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Multi-Task Learning Architecture for Simultaneous Prediction of Water Quality, Energy Output, and Financial Volatility Indices.

Nader Behdad
Volume 5

Abstract

Water quality monitoring, energy output forecasting, and financial volatility prediction represent three critical but heterogeneous time-dependent prediction problems that support environmental sustainability, energy system reliability, and financial risk management. Although these domains differ in data characteristics and operational objectives, they share common modeling challenges, including nonlinear dynamics, temporal dependencies, noise, missing observations, external shocks, uncertainty, and the need for accurate multi-output decision support. This paper proposes a multi-task learning architecture for the simultaneous prediction of water quality, energy output, and financial volatility indices within a unified deep learning framework. The proposed architecture employs shared representation layers to capture common temporal and nonlinear patterns across domains, while task-specific prediction branches are designed to learn domain-dependent features associated with environmental indicators, renewable energy generation, and financial market risk. The framework integrates Long Short-Term Memory networks, Gated Recurrent Units, Temporal Convolutional Networks, attention mechanisms, and Transformer-based encoders to model short-term fluctuations, long-range dependencies, and cross-task relationships among heterogeneous time-series inputs. The water quality task incorporates features such as pH, dissolved oxygen, turbidity, temperature, conductivity, nitrate concentration, biochemical oxygen demand, and chemical oxygen demand. The energy forecasting task utilizes solar irradiance, wind speed, temperature, humidity, historical power generation, load demand, and weather-related variables. The financial volatility task includes market returns, trading volume, volatility indices, moving averages, interest rates, macroeconomic indicators, and risk sentiment measures. A comprehensive preprocessing pipeline was applied, including missing value imputation, outlier treatment, normalization, temporal alignment, sliding-window construction, feature engineering, and chronological train-validation-test splitting to prevent data leakage. Experimental results demonstrate that the proposed multi-task learning framework achieves superior predictive performance compared with single-task learning models and conventional machine learning baselines across all three domains, recording lower RMSE, MAE, and MAPE values while improving prediction stability under noisy and volatile conditions. Furthermore, attention-based interpretability and task-specific feature importance analysis reveal the dominant influence of physicochemical parameters on water quality, meteorological variables on energy output, and lagged volatility and market stress indicators on financial risk prediction. The findings indicate that multi-task learning provides a scalable, adaptive, and data-efficient approach for simultaneous cross-domain forecasting, offering practical value for environmental agencies, energy operators, financial analysts, policymakers, and integrated decision-support platforms.

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Keywords

References

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