Decision support in contaminated site remediation with machine learning algorithms

doi:10.22167/2675-6528-202602835

Article

September 30, 2026

Decision support in contaminated site remediation with machine learning algorithms

Decision Support in Contaminated Area Remediation with Machine Learning Algorithms

Deivid Lucas dos Santos Migueleti; Aimée Shirozono

DOI: 10.22167/2675-6528-202602835

Article derived from a Final Course Work (TCC), with content based on the student’s original work and adapted to the editorial format of the E&S Magazine with the support of the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege for textual synthesis and organization.

Summary

The remediation of areas contaminated by hydrocarbons in aquifers subject to seasonal water level fluctuations demands quantitative instruments for planning and optimization, whose efficiency is limited by the trapping of petroleum derivatives. The work demonstrated the applicability of statistical machine learning models in predicting time series of hydraulic head in an area contaminated by aviation kerosene in the Paulínia-SP petrochemical hub, as evidence for decision support in remediation. Monthly hydraulic head records from monitoring wells and monthly accumulated rainfall over a 10-year period were used. Time series behavior analysis, variable engineering, and correlation analysis aided in the estimation of SARIMA and SARIMAX models. Models with adequate predictive capacity were selected and adjusted based on parsimony criteria and residual diagnostics. Time series modeling with exogenous variables improved model performance compared to SARIMA models. The model with 3-month accumulated precipitation showed a mean absolute error (MAE) of 0.1041 meters for a 12-month forecast, which was considered satisfactory. This constituted a relevant proof of concept to support remediation planning and long-term management of hydrocarbon-contaminated areas.

Keywords: Groundwater; Contamination; LNAPL; Rainfall; Soil.

1. Introduction

Groundwater represents 99% of the planet’s freshwater reserves, being a fundamental strategic resource for the climate adaptation of societies. In this context, public policies aimed at the protection and recovery of underground springs are of extreme relevance (UNESCO, 2022).

Soil and groundwater contamination, however, can severely compromise the use of this vital resource. In the State of São Paulo, the Information System for Contaminated and Rehabilitated Areas (SIACR), maintained by the Environmental Company of the State of São Paulo (CETESB), registered, in October 2025, 7,253 contaminated areas, of which 4,846, approximately 66.8%, were affected by petroleum-derived automotive fuels (CETESB, 2025).

Petroleum derivatives present in the subsurface in their pure form are known as LNAPL, an acronym for Light Non Aqueous Phase Liquid, or immiscible non-aqueous phase liquid. These complex mixtures can contain toxic substances, such as benzene and naphthalene, which contaminate groundwater and pose risks to human health (Alden et al. 2024).

The rehabilitation process of a contaminated area involves intervention steps to mitigate or reduce the risks of exposure to chemical substances of interest (CETESB, 2017). Information on the distribution of contaminants and their interaction with the physical environment and receptors is consolidated in the Conceptual Environmental Model (CEM) (Alden et al. 2024).

Remediation measures use technologies to treat the affected environment, removing contaminants or restricting their mobility (containment) (CETESB, 2017). The São Paulo State Decree number 59.263/2013 prioritizes measures that promote the removal and reduction of contaminant mass (ALESP, 2013). The most common remediation techniques for areas contaminated by petroleum derivatives are based on the hydraulic removal of LNAPL from the aquifer, including multiphase extraction, pumping and treatment, free-phase recovery, and hydraulic barrier (ITRC, 2023; CETESB, 2017).

Despite its wide application, the efficiency of hydraulic removal techniques is limited by several factors and must be optimized as the area’s MCA is updated (ITRC, 2023). One of the phenomena that restricts LNAPL recovery is trapping, where LNAPL becomes imprisoned in soil pores below the water table due to differences in capillary forces and interfacial tension between oil and water (Isler et al. 2018; Teramoto et al., 2020). This phenomenon was observed by Pede (2009) in an area of the Paulínia petrochemical complex.

In regions with well-defined rainfall regimes, LNAPL trapping can be intensified by seasonal water table fluctuation in response to precipitation (Teramoto et al., 2020). Climate change and extreme events, such as prolonged droughts and torrential rains, add complexity to the management of contaminated areas (UNESCO, 2022). Rainfall well below average intensifies trapping, hindering remediation by hydraulic methods.

Although mathematical and computational models for simulating and predicting aquifer water levels have existed for a long time, their application is restricted due to complexity and the need for specialized resources. Previous research has used Artificial Neural Networks, Support Vector Machines, and statistical models such as ARMA and ARIMA to predict water levels, some with the ability to anticipate up to four months in advance (Tao et al., 2022; Nayak et al. 2006). More recently, models based on the Transformer architecture have demonstrated superior predictive capability (Sun et al. 2024).

The need for robust and more accessible machine learning models, capable of predicting water level, is evident. Such models, combined with knowledge about the vertical distribution of LNAPL, can provide a crucial line of evidence for decision-making and negotiation with environmental agencies regarding the discontinuation of remediation efforts in contaminated areas where LNAPL trapping has occurred. The objective of this work was to demonstrate the applicability of at least one machine learning model in predicting water level time series in an LNAPL-contaminated area as a subsidy for decision-making about the continuity of remediation through hydraulic removal techniques.

2. Material and Methods

This study was characterized as a quantitative research, with a methodological approach focused on time series analysis. The objective was to demonstrate the applicability of machine learning models in predicting the water level in an area contaminated by LNAPL, providing support for decision-making on the continuity of remediation by hydraulic techniques. The research was carried out in an area contaminated by aviation kerosene in the petrochemical complex of Paulínia, São Paulo, where the leak was stopped in 2006 and the LNAPL trapping phenomenon was previously observed by Pede (2009).

The unit of analysis consisted of monthly hydraulic load records from 118 monitoring wells (January 2013 to September 2025) and 180 monthly accumulated rainfall records (January 2011 to December 2025). The data were collected from monitoring wells and a surface weather station at the same industrial plant. The use of this company-owned data was authorized by a letter of agreement. The raw data were initially processed in Microsoft PowerBI Desktop for cleaning and treatment, and the hydraulic load was calculated by subtracting the water level from the altimetric elevation of the wellhead.

The subsequent steps of treatment, filling in missing information, and data preparation were conducted in the Python language, in the Google Colaboratory environment (Google, 2026), using libraries such as Pandas, NumPy, Matplotlib, Seaborn, Scipy, Scikit-learn, Statsmodels, and pmdarima. To fill gaps in the time series, the `interpolate` function from Pandas was employed. For model estimation, the period from January 2016 to September 2025 was used, excluding the water crisis period of 2014-2015. From the original variables, six lagged hydraulic load variables (one to six months) and five precipitation variables based on the Standardized Precipitation Index (SPI) for one, two, three, six, and twelve-month scales were calculated, using the SPEI package (Vonk, 2025), according to McKee et al. (1993). Accumulated precipitation variables were created using the `shift` function from Pandas.

An exploratory clustering step of the hydraulic load records was performed, using the DBScan algorithm from the Scikit-Learn library (Ester et al., 1996), after standardizing the values with StandardScaler. The `eps` and `min_samples` parameters of DBScan were adjusted based on a k-neighbors graph. Exploratory data analysis included descriptive analyses, box-plot graphs, line and bar graphs, and a Pearson correlation matrix, visualized through a heatmap generated with the Seaborn package.

The decomposition of the hydraulic load time series was performed using the `seasonal_decompose` function from the Statsmodels library (Hyndman and Athanasopoulos, 2021). Autocorrelation and partial autocorrelation (ACF and PACF) were evaluated with the `plot_acf` and `plot_pacf` functions from the same library. The stationarity of the time series was verified through the Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests, using the `adfuller` and `kpss` functions from the `tsa` module of the Statsmodels library, at the five percent significance level (Morettin and Toloi, 2018). The hydraulic load series required two differencing steps to achieve stationarity.

For the estimation of the models, the `auto_arima` function from the pmdarima package was used, which minimizes the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) (Morettin and Toloi, 2018). Autoregressive Integrated Moving Average (ARIMA) models and their seasonal extensions (SARIMA), as well as SARIMAX models, which incorporate explanatory exogenous variables (Nelson, 1973), were estimated. The adjustment of the selected models considered only exogenous variables with statistically significant coefficients. Performance was evaluated by predictions of hydraulic load for twelve months, using the functions `get_forecast`, `predicted_mean`, and `conf_int`. The error metrics used were Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) (Hyndman and Athanasopoulos, 2021).

The diagnosis of the residuals was performed to verify their representativeness, including the generation of standardized residual plots, histograms, Q-Q plots, and correlograms. Formal verification of the residual characteristics included the Ljung-Box test for autocorrelation, and the Jarque-Bera, Shapiro-Wilk, and Heteroskedasticity tests for adherence to normality and homoscedasticity (Fávero and Belfiore, 2024).

3. Results and Discussion

The analysis of data from the area contaminated by aviation kerosene in Paulínia-SP revealed hydrological and pluviometric patterns that directly influence the behavior of the aquifer’s hydraulic head, providing essential subsidies for the application of predictive models. Initially, the exploration of hydraulic head records from monitoring wells allowed the identification of spatial groupings, although detailed cluster information was not incorporated into subsequent analyses to focus on the general behavior of the water table in the area. The spatial distribution of monitoring wells, as observed, showed a grouping that followed the direction of groundwater flow, from northeast to southwest, with some wells classified as “outliers” due to discrepant measurements.

The time series of hydraulic head, composed of 106 water level records after interpolating 9.40% of missing data, showed a median of 589.092 meters and a mean of 589.093 meters. Linear interpolation was applied to fill gaps, a recognized methodological limitation, but whose impact was considered limited by the nature of the aquifer’s response to precipitation and the short duration of measurement failures. The monthly distribution of hydraulic head revealed a clear seasonal pattern, with peaks in March and April and minima in October and November, indicating seasonal influence on the water level.

The monthly accumulated precipitation, with 117 records, showed a median of 75.000 millimeters and an average of 99.150 millimeters, with a slight right skew. The monthly precipitation distribution confirmed the expected seasonal pattern for the region, characterized by rainy summers and dry winters. This seasonality is a crucial factor for understanding the aquifer recharge dynamics and, consequently, the hydraulic head fluctuations, which are fundamental for planning the remediation of areas contaminated by LNAPL.

Correlation between variables

The evaluation of the correlation between the original and constructed variables, including hydraulic load, monthly rainfall, lagged hydraulic load variables, and standardized precipitation indices (SPI), revealed significant relationships. A 41% correlation was observed between the hydraulic load values and the 6-month SPI (spi6), 41% with the accumulated precipitation over 2 months (pluv_acum2), and 54% with the accumulated precipitation over 3 months (pluv_acum3). These results are consistent with the understanding that the aquifer’s response to precipitation is not immediate but rather delayed, due to the processes of infiltration and water recharge in the soil.

The analysis of hydraulic load time series together with monthly accumulated rainfall, the 6-month SPI, and 3-month accumulated rainfall, visually demonstrated that the hydraulic load tends to increase in periods of higher rainfall, confirming the dependency relationship. This observation is vital for modeling, as the inclusion of exogenous variables that capture this temporal lag can significantly enhance the predictive capacity of the models, aligning with the literature that highlights the importance of rainfall in aquifer dynamics (Teramoto et al., 2020).

Analysis of the decomposition of the response variable

The decomposition of the hydraulic load time series, performed by the additive method, clearly identified the presence of trend, seasonality, and residual components. The observed trend in hydraulic load over time reflects long-term changes in the hydrogeological system, while seasonality captures the annual patterns of water level oscillation, strongly influenced by the region’s rainfall regime. The identification of these components is a fundamental step for time series model estimation, as it allows models to capture and explain these behavioral patterns (Hyndman and Athanasopoulos, 2021).

The Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots of the response variable confirmed the existence of trend and seasonality in the data. The peaks in the PACF indicated that the time series of hydraulic load could be modeled as an autoregressive process of order 2 or 3. This information was crucial for choosing the initial parameters of the `auto_arima` function from the `pmdarima` package, guiding the structure of the SARIMA and SARIMAX models to be estimated.

Time series stationarity tests

The stationarity of the hydraulic load time series was assessed using the Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests, at a significance level of 5%. The ADF test initially indicated non-stationarity of the original series, with a p-value of 0.286026, which is higher than the significance level. After one differencing (d=1), the p-value decreased to 0.129114, still indicating non-stationarity. Only after two differencings (d=2) did the p-value reach 7.300095e-13, confirming the stationarity of the series.

The KPSS test, which assumes stationarity as the null hypothesis, also corroborated the need for differencing. With an initial p-value of 0.100000, the test did not reject the null hypothesis of stationarity, but the combination with the ADF test and the graphical analysis of the twice-differenced series, which showed stable behavior around the mean, confirmed the adequacy of the parameter d=2 for modeling. Stationarity is a prerequisite for applying ARIMA and SARIMA models, ensuring that the mean and variance of the series remain constant over time (Morettin and Toloi, 2018).

Estimation and fitting of the models

The estimation of SARIMA and SARIMAX models was performed using the `auto_arima` function from the `pmdarima` package, seeking to minimize the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), which assess model parsimony and goodness of fit. Initially, SARIMA models, without exogenous variables, were estimated for comparison. The SARIMA(1,2,0)(2,0,1)[12] model showed an AIC of -15,377, while the SARIMA(3,2,0)(2,0,0)[12] had an AIC of -18,023, indicating a better fit.

The introduction of exogenous variables in SARIMAX models demonstrated a significant improvement in performance. The SARIMAX(0,2,3)(2,0,0)[12] model, with all exogenous variables from Table 1, achieved an AIC of -38,882, a substantial drop compared to pure SARIMA models. However, this model presented autocorrelation in the residuals, as indicated by the Ljung-Box test (p<0.05), suggesting that not all information was adequately captured.

The analysis of the coefficients of the SARIMAX models with all exogenous variables revealed that some SPI variables and `pluv_acum2in`, as well as certain autoregressive and moving-average terms, were not statistically significant. This observation can be attributed to multicollinearity among the predictor variables, a common phenomenon in time series (Fávero and Belfiore, 2024). Models with exogenous variables such as `pluv_acum3` (accumulated precipitation in the previous 3 months) and `spi1` (1-month standardized precipitation index) showed the lowest errors, which is consistent with the aquifer’s lagged response to precipitation.

The SARIMAX(2,2,3)(2,0,1)[12] model with the `pluv_acum3` variable stood out with a Mean Absolute Error (MAE) of 0.1041 meters for a 12-month forecast, considered satisfactory for the study’s objective. Another notable model was SARIMAX(2,2,2)(2,0,0)[12] with `spi1`, which achieved an AIC of -52.144 and BIC of -31.066, indicating a robust fit. The final selection of models for fitting and residual diagnostics was based on the AIC, BIC criteria, and the Ljung-Box statistic, prioritizing parsimony and the statistical significance of the coefficients.

Diagnosis of residuals

The diagnosis of the residuals of the adjusted models was performed to verify their representativeness, as recommended by Morettin and Toloi (2018). The evaluation of the scatter plots of the standardized residuals of all adjusted models revealed a random distribution around the zero mean over time, indicating homoscedasticity. This characteristic was formally confirmed by the Heteroskedasticity statistic, which, according to the documentation of the `Statsmodels` library, is analogous to the Goldfeld-Quandt test, whose null hypothesis is the non-heteroscedasticity of the residuals.

However, the Quantile-Quantile plots indicated that the distribution of residuals across all models did not fully adhere to normality, possibly due to the presence of tail values. This non-adherence was corroborated by the Jarque-Bera and Shapiro-Wilk statistical tests, which presented p-values below 0.05 for all adjusted models. Although the non-normality of residuals does not invalidate the models, it may have implications for the determination of confidence intervals (Fávero and Belfiore, 2024). The transformation of the dependent variable, such as the Box-Cox transformation, could mitigate this problem, but it was not explored in the present work.

In summary, the results demonstrated the applicability of statistical machine learning models in predicting the hydraulic load in a contaminated area, with emphasis on SARIMAX models that incorporate exogenous precipitation variables. The ability to predict the water level with a mean absolute error of 0.1041 meters, using accumulated precipitation over 3 months, represents a robust proof of concept. This quantitative evidence is crucial for supporting remediation planning and long-term management of hydrocarbon-contaminated areas, especially in contexts of LNAPL trapping and seasonal water level fluctuations.

4. Conclusion

This work demonstrated the applicability of machine learning models in predicting hydraulic head time series in an area contaminated by aviation kerosene, providing a basis for decision-making regarding the continuation of remediation by hydraulic techniques. It was observed that the aquifer’s hydraulic head exhibits clear seasonal patterns and a lagged response to rainfall. The estimation of SARIMAX models, which incorporated exogenous precipitation variables, significantly improved predictive capacity compared to pure SARIMA models. A SARIMAX model, using three-month accumulated precipitation, achieved a mean absolute error of 0.1041 meters for a twelve-month forecast, which is considered satisfactory. This result established a robust proof of concept, offering a crucial quantitative line of evidence for remediation planning and long-term management of hydrocarbon-contaminated sites, especially in scenarios of LNAPL trapping and seasonal water table fluctuations.

Despite the relevance of the findings, the study presented limitations, such as the linear interpolation of 9.40% of the missing data, whose impact on the models’ sensitivity was not evaluated, and the lack of full adherence of the residuals to normality, which may influence the determination of confidence intervals. Multicollinearity was also observed among some exogenous SPI variables, resulting in non-significant coefficients for certain variables. For future studies, it is suggested to deepen the investigation into multicollinearity and the use of clusters identified by the DBScan algorithm in model estimation. It is also recommended to test approaches such as the Global Forecasting Method and explore more advanced models, such as LSTM, NeuralProphet, and those based on Transformers, to capture non-linearities and improve predictive capacity in specific piezometric wells.

Bibliographic References

Alden, D.F.; García-Rincón, J.; Rivett, M.O.; Wealthall, G.P.; Thomson, N.R. 2024. Complexities of Petroleum Hydrocarbon Contaminated Sites. p. 1-19. In: García-Rincón, J.; Gatsios, E.; Lenhard, R.J.; Atekwana, E.A.; Naidu, R. Advances in the Characterisation and Remediation of Sites Contaminated with Petroleum Hydrocarbons. Environmental Contamination Remediation and Management. Springer, Cham.

Companhia Ambiental do Estado de São Paulo [CET

Companhia Ambiental do Estado de São Paulo [CETESB], 2025. Sistema de Áreas Contaminadas e Reabilitadas. Relatório de Áreas Contaminadas e Reabilitadas no Estado de São Paulo. Disponível em: < https://sigam.ambiente.sp.gov.br/sigam3/Default.aspx?idPagina=17653>. Acesso em: 10 out. 2025.

Organização das Nações Unidas para a Educação, a Ciência e a Cultura [UNESCO]. 2022. UN World Water Development Report 2022: Groundwater: Making the invisible visible. Disponível em: <https://www.unwater.org/publications/un-world-water-development-report-2022>. Acesso em: 12 out. 2025.

Article originating from the Final Course Work of the Specialization in Data Science and Analytics of the MBA USP/Esalq

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Why, for certain students, does school manage to offer so few experiences of achievement? The World Cup is over. Brazil left the competition, the school holidays came, the final was played and the football calendar moved on. It would be reasonable to imagine that, with the end of the tournament and the return to classes, […] would also begin

Energy Transition

September 30, 2026

Renewable energies: the Brazilian advantage

Brazil needs to invest in the capacity to transform natural resources into innovation, productivity, and development. Talking about energy is talking about development. Behind industry, transportation, services, cities, and increasingly artificial intelligence, there is an energy infrastructure that sustains the economy. Understanding a country’s energy matrix is understanding […]

Data Analysis

September 30, 2026

When the data changes, has reality changed?

An analysis of almost 20 million teaching positions-year between 2020 and 2025 shows how changes in population composition and record production can alter the interpretation of historical series Between 2020 and 2025, the number of formal teaching employment positions identified in RAIS (Annual Relation of Social Information), from the Ministry of Labor, […]

Artificial Intelligence

September 28, 2026

Artificial intelligence and the trends

Why do so many people join in on the fun with ChatGPT? I was never the trend person. I still am not. Some pass through my timeline, I see them, I find them interesting but not to the point of trying them out. This was the case, inclusive, with some recent waves of images made by artificial intelligence, like the 80s trend, the one that […]

Tax Management

September 01, 2026

Tax reform and estate planning in the countryside

Efficiency in managing taxes facilitates governance and allows for the organization of family succession. Rural wealth organization has become a strategic measure in light of the tax reform in Brazil, which came into effect this year. The planning of activities and rural wealth management, through tools such as the transition from an Individual to […]

Renata de Gaspari Valdejão Almeida

Artificial Intelligence

August 05, 2026

Inside the company and off the radar: the risks of Shadow AI

According to Microsoft, 75% of professionals already use artificial intelligence at work without company approval. The use of generative artificial intelligence is already part of the corporate routine. Tools like ChatGPT, Copilot, and other platforms help professionals produce texts, analyze data, create presentations, and accelerate tasks in practically all sectors. But this advance […]

Renata de Gaspari Valdejão Almeida

Strategy

July 02, 2026

The future as a competence

Academic interest reinforces the projection of scenarios as an essential tool for decision-making. While some companies are still trying to get the next step right, others are already working with multiple possible futures — and preparing for all of them. But after all, what does it mean to “think about the future”? Is it a trend, a strategy, or a new competence that […]

Renata de Gaspari Valdejão Almeida

Innovation

Technology

May 21, 2026

Agro SP Corridor: innovation, institutional articulation and future

Pecege commands the executive operation of the 400 km ecosystem activities that became known as the Brazilian agro Silicon Valley In 2025, in addition to taking over the management of the Piracicaba Technology Park, the Pecege Institute was also designated to command the executive operation of the State Agribusiness Innovation Corridor activities, the so-called […]

Education

April 27, 2026

How to turn a TCC into a business

New management of the Piracicaba Technology Park offers consulting to MBA graduates The new project director of the Piracicaba Technology Park (PTP), Alexandre Barreto, often says that one of the greatest rewards he has ever had in life was seeing his MBA USP/Esalq students structure companies from their final projects […]

Technology

March 20, 2026

The human repertoire in the age of AI

Critical thinking and creativity are irreplaceable in the work of a marketing professional Artificial Intelligence (AI) is capable of incredible feats, but it does not yet possess the judgment, empathy, intuition, and strategic creativity necessary to do the job of a good marketing professional. In this area, it is important to know how to use the tool to automate […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

February 23, 2026

ESG as a growth strategy in a cooperative in RS

Santa Clara balances social and environmental responsibility with innovation Founded in 1911 by Italian immigrants in Carlos Barbosa, in the Serra Gaúcha region, Cooperativa Santa Clara is an example that it is possible to balance economic growth with social and environmental responsibility. From a small cheese production company, the organization has evolved into a […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

January 22, 2026

Brazil free of Foot-and-Mouth Disease: what changes for Brazilian beef?

With a herd of over 200 million head of cattle, the country is currently the leader in exports. Brazil recently received, for the first time, the certification of country free of foot -and-mouth disease without vaccination, conferred by the World Organisation for Animal Health (WOAH). The seal strengthens the country’s position as a reliable supplier of meat for the […]

Renata de Gaspari Valdejão Almeida

Innovation

December 09, 2025

Innovation: the new cycle of the Piracicaba Technology Park

Instituto Pecege assumes management, betting on the creation of new businesses Starting in 2026, Instituto Pecege will assume the management of the Piracicaba Technology Park (PTP). Among its responsibilities will be the administration, operation, maintenance, and expansion of the park, in addition to fostering activities in innovation, entrepreneurship, and science and technology. The choice was made […]

Renata de Gaspari Valdejão Almeida

July 06, 2026

Large-Scale Architecture: DSR-SCRUM Change Management

Mônica Alessandra Guerios; Maria do Carmo Assis Todorov Abstract generated by ResumeAI, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. Architecture and urbanism are configured as essential bases in an increasingly complex and challenging urban space, where the intrinsic connection between social, environmental, and technological issues […]