A soft sensor for simulating algal cell density based on dynamic response to environmental changes in a eutrophic shallow lake

Explainable deep learning identifies patterns and drivers of freshwater harmful algal blooms

The escalating magnitude, frequency, and duration of harmful algal blooms (HABs) pose significant challenges to freshwater ecosystems worldwide. However, the mechanisms driving HABs remain poorly understood, in part due to the strong regional specificity of algal processes and the uneven data availability. These complexities make it difficult to generalize HAB dynamics and effectively predict their occurrence using traditional models. To address these challenges, we developed an explainable deep learning approach using long short-term memory (LSTM) models combined with explanation techniques that can capture complex patterns and provide explainable insights into key HAB drivers. We applied this approach for algal density modeling at 102 sites in China's lakes and reservoirs over three years. LSTMs effectively captured daily algal dynamics, achieving mean and maximum Nash-Sutcliffe efficiency coefficients of 0.48 and 0.95 during testing phase. Moreover, water temperature emerged as the primary driver of HABs both nationally and in over 30% of localities, with stronger water temperature sensitivity observed in mid-to low-latitudes. We also identified regional similarities that allow for the successful transferability in modeling algal dynamics. Specifically, using fine-tuned transfer learning, we improved the prediction accuracy in over 75% of poorly gauged areas. Overall, LSTM-based explainable deep learning approach effectively addresses key challenges in HAB modeling by tackling both regional specificity and data limitations. By accurately predicting algal dynamics and identifying critical drivers, this approach provides actionable insights into the mechanisms of HABs, ultimately aids in the implementation of effective mitigation measures for nationwide and regional freshwater ecosystems.

Exploring Spatiotemporal Patterns of Algal Cell Density in Lake Dianchi with Explainable Machine Learning

The escalating global occurrence of algal blooms poses a growing threat to ecosystem services. In this study, the spatiotemporal heterogeneity of water quality parameters was leveraged to partition Lake Dianchi into three clusters. Considering water quality parameters and both the delayed and instantaneous effects of meteorological factors, ensemble learning, and quasi-Monte Carlo methods were employed to predict daily algal cell density (AD) between January 2021 and January 2024. Consistently, superior predictive accuracy across all three clusters was exhibited by the Stacking-Elastic-Net regularization model. Furthermore, the minimum combination of drivers that achieved near-optimal accuracy for each cluster was identified, striking a balance between accuracy and cost. The ranking of the effect of drivers on AD varied by cluster, while the delayed effect of meteorological factors on AD generally outweighed their instantaneous effect for all clusters. Additionally, the heterogeneous or homogeneous thresholds and responses between drivers and AD were explored. These findings could serve as a scientific and cost-effective basis for government agencies to develop regional sustainable strategies for managing water quality.

Enhancing biomass conversion to bioenergy with machine learning: Gains and problems

The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.

Prediction of hydrological and water quality data based on granular-ball rough set and k-nearest neighbor analysis

Hydrological and water quality datasets usually encompass a large number of characteristic variables, but not all of these significantly influence analytical outcomes. Therefore, by wisely selecting feature variables with rich information content and removing redundant features, it not only can the analysis efficiency be improved, but the model complexity can also be simplified. This paper considers introducing the granular-ball rough set algorithm for feature variable selection and combining it with the k-nearest neighbor method and back propagation network to analyze hydrological and water quality data, thus promoting overall and fused inspection. The results of hydrological water quality data analysis show that the proposed method produces better results compared to using a standalone k-nearest neighbor regressor.