Beyond Tides and Time: Machine Learning Triumph in Water Quality

• URL: http://arxiv.org/abs/2309.16951v2
• Date: Fri, 6 Oct 2023 20:54:42 GMT
• Title: Beyond Tides and Time: Machine Learning Triumph in Water Quality
• Authors: Yinpu Li, Siqi Mao, Yaping Yuan, Ziren Wang, Yixin Kang, Yuanxin Yao
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Water resources are essential for sustaining human livelihoods and environmental well being. Accurate water quality prediction plays a pivotal role in effective resource management and pollution mitigation. In this study, we assess the effectiveness of five distinct predictive models linear regression, Random Forest, XGBoost, LightGBM, and MLP neural network, in forecasting pH values within the geographical context of Georgia, USA. Notably, LightGBM emerges as the top performing model, achieving the highest average precision. Our analysis underscores the supremacy of tree-based models in addressing regression challenges, while revealing the sensitivity of MLP neural networks to feature scaling. Intriguingly, our findings shed light on a counterintuitive discovery: machine learning models, which do not explicitly account for time dependencies and spatial considerations, outperform spatial temporal models. This unexpected superiority of machine learning models challenges conventional assumptions and highlights their potential for practical applications in water quality prediction. Our research aims to establish a robust predictive pipeline accessible to both data science experts and those without domain specific knowledge. In essence, we present a novel perspective on achieving high prediction accuracy and interpretability in data science methodologies. Through this study, we redefine the boundaries of water quality forecasting, emphasizing the significance of data driven approaches over traditional spatial temporal models. Our findings offer valuable insights into the evolving landscape of water resource management and environmental protection.

Related papers

• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction [40.58819011476455]
  This paper presents a novel approach named Physics guided Neural Network for Air Quality Prediction (AirPhyNet) We leverage two well-established physics principles of air particle movement (diffusion and advection) by representing them as differential equation networks. Experiments on two real-world benchmark datasets demonstrate that AirPhyNet outperforms state-of-the-art models for different testing scenarios.
  arXiv  Detail & Related papers  (2024-02-06T07:55:54Z)
• Air Quality Forecasting Using Machine Learning: A Global perspective with Relevance to Low-Resource Settings [0.0]
  Air pollution stands as the fourth leading cause of death globally. This study proposes a novel machine learning approach for accurate air quality prediction using two months of air quality data.
  arXiv  Detail & Related papers  (2024-01-09T05:52:02Z)
• Challenges in data-based geospatial modeling for environmental research and practice [19.316860936437823]
  Data-based geospatial modelling using machine learning (ML) has gained popularity in environmental research. This survey reviews common nuances in geospatial modelling, such as imbalanced data, spatial autocorrelation, prediction errors, model generalisation, domain specificity, and uncertainty estimation.
  arXiv  Detail & Related papers  (2023-11-18T12:30:49Z)
• 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)
• Intelligent model for offshore China sea fog forecasting [0.7503129292751938]
  This study aims to develop an advanced sea fog forecasting method embedded in a numerical weather prediction model. We employ a time-lagged correlation analysis technique to identify key predictors and decipher the underlying mechanisms driving sea fog occurrence. To verify the accuracy of our method, we evaluate its performance using a comprehensive dataset spanning one year.
  arXiv  Detail & Related papers  (2023-07-20T04:46:34Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• Artificial Intelligence Hybrid Deep Learning Model for Groundwater Level Prediction Using MLP-ADAM [0.0]
  In this paper, a multi-layer perceptron is applied to simulate groundwater level. The adaptive moment estimation algorithm is also used to this matter. Results indicate that deep learning algorithms can demonstrate a high accuracy prediction.
  arXiv  Detail & Related papers  (2021-07-29T10:11:45Z)
• 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)
• Predictive Analytics for Water Asset Management: Machine Learning and Survival Analysis [55.41644538483948]
  We study a statistical and machine learning framework for the prediction of water pipe failures. We use a dataset containing the failure records of all pipes within the water distribution network in Barcelona, Spain. The results shed light on the effect of important risk factors, such as pipe geometry, age, material, and soil cover, among others.
  arXiv  Detail & Related papers  (2020-07-02T19:08:36Z)

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.