Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning

• URL: http://arxiv.org/abs/2501.07599v1
• Date: Fri, 10 Jan 2025 16:54:52 GMT
• Title: Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning
• Authors: Hankun He, Takuya Boehringer, Benjamin Schäfer, Kate Heppell, Christian Beck,
• Abstract summary: We use superstatistical methods and machine learning to predict dissolved oxygen levels in the River Thames. For long-term forecasting, the Informer model consistently delivers superior performance.
• Score: 0.0
• License:
• Abstract: By employing superstatistical methods and machine learning, we analyze time series data of water quality indicators for the River Thames, with a specific focus on the dynamics of dissolved oxygen. After detrending, the probability density functions of dissolved oxygen fluctuations exhibit heavy tails that are effectively modeled using $q$-Gaussian distributions. Our findings indicate that the multiplicative Empirical Mode Decomposition method stands out as the most effective detrending technique, yielding the highest log-likelihood in nearly all fittings. We also observe that the optimally fitted width parameter of the $q$-Gaussian shows a negative correlation with the distance to the sea, highlighting the influence of geographical factors on water quality dynamics. In the context of same-time prediction of dissolved oxygen, regression analysis incorporating various water quality indicators and temporal features identify the Light Gradient Boosting Machine as the best model. SHapley Additive exPlanations reveal that temperature, pH, and time of year play crucial roles in the predictions. Furthermore, we use the Transformer to forecast dissolved oxygen concentrations. For long-term forecasting, the Informer model consistently delivers superior performance, achieving the lowest MAE and SMAPE with the 192 historical time steps that we used. This performance is attributed to the Informer's ProbSparse self-attention mechanism, which allows it to capture long-range dependencies in time-series data more effectively than other machine learning models. It effectively recognizes the half-life cycle of dissolved oxygen, with particular attention to key intervals. Our findings provide valuable insights for policymakers involved in ecological health assessments, aiding in accurate predictions of river water quality and the maintenance of healthy aquatic ecosystems.

Related papers

• LSTM networks provide efficient cyanobacterial blooms forecasting even with incomplete spatio-temporal data [2.5537500385691594]
  Early warning systems (EWS) for cyanobacterial blooms allow timely implementation of management measures. In this paper, we propose an effective EWS for cyanobacterial bloom forecasting, which uses 6 years incomplete high-frequency-temporal data. Results were analyzed for seven forecasting time horizons ranging from 4 to 28 days evaluated with a hybrid system.
  arXiv  Detail & Related papers  (2024-10-09T15:13:24Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• OXYGENERATOR: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning [50.365198230613956]
  Existing expert-dominated numerical simulations fail to catch up with the dynamic variation caused by global warming and human activities. We propose OxyGenerator, the first deep learning based model, to reconstruct the global ocean deoxygenation from 1920 to 2023.
  arXiv  Detail & Related papers  (2024-05-12T09:32:40Z)
• Harmful algal bloom forecasting. A comparison between stream and batch learning [0.7067443325368975]
  Harmful Algal Blooms (HABs) pose risks to public health and the shellfish industry. This study develops a machine learning workflow for predicting the number of cells of a toxic dinoflagellate. The model DoME emerged as the most effective and interpretable predictor, outperforming the other algorithms.
  arXiv  Detail & Related papers  (2024-02-20T15:01:11Z)
• Beyond Tides and Time: Machine Learning Triumph in Water Quality [0.0]
  This study aims to establish a robust predictive pipeline to both data science experts and those without domain specific knowledge. Our research aims to establish a robust predictive pipeline to both data science experts and those without domain specific knowledge.
  arXiv  Detail & Related papers  (2023-09-29T03:33:53Z)
• Multi-step prediction of chlorophyll concentration based on Adaptive Graph-Temporal Convolutional Network with Series Decomposition [11.090455139282883]
  This paper proposes a time-series decomposition adaptive graph-time convolutional network ( AGTCNSD) prediction model. Based on the graph convolutional neural network, the water quality parameter data is modeled, and a parameter embedding matrix is defined. The validity of the model is verified by the water quality data of the coastal city Beihai.
  arXiv  Detail & Related papers  (2023-09-13T02:15:02Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• Optimization of a Hydrodynamic Computational Reservoir through Evolution [58.720142291102135]
  We interface with a model of a hydrodynamic system, under development by a startup, as a computational reservoir. We optimized the readout times and how inputs are mapped to the wave amplitude or frequency using an evolutionary search algorithm. Applying evolutionary methods to this reservoir system substantially improved separability on an XNOR task, in comparison to implementations with hand-selected parameters.
  arXiv  Detail & Related papers  (2023-04-20T19:15:02Z)
• DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions [53.37679435230207]
  We propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data.
  arXiv  Detail & Related papers  (2022-09-23T16:13:47Z)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)
• Modeling Atmospheric Data and Identifying Dynamics: Temporal Data-Driven Modeling of Air Pollutants [2.578242050187029]
  We present an empirical approach using data-driven techniques to study air quality in Madrid. We find parsimonious systems of ordinary differential equations that model the concentration of pollutants and their changes over time. Our results show that Akaike's Information Criterion can work well in conjunction with best subset regression as to find an equilibrium between sparsity and goodness of fit.
  arXiv  Detail & Related papers  (2020-10-13T16:46:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.