scholarly journals Improving Flow Discharge-Suspended Sediment Relations: Intelligent Algorithms versus Data Separation

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3650
Author(s):  
Haniyeh Asadi ◽  
Mohammad T. Dastorani ◽  
Roy C. Sidle ◽  
Kaka Shahedi
Keyword(s):  
Suspended Sediment ◽  
Model Performance ◽  
Imperialist Competitive Algorithm ◽  
Climatic Conditions ◽  
Metaheuristic Algorithms ◽  
Support Vector ◽  
Temporal Separation ◽  
Flow Discharge ◽  
Sediment Rating Curve ◽  
Food And Agriculture

Information on the transport of fluvial suspended sediment loads (SSL) is crucial due to its effects on water quality, pollutant transport and transformation, dam operations, and reservoir capacity. As such, adopting a reliable method to accurately estimate SSL is a key topic for watershed managers, hydrologists, river engineers, and hydraulic engineers. One of the most common methods for estimating SSL or suspended sediment concentrations (SSC) is sediment rating curve (SRC), which has several weaknesses. Here, we optimize the SRC equation using two main approaches. Firstly, three well recognized metaheuristic algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competitive algorithm (ICA)) were used together with two classical approaches (food and agriculture organization (FAO) and non-parametric smearing estimator (CF2)) to optimize the coefficients of the SRC regression model. The second approach uses separation of data based on season and flow discharge (Qw) characteristics. A support vector regression (SVR) model using only Qw as an input was employed for SSC estimation and the results were compared with the SRC and its optimized versions. Metaheuristic algorithms improved the performance of the SRC model and the PSO model outperformed the other algorithms. These results also indicate that the model performance was directly related to the temporal separation of data. Based on these findings, if data are more homogenous and related to the limited climatic conditions used in the estimation of SSC, the estimations are improved. Moreover, it was observed that optimizing SRC through metaheuristic models was much more effective than separating data in the SCR model. The results also indicated that with the same input data, SVR was superior to the SRC model and its optimized version.

scholarly journals ESTIMATION OF EROSION AND SEDIMENTATION USING SEDIMENTOLOGICAL DATA AND CALCULATION OF SUSPENDED SEDIMENT LOAD IN GARMSAR WATERSHED

Geosaberes ◽  
2020 ◽  
Vol 11 ◽  
pp. 334
Author(s):  
Gohar Mirakhorlo ◽  
Mohammad Nakhaei ◽  
Mohammadreza Espahbod ◽  
Davoud Jahani ◽  
Khalil Rezaei
Keyword(s):  
Suspended Sediment ◽  
Sediment Load ◽  
Least Squares Method ◽  
Initial Study ◽  
Suitable Model ◽  
Suspended Sediment Load ◽  
Statistical Regression ◽  
Temporal Separation ◽  
Discharge Data ◽  
Flow Discharge

Erosion, sediment transport, sedimentation and water quality are very important issues in watershed management. Based on the initial study, it was found that the three factors of geological materials, slope, and climate are the most important factors in erosion. Therefore, these three factors were examined and combined to create work units. Then, the qualitative sensitivity of the rocks and pre-Quaternary formations was continually determined using the criteria of resistance method and hardness of the rock mass in a field method. Then, several statistical regression models were investigated by discharge classification and temporal separation of data; and by establishing a regression relation between flow discharge and sediment discharge data and its simulation, sedimentation rating curves along with FAO and USBR were presented based on the error least squares method according to statistical analysis methods, and annual long run suspended sediment load values were estimated by combining the proposed models and flow discharge methods such as flow continuity curve, daily mean discharge, and monthly mean discharge. In this regard, the statistics of a 13-year period of Bankooh hydrometric station on the HablehRood River were used. Finally, combination of the classes mean model with the mean daily discharge by FAO method was introduced as a suitable model.

scholarly journals Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 445-451
Author(s):  
Yifei Sun ◽  
Navid Rashedi ◽  
Vikrant Vaze ◽  
Parikshit Shah ◽  
Ryan Halter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real World ◽  
Short Term Memory ◽  
Model Performance ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Continuous Map

ABSTRACT Introduction Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. Materials and Methods Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. Results Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. Conclusion We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

Regionalization of sediment rating curve for sediment yield prediction in ungauged catchments

2013 ◽  
Vol 46 (1) ◽  
pp. 26-38 ◽  
Author(s):  
Sokchhay Heng ◽  
Tadashi Suetsugi
Keyword(s):  
Sediment Yield ◽  
Model Performance ◽  
Data Uncertainty ◽  
Spatial Proximity ◽  
Yield Prediction ◽  
Rating Curve ◽  
Ungauged Catchments ◽  
Probability Curve ◽  
Sediment Rating Curve ◽  
Almost All

The main objective of this research is to regionalize the sediment rating curve (SRC) for subsequent sediment yield prediction in ungauged catchments (UCs) in the Lower Mekong Basin. Firstly, a power function-based SRC was fitted for 17 catchments located in different parts of the basin. According to physical characteristics of the fitted SRCs, the sediment amount observed at the catchment outlets is mainly transported by several events. This also indicates that clockwise hysteretic phenomenon of sediment transport is rather important in this basin. Secondly, after discarding two outlier catchments due to data uncertainty, the remaining 15 catchments were accounted for the assessment of model performance in UCs by means of jack-knife procedure. The model regionalization was conducted using spatial proximity approach. As a result of comparative study, the spatial proximity approach based on single donor catchment provides a better regionalization solution than the one based on multiple donor catchments. By considering the ideal alternative, a satisfactory result was obtained in almost all the modeled catchments. Finally, a regional model which is a combination of the 15 locally fitted SRCs was established for use in the basin. The model users can check the probability that the prediction results are satisfactory using the designed probability curve.

Forecasting the risk at infractions: an ensemble comparison of machine learning approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lei Li ◽  
Desheng Wu
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Short Term Memory ◽  
Model Performance ◽  
Large Data ◽  
Support Vector ◽  
Learning Approaches ◽  
Content Type ◽  
Day To Day Operations ◽  
Prediction Approach

PurposeThe infraction of securities regulations (ISRs) of listed firms in their day-to-day operations and management has become one of common problems. This paper proposed several machine learning approaches to forecast the risk at infractions of listed corporates to solve financial problems that are not effective and precise in supervision.Design/methodology/approachThe overall proposed research framework designed for forecasting the infractions (ISRs) include data collection and cleaning, feature engineering, data split, prediction approach application and model performance evaluation. We select Logistic Regression, Naïve Bayes, Random Forest, Support Vector Machines, Artificial Neural Network and Long Short-Term Memory Networks (LSTMs) as ISRs prediction models.FindingsThe research results show that prediction performance of proposed models with the prior infractions provides a significant improvement of the ISRs than those without prior, especially for large sample set. The results also indicate when judging whether a company has infractions, we should pay attention to novel artificial intelligence methods, previous infractions of the company, and large data sets.Originality/valueThe findings could be utilized to address the problems of identifying listed corporates' ISRs at hand to a certain degree. Overall, results elucidate the value of the prior infraction of securities regulations (ISRs). This shows the importance of including more data sources when constructing distress models and not only focus on building increasingly more complex models on the same data. This is also beneficial to the regulatory authorities.

scholarly journals Simulating soil organic C dynamics in managed grasslands under humid temperate climatic conditions

2020 ◽  
Author(s):  
Asma Jebari ◽  
Jorge Álvaro-Fuentes ◽  
Guillermo Pardo ◽  
María Almagro ◽  
Agustin del Prado
Keyword(s):  
Field Experiments ◽  
Model Performance ◽  
Climatic Conditions ◽  
Plant Residue ◽  
Plant Residues ◽  
Organic C ◽  
Natural Grasslands ◽  
Rothc Model ◽  
Below Ground ◽  
Intensively Managed

Abstract. Temperate grasslands are of paramount importance in terms of soil organic carbon (SOC) dynamics. Globally, research on SOC dynamics has largely focused on forests, croplands and natural grasslands, while intensively managed grasslands has received much less attention. In this regard, we aimed to improve the prediction of SOC dynamics in managed grasslands under humid temperate regions. In order to do so, we modified and recalibrated the SOC model RothC, originally developed to model the turnover of SOC in arable topsoils, which requires limited amount of readily available input data. The modifications proposed for the RothC are: (1) water content up to saturation conditions in the soil water function of RothC to fit the humid temperate climatic conditions, (2) entry pools that account for particularity of exogenous organic matter (EOM) applied (e.g., ruminant excreta), (3) annual variation in the carbon inputs derived from plant residues considering both above- and below-ground plant residue and rhizodeposits components as well as their quality, and (4) the livestock treading effect (i.e., poaching damage) as a common problem in humid areas with higher annual precipitation. In the paper, we describe the basis of these modifications, carry out a simple sensitivity analysis and validate predictions against data from existing field experiments from four sites in Europe. Model performance showed that modified RothC reasonably captures well the different modifications. However, the model seems to be more sensitive to soil moisture and plant residues modifications than to the other modifications. The applied changes in RothC model could be appropriate to simulate both farm and regional SOC dynamics from managed grassland-based systems under humid temperate conditions.

scholarly journals Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy

2021 ◽  
Author(s):  
Saeed Farzin ◽  
Mahdi Valikhan Anaraki
Keyword(s):  
Climate Change ◽  
Suspended Sediment ◽  
General Circulation ◽  
Sediment Load ◽  
Fuzzy Inference ◽  
Circulation Model ◽  
Support Vector ◽  
Suspended Sediment Load ◽  
Inference System ◽  
New Strategy

Abstract In the present study, for the first time, a new strategy based on a combination of the hybrid least-squares support-vector machine (LS-SVM) and flower pollination optimization algorithm (FPA), average 24 general circulation model (GCM) output, and delta change factor method has been developed to achieve the impacts of climate change on runoff and suspended sediment load (SSL) in the Lighvan Basin in the period (2020–2099). Also, the results of modeling were compared to those of LS-SVM and adaptive neuro-fuzzy inference system (ANFIS) methods. The comparison of runoff and SSL modeling results showed that the LS-SVM-FPA algorithm had the best results and the ANFIS algorithm had the worst results. After the acceptable performance of the LS-SVM-FPA algorithm was proved, the algorithm was used to predict runoff and SSL under climate change conditions based on ensemble GCM outputs for periods (2020–2034, 2035–2049, 2070–2084, and 2085–2099) under three scenarios of RCP2.6, RCP4.5, and RCP8.5. The results showed a decrease in the runoff in all periods and scenarios, except for the two near periods under the RCP2.6 scenario for runoff. The predicted runoff and SSL time series also showed that the SSL values were lower than the average observation period, except for 2036–2039 (up to an 8% increase in 2038).

FRUIT AND BERRIES WORLD PRODUCTION AND INTERNATIONAL TRADE

2020 ◽  
Vol 10 (8) ◽  
pp. 1445-1464
Author(s):  
R.R. Mukhametzyanov ◽  
◽  
E.V. Britik ◽  
Keyword(s):  
International Trade ◽  
Large Scale ◽  
Import Substitution ◽  
World Market ◽  
Climatic Conditions ◽  
Global Rating ◽  
Production Volume ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
The World

Horticulture is an important branch of agriculture with particular importance in some countries of the world. The production of fruits, berries and nuts is an important part of forming a high-grade food supply for the population in many countries, including the developing ones. Basing on the statistical data from the Food and Agriculture Organization of the United Nations (UN), the authors examined the change in the production volume of these products in the world as a whole for 1961-2018, as well as for the period 1992-2018 in some countries - twenty largest producers in 2018; and a number of trends were identified. In particular, it was noted that in 2018 the global gross harvest of fruits and berries increased by 4.34 times compared to 1961, while that of nuts - by 7.04 times. A deeper analysis in the context of states, which are the main producers of fruits, berries and nuts, carried out for 1922-2018, indicates that there is a change in the positions of these countries in the corresponding world ranking. The quantitative and qualitative changes we observe inevitably have a significant impact both on the volume of the world market in terms of production, and, consequently, the supply of fruit and berry products, and on the parameters of international trade in fruits, berries and nuts. Due to the fact that the Russian Federation is not among the countries - largest producers of fruit and berry products (in 2018 it was the 31st in the global rating for fruits and berries, and the 52nd for nuts), it occupies a very significant position in the world on its imports, especially on some of them. In connection with the policy of import substitution, deployed in response to sanctions from a number of Western states, some positive changes are also observed in the Russian gardening industry. However, imports in the resources of fruits and berries still amounted to 53.6% in 2018. Naturally, many types of fruit and berry products are economically inexpedient to cultivate on an industrial scale in the natural and climatic conditions of our country, but it is necessary to carry out scientifically grounded and systematic work to increase the production of relatively traditional for Russia fruit and berry plants in the large-scale commodity sector.

Clinical and genomic predictors of brain metastases (BM) in non-small cell lung cancer (NSCLC): An AACR Project GENIE analysis.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 2032-2032
Author(s):  
Protiva Rahman ◽  
Michele LeNoue-Newton ◽  
Sandip Chaugai ◽  
Marilyn Holt ◽  
Neha M Jain ◽  
...  
Keyword(s):  
Ridge Regression ◽  
Model Performance ◽  
Parameter Tuning ◽  
Ensemble Classifier ◽  
Receiver Operating Curve ◽  
Support Vector ◽  
Lasso Regression ◽  
Test Set ◽  
Genetic Features ◽  
Nsclc Patients

2032 Background: 30-50% of patients with non-early NSCLC will eventually develop BM, with a median survival of less than one year from BM diagnosis. There are no widely accepted clinical risk models for development of BM in patients without them at baseline. We predicted the binary risk of BM using clinical and genetic factors from a large multi-institutional cohort. Methods: Stage II-IV NSCLC patients from the AACR Project GENIE Biopharma Consortium dataset were eligible. This consisted of 4 academic institutions who curated clinical data of patients who had somatic next-generation tumor sequencing (NGS) between 2015-2017. We excluded patients who had BM at baseline, died within 30 days of NSCLC diagnosis, or did not undergo brain imaging. Covariates included demographics, anticancer therapies (received up to 90 days prior to BM development and within 5 years from NSCLC diagnosis), and NGS data; radiotherapy (RT) data were not available. NGS features included mutations and copy number alterations. These features were restricted to those classified as oncogenic by OncoKB. Univariate feature selection with Fisher’s test (p<.1) was performed on medication and genetic features. We compared 5 different machine learning models for prediction: random forest (RF), support vector machine (SVM), lasso regression, ridge regression, and an ensemble classifier. We split our data into training and test sets. 10-fold cross-validation was done on the training set for parameter tuning. The area under the receiver-operating curve (AUC) is reported on the test set. Results: 956 patients were included, 192 (20%) in the test set. Univariate features associated with BM were treatment with etoposide, Asian race, presence of bone metastases at NSCLC diagnosis, mutations in TP53 and EGFR, amplifications of ERBB2 and EGFR, and deletions of RB1, CDKN2A and CDKN2B. Univariate features inversely associated with BM were older age, treatment with nivolumab, vinorelbine, alectinib, pembrolizumab, atezolizumab, and gemcitabine, as well as mutations in NOTCH1 and KRAS. Ridge regression had the best AUC, 0.73 (Table). Conclusions: We achieved reasonable prediction performance using commonly obtained clinical and genomic information in non-early NSCLC. The biologic role of the associated alterations deserves further scrutiny; this study replicates similar findings for EGFR and KRAS in a much smaller cohort. Certain subsets of NSCLC patients may benefit from increased surveillance for BM and transition to drug therapies known to effectively cross the blood-brain barrier, e.g., nivolumab and alectinib. Inclusion of additional covariates, e.g., brain RT, may further improve model performance.[Table: see text]

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