Real‑Time and Explainable Hybrid Machine Learning Framework

for Multivariate Prediction and Classification of Water

Contamination Using Environmental and Temporal Features

Er. Prafull Kothari

, S. Sagaya Mary

, Jyotsna Pandit

, D. B. K. Kamesh

Priyadharshini K.

and Syed Hauider Abbas

Civil Engineering, Institute of Engineering and Technology, Mohanlal Sukhadia University, Udaipur (Raj.)‑313001,

Rajasthan, India

Department of Electronics and Communication Engineering, J.J. College of Engineering and Technology, Tiruchirappalli,

Tamil Nadu, India

Department of Chemistry, Manav Rachna University, Sector‑43, Delhi‑Surajkund Road, Faridabad, Haryana 121004,

India

Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad‑500043, Telangana, India

Department of ECE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Department of Computer Science & Engineering, Integral University, Lucknow, Uttar Pradesh, India

Keywords: Water Contamination, Hybrid Machine Learning, Explainable AI, Environmental Features, Real‑Time

Prediction.

Abstract: Accurate prediction and categorization of water contamination is important to protect public health and

promote sustainable water resource. In this research, we introduce a real-time, explainable hybrid

supplemented machine learning approach, weaving deep learning and classical classifiers, as well as

exploiting a broad variety of environmental and temporal features, to predict and categorize the pollution level

of water. While other models are based on small datasets or do not have multimodal or reasoning capabilities,

the proposed model uses a combined CNN–LSTM–XGBoost model with an explainability layer using SHAP

values, in order to provide interpretable decisions. The model involves chemical parameters and context

factors including meteorological data, seasonal pattern, and human activities to increase accuracy and

generality among diverse water bodies. Real-time integration of sensors and quantification of uncertainty

enhance the potential of the model to provide timely decision support and accurate alerts regarding

contamination risk. This article presents a solution to next generation water quality intelligence systems which

is both robust and scalable as well as interpretable.

1 INTRODUCTION

Clean and safe water is a basic requirement, however,

urbanization, industrialization and climate variation

have caused widespread water contamination

worldwide. Conventional monitoring methods of

water based on manually collecting water samples

followed by complicated, time-consuming, costly and

non-continuous laboratory analysis. With the

development of the sensor technologies and

environmental databases, machine-learning-based

predictive analytics changed the way of water quality

monitoring.

However, the vast majority of alternative machine

learning models only concentrate on chemical

parameters and need geographic data on which to

train, and are therefore not generalizable. What’s

more, these models can be black boxes the process by

which they make predictions isn’t intuitive, which

can make it hard for environmental agencies to

comprehend and trust the outcomes. In practice,

decision makers also require reasoning support that

not only predicts what would occur, but also tells

why.

In this paper, a new hybrid ML model,

interpretable in real-time, providing timely results

and utilizing learning to solve environmental

544

Kothari, E. P., Mary, S. S., Pandit, J., Kamesh, D. B. K., K., P. and Abbas, S. H.

Real-Time and Explainable Hybrid Machine Learning Framework for Multivariate Prediction and Classiﬁcation of Water Contamination Using Environmental and Temporal Features.

DOI: 10.5220/0013869000004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages

544-551

ISBN: 978-989-758-777-1

problem, is proposed for water contamination

classification and prediction by combining deep

neural architectures with conventional ones. Through

the integration of abundant of environmental and

meteorological data as well as temporal features, our

proposed model not only presents better performance,

but also provides interpretability through explainable

AI interpretable components. We bridge the worlds

of data science and environmental monitoring by

providing a scalable, smart product designed to work

off the shelf in a wide variety of aquatic

environments. In this way, it offers the potential for

proactive water-resource management, allowing the

system to manage contamination threats before they

happen and serving the sustainability aims for public

health and environmental interests.

1.1 Problem Statement

Yet, the prevalence of machine learning in pollution

monitoring, especially the prediction and

classification of water contamination, is restricted by

a number of important limitations. The majority of

databases are built based on independent chemical

parameters without the implementation of real-time

monitoring, also, wider environmental and temporal

dynamics are often overlooked. Moreover, a majority

of the existing models are still region-based and are

developed based on unbalanced or small datasets,

which makes these models generalizable and

robustness for different geographies questionable.

There's also a critical issue with interpreting

machine learning prediction. Black-box models while

accurate frequently offer no explanation for their

outputs, leaving them less useful for decision-makers,

regulators and environmental stakeholders who need

confidence and understanding in automated systems.

And finally, it is rare for the current models to

include uncertainty quantification or predictive

confidence, which presents risks in the decision

making, especially in the public health-sensitive

setting.

In this context, the present study overcomes those

gaps by developing a real-time, hybrid and

explainable machine learning framework

(RMILWQ) to predict the level of the water

contamination and to classify it. The goal of this

study is to provide a robust, interpretable and scalable

solution for intelligent water quality monitoring in

dynamic, real-world scenarios by leveraging hybrid

deep learning and classical ML models and a rich set

of environmental and temporal characteristics.

2 LITERATURE SURVEY

In recent years there has been an increasing interest

in using machine learning (ML) for environmental

monitoring, especially for predicting and classifying

water contamination. Several studies have proved

that traditional and deep learning models are effective

in water quality prediction tasks, but most of them are

not generalizable, able to generalize the findings to

other areas, incapable of real-time deployment and

model interpretation.

Because of its simplicity and interpretability,

traditional approaches including Support Vector

Machines (SVM), Random Forests (RF) and

Decision Trees have been widely applied for the

classification of water quality (Abba et al., 2021;

Azrour et al., 2021). However, they commonly fail

in handling nonlinear and highdimensional data

character of environmental systems. To mitigate this,

some latest studies have employed deep learning

approaches such as the CNN (Convolutional Neural

Network) and LSTM (Long Short-Term Memory)

networks, which proved to be powerful in modeling

complex temporal dependencies (Liu et al., 2020; Fu

et al., 2021).

Hybrid approaches also have been developed to

combine traditional ML and DL to the

complementary benefits. For example, Khosravi et al.

(2025) have proposed a deep hybrid model based on

tree-based classifiers and neural networks in which

the accurate prediction of water quality was achieved.

Similarly, Elshewey et al. (2025) proposed a stacked

hybrid model which performed better than the basic

classifier for potability classification with the help of

feature selection methods. Guo et al. (2024)

developed a hybrid CNN–LSTM architecture to

predict water quality in real time with a distinct

advantage to achieve more accurate temporal

predictions.

However, a lot of such models are trained using

geographically limited data, compromising their

ability to generalize'' (Grbčić et al., 2021; Khullar &

Singh, 2022). And a lack of interpretability in such

systems complicates practical deployment. In a

critique of the UCT model, Saltelli et al (2021)

criticized the use of black-box models, stating that its

interpretation remains unknowable, especially in

water prediction systems for seasonal flow (Bernardo

et al, 2019), but recent studies by Paneru & Paneru

(2024) have used Explainable AI (XAI) methods like

LIME to interpret model prediction. However, these

methods are still at an embryonic stage and we have

not covered all the water quality frameworks.

Real-Time and Explainable Hybrid Machine Learning Framework for Multivariate Prediction and Classiﬁcation of Water Contamination

Using Environmental and Temporal Features

545

In order to improve prediction sensitivity, Shafi et

al. (2025) and Swain et al. (2025) argue that it is

important to include additional environmental aspects

such as meteorological information, hydrologic

cycles, and human behavior. This is consistent with

what is observed in Najah et al. (2021), and

environmental context improved the predictions

considerably. Moreover, instant and continuous data

from IoT enabled systems are also under testing for

accurate predictions, however very little research is

successful to implement such systems in an automatic

loop (Ahmed et al., 2019; Aldhyani et al., 2020).

The problem of unreliable datasets is yet another

often ignored obstacle. Such work, like for example

Subudhi et al. (2025) have used evolutionary

algorithms as opposed to their others have tried out

oversampling techniques artificially constructed to

balance the class distribution. However, the

incorporation of uncertainty quantification is scarce,

and hardly any study presents confidence intervals or

predictive distributions along with their results

(Barzegar et al., 2020).

In summary, although existing literature provides

a strong base for using machine learning to predict

water quality, the increased interest in developing

hybrid (machine learning-physical models),

interpretable, and real-time models that generalize

well across regions, use a varied set of environmental

predictors, and transforms model output into

actionable, quantifiable information points to a

critical need.

3 METHODOLOGY

The proposed study will adopt a structured and data-

driven method to establish a real-time interpretable

hybrid machine learning based predictive and

classification model for contamination levels in

water. It comprises data acquisition, preprocessing,

feature engineering, model building, explainability

integration, and deployment, making it a complete

and scalable solution. Figure 1 shows the flowchart

of the Proposed Hybrid Machine Learning

Framework for Water Contamination Prediction and

Classification. Figure 2 Correlation heatmap showing

associations between the physicochemical and

environmental descriptors applied in the prediction of

contamination.

Table 1 tabulates the environmental

and temporal features used.

Figure 1: Flowchart of the proposed hybrid machine learning framework for water contamination prediction and classification.

Data

Acquisition

Collect

physicochemical

parameters

Gather

contextual

features

Data

Preprocessing

Missing value

treatment

Normalization

and encoding

SMOTE for class

balancing

Feature

Engineering

Select important

environmental

and temporal

features

Model

Construction

CNN for spatial

feature

extraction

LSTM for

temporal

sequence

learning

XGBoost for final

classification/pr

ediction

Model Fusion

Combine CNN-

LSTM outputs

with XGBoost

Explainability

Layer

SHAP/LIME for

feature

importance

Uncertainty

Quantification

Monte Carlo

Dropout /

Bayesian

estimation

Real-Time

Deployment

Flask API +

Dashboard

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Figure 2: Feature correlation heatmap.

Table 1: Environmental and temporal features used.

Feature Type Feature Name Unit Source / Sensor Type

Physicochemical pH - pH Sensor

Turbidity NTU Optical Sensor

Dissolved Oxygen (DO) mg/L DO Sensor

Nitrate Concentration mg/L Water Quality Probe

Environmental Rainfall mm Weather API / Rain Gauge

Temperature °C Weather Station / Sensor

Temporal Sampling Month - Timestamp

Season - Derived from Timestamp

Anthropogenic Industrial Discharge Index Score (0–10) Government Dataset

The first phase consists on collecting different

water quality databases from various open-source

databases, governmental monitoring agencies and

real-time IoT sensor feeds. These variety of

parameters are physicochemical parameters that

consist of different nitrates, BOD, heavy metals, pH,

DO, turbidity and EC. Furthermore, contextual

environmental parameters (e.g., temperature, rainfall,

land use, and season) are incorporated to improve the

robustness and applicability of the prediction model.

Pre-processing of data is used to treat missing and

noisy data and inconsistencies among data sources.

Numeric imputation techniques and scaling methods

such as Min-Max Scaling and Z-score standardization

are used to have consistently scaled features.

Categorical columns, if any, are processed via one-

hot encoding or label encoding, as appropriate. To

deal with class imbalance (which is a typical issue in

contamination classification), we use the Synthetic

Minority Over sampling algorithm (SMOTE) to even

out the data distribution of training set among

different contamination levels.

Dataset Summary is

given in Table 2.

Real-Time and Explainable Hybrid Machine Learning Framework for Multivariate Prediction and Classiﬁcation of Water Contamination

Using Environmental and Temporal Features

547

Table 2: Dataset summary.

Dataset

Name

Regi

No.

Rec

ords

No.

Feat

ures

Contami

nation

Labels

Missi

Data

(%)

Dataset

(Yamun

)

Indi

2,00

12 3 4.1

Dataset

(Thame

)

1,80

10 3 2.5

Dataset

(Local

IoT)

Sim

ulate

Real

Tim

3,50

15 3 1.2

The main structure is a multi-stage hybrid model that

combines traditional machine learning and deep

learning to work with. That is, the proposed model

mainly includes a CNN layer for spatial features, a

LSTM layer for temporal dynamics, and XGBoost for

the final prediction, which further improve the

classification performance. The CNN captures the

structured environmental features and the LSTM

captures the temporal dependencies of water quality

over time. Output from this ensemble proportion is

combined with other from RF ensemble and again

sent to the XGBoost layer for robust ensemble which

can do regression and classification.

Explainable AI (XAI) methods like SHapley

Additive exPlanations (SHAP) and Local

Interpretable Model-Agnostic Explanations (LIME)

are then applied to make the model more interpretable

beyond prediction. With these tools, domain experts

and regulatory authorities can decide upon the

influence of each environmental factor on the

predicted contamination levels, thus, the proposed

framework becomes more transparent and trustful.

In addition, we present uncertainty quantification

with Monte Carlo dropout and Bayesian inference

methods, and provide the confidence interval for

predictions. This is important not only to make the

model is accurate also to its uncertainty, which is

important for risk-averse decision-making in water

resources.

The ultimate model is deployed in a simulated real

time environment in Python and TensorFlow with a

Flask REST API that communicates with a

dashboard implemented in Plotly Dash. The

dashboard offers interactive visualizations of the

water quality trend, contamination alerts, and model

guidance to end-users for them to track contamination

level and prompt to act.

This approach helps guarantee that the system we

propose would be data-efficient and high-performing

with well-interpretation, scalability, and real-time

applicability.

4 RESULTS AND DISCUSSION

The developed hybrid machine learning framework

was tested with the integration of historical datasets

and real-time sensor measurements across varied

geographical water bodies. Figure 3 and table 3

shows the comparison of accuracy scores of

individual and hybrid machine learning models. The

experiments are performed in Python using TF and

scikit-learn libraries, XGBoost, and are aided by a

cloud-based simulation of the real-time data flow for

testing.

Figure 3: Model accuracy comparison (bar chart).

Table 3: Performance comparison of models.

Model

Accuracy

(%)

Precisi

Recall

F1-

Score

AUC-

ROC

Random

Fores

84.2 0.81 0.82 0.81 0.88

LSTM 88.7 0.87 0.88 0.87 0.91

CNN-

LSTM

91.1 0.89 0.90 0.89 0.94

Hybrid

(CNN+L

STM+X

GB)

94.6 0.93 0.94 0.93 0.97

The first level of investigation aimed at the

performance of a single model in the hybrid

architecture. The traditional classifiers such as

Random Forest, SVM and Decision Trees produced

reasonable accuracy (78%–84%) but fell short in

terms of capturing temporal dependencies and non-

linear interaction of variables. Figure 4 ROC curve

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

548

for model sensitivity and specificity. Deep learning

models such as CNN and LSTM outperformed

standalone (85%–89%), especially in detecting

seasonality patterns and contamination peaks. But

when incorporated into the CNN-LSTM-XGBoost

hybrid model, the accuracy jumped to 94.6%, and

significant boosts were observed in the precision,

recall and F1-score among all the contamination

categories.

Table 4 tabulates the SHAP-Based

Feature Importance Scores.

Figure 4: ROC curve for model classification.

Table 4: Shap-based feature importance scores.

Feature

SHAP Value

(Avg)

Importance

Ran

Dissolved

0.241 1

Rainfall 0.213 2

Turbidity 0.197 3

Temperature 0.134 4

pH 0.121 5

The confusion matrix demonstrated that the hybrid

model, for border contamination cases, reduced the

level of misclassification considerably, particularly

“moderate contamination” and “highly

contaminated” classes. This means that the hybrid

classifier does not only increase predictive power but

also increases granularity in the classification, which

is an essential feature in the context of environmental

agencies releasing risk-level warnings.

Uncertainty

Estimation – Confidence Intervals are shown in Table

Table 5: Uncertainty estimation – Confidence intervals.

Predicted Class

Mean

Probabilit

Confidence

Interval (95%)

Safe 0.92 [0.89, 0.95]

Moderate

Contamination

0.87 [0.83, 0.91]

High

Contamination

0.94 [0.91, 0.97]

Investigating AUC-ROC in comparative perspective,

the hybrid model (AUC = 0.97) outperformed

combined algorithms, confirming strong

discriminative potential for each target class. The

regression task of contamination index prediction in

MSE was minimized to 0.011, which also indicates

the precise numeric prediction in addition to the

categorical classification ability of the hybrid model.

The value adds here included explainable AI

(XAI). Regarding the SHAP model, the major

predictors were features related to the amount of DO,

turbidity, and rainfall, which were also consistent

with domain knowledge. Temporal factors,

including the month samples were collected and lags

in rainfall patterns, were further emphasized, as a

demonstration of the importance of inclusion of time-

dependent variables. The explainable piece made the

model outputs interpretable and actionable and was

something that could be relied on and understood by

an environmental expert.

Uncertainty estimation was a significant aspect

of the system evaluation, particularly beneficial for

circumstances where contamination measurements

approached classification limits. The confidence

estimated using Monte Carlo dropout was within

±2.3% deviation for decision making from

confronting stakeholders whether they need to take

urgent action or keep observing the systems. This

trust in the model, while using scoring for

confidence, made of the model much more than a

black-box predictor, but a decision support system.

The model was also tested in real-time simulator

to verify its practicality. Sensor feeds were

continuously processed and predictions provided by

an API built on Flask to a dashboard. Figure 5 shows

the simulated variation of the real-time CI across a 24

hours period; contamination alerts were emitted

dynamically when there was a breach of threholsd

values and in the dashboard, the trends visualizations,

the prediction explanations and the feature

importance map were updated. This proved the

practicality of the framework in field deployments

which require real-time responsiveness.

Figure 5: Real-time contamination monitoring graph.

Real-Time and Explainable Hybrid Machine Learning Framework for Multivariate Prediction and Classiﬁcation of Water Contamination

Using Environmental and Temporal Features

549

Furthermore, a cross-region dataset validation was

carried out to evaluate generalizability of the model.

The average accuracy rate of the hybrid model was

higher than 91% for the three diverse water basins

(urban, agricultural, and industrial), indicating its

capability to accommodate different environmental

status and contamination uncertainty. Its performance

was even shown to be durable to noise and missing

values, due to the robust pre-processing and feature

imputation methods.

In conclusion, the findings suggest that the hybrid

CNN-LSTM-XGBoost model, modified by the

explainable AI and real-time structure, outperforms

in predictive performance and operational

applicability. It does not only mitigate the drawbacks

of existing models, but also a scalable and intelligent

framework for preemptive water contamination

control is proposed. This renders it a useful

instrument for environmental authorities, policy

makers and smart city infrastructures in their attempts

to safeguard water quality and human health.

5 CONCLUSIONS

This study develops a new intelligent hybrid machine

learning model for online measurement of water

contamination level prediction and classification

based on diverse environmental and temporal

features. The model integrates the merits of CNN,

LSTM and XGBoost, and performs well not only in

high accuracy but also in robustness heterogeneity of

geographical and contamination situation.

Incorporating explainable AI technologies such as

SHAP ensure outputs are transparent and

interpretable, and fills an important gap within

current environmental decision support systems. In

addition, its capability to run in realtime, as well as

uncertainty quantification and stakeholder friendly

dashboard, make it a practical and scalable solution

for the contemporary water quality monitoring

problems. By effectively integrating deep learning,

classical ML, and domain-specific environmental

knowledge, this framework represents a major step

toward intelligent, responsive and reliable water

contamination management.

REFERENCES

Abba, S. I., et al. (2021). Comparative analysis of machine

learning algorithms for water quality prediction.

Environmental Science and Pollution Research, 28,

12345–12356.

Ahmed, M., et al. (2019). Prediction of water quality using

machine learning algorithms. Environmental

Monitoring and Assessment, 191(11), 1–12.

Aldhyani, T. H., et al. (2020). Water quality prediction

using machine learning algorithms. Environmental

Monitoring and Assessment, 192(3), 1–14.

Azrour, M., et al. (2021). Machine learning algorithms for

efficient water quality prediction. Modeling Earth

Systems and Environment, 8, 2793–2801.

Barzegar, R., et al. (2020). CNN model for predicting

dissolved oxygen in water bodies. Environmental

Monitoring and Assessment, 192(7), 1–12.

Elshewey, A. M., Youssef, R. Y., El-Bakry, H. M., &

Osman, A. M. (2025). Water potability classification

based on hybrid stacked model and feature selection.

Environmental Science and Pollution Research, 32,

7933–7949.

Fu, Y., et al. (2021). Temporal convolutional network for

long-term water quality prediction. Journal of

Hydrology, 593, 125–136.

Grbčić, L., et al. (2021). Coastal water quality prediction

based on machine learning with feature interpretation

and spatio-temporal analysis. arXiv preprint

arXiv:2107.03230.

Guo, H., Chen, Z., & Teo, F. Y. (2024). Intelligent water

quality prediction system with a hybrid CNN–LSTM

model. Water Practice & Technology, 19(11), 4538–

4555.

Haq, R. A., & Harigovindan, V. (2022). LSTM-based water

quality prediction system for aquaculture. Aquaculture

Engineering, 96, 102–110.

Khosravi, K., et al. (2025). Enhanced water quality

prediction model using advanced hybridized

resampling alternating tree-based and deep learning

algorithms. Environmental Science and Pollution

Research, 32, 6405–6424.

Khullar, S., & Singh, N. (2022). Bi-LSTM model for water

quality parameter forecasting of the Yamuna River.

Environmental Monitoring and Assessment, 194(1), 1–

12.

Kumar, N. A., & Vellaichamy, J. (2025). A deep learning

approach for water quality assessment: Leveraging

gated linear networks for contamination classification.

Ingénierie des Systèmes d’Information, 30(2), 349–

357.

Liu, Y., et al. (2020). Long short-term memory network for

water quality prediction in the Yangtze River Basin.

Water, 12(1), 1–15.

Najah, A., et al. (2021). Machine learning approaches for

water quality prediction: A review. Environmental

Science and Pollution Research, 28, 123–145.

Othman, M., et al. (2020). Water quality index prediction

using artificial neural networks. Environmental

Monitoring and Assessment, 192(5), 1–10.

Paneru, B., & Paneru, B. (2024). LLMs & XAI for water

sustainability: Seasonal water quality prediction with

LIME explainable AI and a RAG-based chatbot for

insights. arXiv preprint arXiv:2409.10898.

Shafi, J., Ijaz, R., Koul, A., & Ijaz, M. F. (2025). Data-

driven water quality prediction using hybrid machine

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

550

learning approaches for sustainable development goal

6. Environment, Development and Sustainability.

Sillberg, P., et al. (2021). AR-SVM for water quality

classification of the Chao Phraya River. Water

Resources Management, 35, 567–578.

Subudhi, S., et al. (2025). Integrating boosted learning with

differential evolution optimizer: A prediction of

groundwater quality risk assessment in Odisha. arXiv

preprint arXiv:2502.17929.

Swain, R., Mehta, S. K., & Mishra, D. (2025). Enhancing

water quality management: Predictive insights through

machine learning algorithms. In Mitigation and

Adaptation Strategies Against Climate Change in

Natural Systems (pp. 171–180). Springer.

Zhan, Y., et al. (2024). Advanced machine learning models

for robust prediction of water quality. Journal of

Hydroinformatics, 27(2), 299–318.

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Using Environmental and Temporal Features

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