ArticleEnvironmental science and pollution research international2019
Water quality prediction based on recurrent neural network and improved evidence theory: a case study of Qiantang River, China.
Article in Environmental science and pollution research international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing.Sensors (Basel, Switzerland) · 2022Pooled it
- Remote Sensing Inversion of Water Quality Grades Using a Stacked Generalization Approach.Sensors (Basel, Switzerland) · 2024Article
- Deep learning approach for prediction and classification of potable water.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2023Article
- Deep learning approach for prediction and classification of potable water.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2023Article
- Failure Mode and Effects Analysis on the Air System of an Aero Turbofan Engine Using the Gaussian Model and Evidence Theory.Entropy (Basel, Switzerland) · 2023Article
- Water-Quality Assessment and Pollution-Risk Early-Warning System Based on Web Crawler Technology and LSTM.International journal of environmental research and public health · 2022Article
- An improved adaptive neuro fuzzy inference system model using conjoined metaheuristic algorithms for electrical conductivity prediction.Scientific reports · 2022Article
- Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation.Environmental science and pollution research international · 2022Article
- Association between heavy metals and metabolic syndrome in drinking water and surface soil: case-control study in Iran.Environmental science and pollution research international · 2022Article
- Review
- The Use of Artificial Neural Networks to Predict the Physicochemical Characteristics of Water Quality in Three District Municipalities, Eastern Cape Province, South Africa.International journal of environmental research and public health · 2021Article
- Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index.Environmental science and pollution research international · 2020Article
- Comprehensive evaluation on water resource carrying capacity in karst areas using cloud model with combination weighting method: a case study of Guiyang, southwest China.Environmental science and pollution research international · 2020Article
- A method overview in smart aquaculture.Environmental monitoring and assessment · 2020Review
- A water quality prediction method based on the multi-time scale bidirectional long short-term memory network.Environmental science and pollution research international · 2020Article
- Estimation of chlorophyll-a Concentration of lakes based on SVM algorithm and Landsat 8 OLI images.Environmental science and pollution research international · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Water quality prediction is an effective method for managing and protecting water resources by providing an early warning against water quality deterioration. In general, the existing water quality prediction methods are based on a single shallow model which fails to capture the long-term dependence in historical time series and is more likely to cause a high rate of false alarms and false negatives in practical water monitoring application. To resolve these problems, a new model combining recurrent neural network (RNN) with improved Dempster/Shafer (D-S) evidence theory (RNNs-DS) is proposed in this paper. Among them, the RNNs which can handle the long-term dependence in historical time series effectively are used to realize the preliminary prediction of water quality. And the improved D-S evidence theory is used to synthesize the prediction results of RNNs. In addition, an improved strategy based on correlation analysis method is presented for evidence theory to obtain the number of evidence, which reduces uncertainty in evidence selection effectively. Besides, a new basic probability assignment function which based on modified softmax function is proposed. The new function can effectively solve the problems of weight allocation failure in the traditional function. Then, data about permanganate index, pH, total phosphorus, and dissolved oxygen from Jiuxishuichang monitoring station near Qiantang River, Zhejiang Province, China is used to verify the proposed model. Compared with support vector regression (SVR) and backpropagation neural network (BPNN) and three RNN models, the new model shows higher accuracy and better stability as indicated by four indices. Finally, the engineering application of the RNNs-DS algorithm has been realized on the self-developed water environmental monitoring and forecasting system, which can provide effective support for early risk assessment and prevention in water environment.
Indexed as
Identifiers
31093910What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.