Comprehensive evaluation of machine learning models for suspended sediment load inflow prediction in a reservoir

2021 ◽  
Author(s):  
Muhammad Bilal Idrees ◽  
Muhammad Jehanzaib ◽  
Dongkyun Kim ◽  
Tae-Woong Kim
Keyword(s):  
Machine Learning ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Comprehensive Evaluation ◽  
Suspended Sediment Load ◽  
Learning Models ◽  
Inflow Prediction ◽  
Machine Learning Models

A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia

2021 ◽  
Author(s):  
Marwah Sattar Hanoon ◽  
Alharazi Abdulhadi Abdullatif B ◽  
Ali Najah Ahmed ◽  
Arif Razzaq ◽  
Ahmed H. Birima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Learning Approaches ◽  
Suspended Sediment Load ◽  
River Systems

Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation

2019 ◽  
Vol 33 (3) ◽  
pp. 1217-1231 ◽  
Author(s):  
Ashish Kumar ◽  
Pravendra Kumar ◽  
Vijay Kumar Singh
Keyword(s):  
Machine Learning ◽  
Suspended Sediment ◽  
Learning Models ◽  
Machine Learning Models

Advanced machine learning models development for suspended sediment prediction: Comparative analysis study

2021 ◽  
pp. 1-25
Author(s):  
Achite Mohammed ◽  
Zaher Mundher Yaseen ◽  
Salim Heddam ◽  
Anurag Malik ◽  
Ozgur Kisi
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Suspended Sediment ◽  
Learning Models ◽  
Analysis Study ◽  
Machine Learning Models

scholarly journals Prediction of river suspended sediment load using machine learning models and geo-morphometric parameters

2021 ◽  
Vol 14 (18) ◽  
Author(s):  
Maryam Asadi ◽  
Ali Fathzadeh ◽  
Ruth Kerry ◽  
Zohre Ebrahimi-Khusfi ◽  
Ruhollah Taghizadeh-Mehrjardi
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Morphometric Parameters ◽  
Support Vector ◽  
Suspended Sediment Load ◽  
K Nearest Neighbor ◽  
Discharge Data ◽  
Vector Machines

AbstractEstimating sediment load of rivers is one of the major problems in river engineering that has been using various data mining algorithms and variables. It is desirable to obtain accurate estimates of sediment load while using techniques that limit computational intensity when datasets are large. This study investigates the usefulness of geo-morphometric factors and machine learning (ML) models for predicting suspended sediment load (SSL) in several river basins in Lorestan and Gilan, Iran. Six ML models, namely, multiple linear regression (MLR), artificial neural networks (ANN), K-nearest neighbor (KNN), Gaussian processes (GP), support vector machines (SVM), and evolutionary support vector machines (ESVM), were evaluated for estimating minimum and average SSL for the study regions. Geo-morphometric parameters and river discharge data were utilized as the main predictors in modeling process. In addition, an attribute reduction technique was applied to decrease the algorithm complexity and computational resources used. The results showed that all models estimated both target variables well. However, the optimal models for predicting average sediment load and minimum sediment load were the GP and ESVM models, respectively.

scholarly journals A comparison of performance of SWAT and machine learning models for predicting sediment load in a forested Basin, Northern Spain

CATENA ◽  
2021 ◽  
pp. 105953
Author(s):  
Patricia Jimeno-Sáez ◽  
Raquel Martínez-España ◽  
Javier Casalí ◽  
Julio Pérez-Sánchez ◽  
Javier Senent-Aparicio
Keyword(s):  
Machine Learning ◽  
Sediment Load ◽  
Learning Models ◽  
Northern Spain ◽  
Machine Learning Models

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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