenario. The data generated by GANs exhibits a high degree of similarity to the original data, demonstrating GANs’ ability to accurately replicate the underlying distribution of the actual dataset (5.5). Models trained on GAN-generated data show superior performance compared to those trained on real data, as evidenced by significantly improved evaluation metrics, such as lower MSE and higher R2 scores. These improvements are observed in both machine learning and deep learning models across various datasets described in section (5.6). The findings of this paper have broad applicability in biological sciences and environmental research. This study enhances the resilience and precision of models predicting soil properties under diferent climatic conditions, facilitating more reliable agricultural planning and environmental monitoring.
Either: The author(s) have not employed any Generative AI tools.
During the preparation of this work, the author(s) used X-GPT-4 and Gramby in order to: Grammar and spelling check. Further, the author(s) used X-AI-IMG for figures 3 and 4 in order to: Generate images. After using these tool(s)/service(s), the author(s) reviewed and edited the content as needed and take(s) full responsibility for the publication’s content.