scholarly journals Machine learning for manually-measured water quality prediction in fish farming

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256380
Author(s):  
Andres Felipe Zambrano ◽  
Luis Felipe Giraldo ◽  
Julian Quimbayo ◽  
Brayan Medina ◽  
Eduardo Castillo
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Random Forests ◽  
Prediction Models ◽  
Fish Farming ◽  
Time Data ◽  
Water Quality Prediction ◽  
Water Pond ◽  
Measured Water

Monitoring variables such as dissolved oxygen, pH, and pond temperature is a key aspect of high-quality fish farming. Machine learning (ML) techniques have been proposed to model the dynamics of such variables to improve the fish farmer’s decision-making. Most of the research on ML in aquaculture has focused on scenarios where devices for real-time data acquisition, storage, and remote monitoring are available, making it easy to develop accurate ML techniques. However, fish farmers do not necessarily have access to such devices. Many of them prefer to use equipment to manually measure these variables limiting the amount of available data to process. In this work, we study the use of random forests, multivariate linear regression, and artificial neural networks in scenarios with limited amount of measurements to analyze data from water-quality variables that are commonly measured in fish farming. We propose a methodology to build models in two scenarios: i) estimation of unobserved variables based on the observed ones, and ii) forecasting when a low amount of data is available for training. We show that random forests can be used to forecast dissolved oxygen, pond temperature, pH, ammonia, and ammonium when the water pond variables are measured only twice per day. Moreover, we showed that these prediction models can be implemented on a mobile-based information system and run in an average smartphone that fish farmers can afford.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

scholarly journals Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)

2021 ◽  
Author(s):  
Kazutaka Uchida ◽  
Junichi Kouno ◽  
Shinichi Yoshimura ◽  
Norito Kinjo ◽  
Fumihiro Sakakibara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Vessel Occlusion ◽  
Predictive Values ◽  
Training Cohort ◽  
Sensitivity Specificity

AbstractIn conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), subarachnoid hemorrhage (SAH), and cerebral infarction (CI) other than LVO. The predictive abilities were validated in the test cohort with accuracy, positive predictive value, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and F score. The training cohort included 3178 patients with 337 LVO, 487 ICH, 131 SAH, and 676 CI cases, and the test cohort included 3127 patients with 183 LVO, 372 ICH, 90 SAH, and 577 CI cases. The overall accuracies were 0.65, and the positive predictive values, sensitivities, specificities, AUCs, and F scores were stable in the test cohort. The classification abilities were also fair for all ML models. The AUCs for LVO of logistic regression, random forests, and XGBoost were 0.89, 0.89, and 0.88, respectively, in the test cohort, and these values were higher than the previously reported prediction models for LVO. The ML models developed to predict the probability and types of stroke at the prehospital stage had superior predictive abilities.

scholarly journals Monitoring of the hydrochemical regime of growing ponds

2020 ◽  
Vol 22 (93) ◽  
pp. 45-49
Author(s):  
S. I. Kravets ◽  
O. V. Krushelnytska ◽  
N. V. Poliukhovych
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Quality Indicators ◽  
Fish Farming ◽  
Growing Season ◽  
Pond Water ◽  
Total Hardness ◽  
Normative Values ◽  
Hydrochemical Regime ◽  
Water Quality Indicators

The growth and development of fish depend on the physicochemical properties of water, such as temperature, turbidity, concentration of hydrogen ions (pH), dissolved oxygen, total alkalinity, total hardness, nitrates, nitrites, etc. The article presents data on the hydrochemical regime of growing ponds of the farm “Rudnyky” of Lviv region. Studies have shown that the water quality indicators of growing ponds during the growing season are within the normative values and the water is suitable for fish farming. In the breeding ponds of the Rudnyky fishery, the water quality indicators fluctuated within the following limits, namely: the hydrogen index (pH) was within the maximum permissible concentrations (6.7–8.0). The content of dissolved oxygen in the water during the growing season was within normal limits (6.5 to 7.8 mg/l) and fully met the requirements for carp fish ponds, the alkalinity of the water was average and varied between 2.40 and 3.20 mg-eq/l, permanganate and dichromate oxidation varied slightly – 8.6–15.45 mgO/l and 25.6–40.50 mgO/l, which corresponds to the normative indicators for fish farming. The concentration of ammonium nitrogen (NH4+) in the growing ponds of the farm averaged 0.008–0,520 mg N/l, the content of nitrites and nitrates was 0.002–0.100 mg N/l and 0.060–0,520 mg N/l, respectively. The average concentration ranged from 0.10 mg P/l to 0.40 mg P/l of mineral phosphorus (PO4-3), which did not exceed the normative values. The amount of total iron in the water of the pond did not have high values, averaged 0.10–0.38 mg Fe/l, which was within the maximum allowable concentrations. The values of total hardness of water were determined as moderately hard and were in the range of 2.9–5.0 mg-eq/l. The salt composition of pond water was characterized by total mineralization, the ratio of ions and the content of chlorides, sulfates, bicarbonates. During the study period, the content of chlorides (Cl-) sulfates (SO4 2-); hydrocarbons (HCO3-), did not exceed the maximum allowable concentration. Among the cations in the pond water, calcium ions (Ca2+ ) predominated, their content averaged 28.2–80 mg Ca/l. In the studied pond, the concentration of magnesium (Mg2+) reached average values – 6.8–20.0 mg/l. The total mineralization of water was 300.4–440.4 mg/l. By the nature of the ionic composition, the water of the growing ponds belongs to the hydrocarbonate composition of the calcium group.

scholarly journals Potensi budidaya ikan di Waduk Embung Klamalu Kabupaten Sorong Provinsi Papua Barat: Kajian kualitas fisika kimia air

2013 ◽  
Vol 1 (3) ◽  
Author(s):  
Agustina Frasawi ◽  
Robert J Rompas ◽  
Juliaan Ch. Watung
Keyword(s):  
Carbon Dioxide ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Good Condition ◽  
Fish Farming ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Physical Chemical ◽  
Papua Barat

The objective of this research was to measure and analyze the water quality parameters including temperature, brightness, pH, dissolved oxygen, total alkalinity, carbon dioxide and BOD in reservoir Embung Klamalu Sorong regency, and to know the factors that affected the water quality of Embung Klamalu. Measurement of water quality parameters was done in situ for temperature, brightness, pH and in laboratory for dissolved oxygen, total alkalinity, carbon dioxide, and BOD. The results showed the temperature at the five observation stations ranged from 26.2 to 29.8 0C, brightness 38 to 46 cm, pH 7.20 to 8.48 mg /L, dissolved oxygen from 7.20 to 8.48 mg / L, alkalinity 100 to 150 mg /L, carbon dioxide from 25.90 to 28.95 mg / L, BOD from 0.20 to 0.38. Refers to the standards of water quality according to the PP. 82, 2001, it could be concluded that water physical-chemical qualities in fish farming locations in the Village Klamalu were still in good condition. Keywords: Water physical-chemical quality, aquaculture, waduk Embung Klamalu

Hybrid Non-Dominated Sorting Genetic Algorithm

2018 ◽  
pp. 264-286 ◽  
Author(s):  
Sankhadeep Chatterjee ◽  
Sarbartha Sarkar ◽  
Nilanjan Dey ◽  
Amira S. Ashour ◽  
Soumya Sen
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Water Quality ◽  
Quality Prediction ◽  
Learning Methods ◽  
Nsga Ii ◽  
Water Quality Prediction ◽  
Machine Learning Methods ◽  
Precautionary Measures

Water pollution due to industrial and domestic reasons is highly affecting the water quality. In undeveloped and developed countries, it has become a major reason behind a number of water borne diseases. Poor public health is putting an extra economic liability in order to deploy precautionary measures against these diseases. Recent research works have been directed toward more sustainable solutions to this problem. It has been revealed that good quality of water supply can not only improve the public health, it also accelerates economic growth of a geographical location as well. Water quality prediction using machine learning methods is still at its primitive stage. Besides, most of the studies did not follow any national or international standard for water quality prediction. In the current work, both the problems have been addressed. First, advanced machine learning methods, namely Artificial Neural Networks (ANNs) supported by a well-known multi-objective optimization algorithm called the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been used to classify the water samples into two different classes. Secondly, Indian national standard for water quality (IS 10500:2012) has been utilized for this classification task. The hybrid NN-NSGA-II model is compared with another two well-known meta-heuristic supported ANN classifiers, namely ANN trained by Genetic Algorithm (NN-GA) and by Particle Swarm Optimization (NN-PSO). Apart from that, the support vector machine (SVM) has also been included in the comparative study. Besides analysing the performance based on several performance measuring methods, the statistical significance of the results obtained by NN-NSGA-II has been judged by performing Wilcoxon rank sum test with 5% confidence level. Results have indicated the ingenuity of the proposed NN-NSGA-II model over the other classifiers under current study.

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