Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions
Hai Tao,Hai Tao,Hai Tao,Zainab S. Al-Khafaji,Zainab S. Al-Khafaji,Chongchong Qi,Mohammad Zounemat-Kermani,Ozgur Kisi,Tiyasha Tiyasha,Kwok Wing Chau,Vahid Nourani,Vahid Nourani,Assefa M. Melesse,Mohamed Elhakeem,Mohamed Elhakeem,Aitazaz A. Farooque,A. Pouyan Nejadhashemi,Khaled Mohamed Khedher,Omer A. Alawi,Ravinesh C. Deo,Shamsuddin Shahid,Vijay P. Singh,Zaher Mundher Yaseen,Zaher Mundher Yaseen +23 more
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TLDR
Sediment transport in a river basin is therefore a multifa... as discussed by the authors, which is an important indicator for ecological and geomorphological assessments of soil erosion within any watershed region.Abstract:
River sedimentation is an important indicator for ecological and geomorphological assessments of soil erosion within any watershed region. Sediment transport in a river basin is therefore a multifa...read more
Citations
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Novel Genetic Algorithm (GA) based hybrid machine learning-pedotransfer Function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity
Vijay K. Singh,Kanhu Charan Panda,Atish Sagar,Nadhir Al-Ansari,Huanfeng Duan,Pradosh Kumar Paramaguru,Dinesh Kumar Vishwakarma,Ashish Kumar,Devendra Kumar,Purushottam Singh Kashyap,Ravendra Singh,Ahmed Elbeltagi +11 more
TL;DR: In this paper , the authors compared the performances of four machine learning algorithms for different sets of predictors, i.e., Genetic Algorithm (GA), two hybrid Machine Learning based PTFs (ML-PTFs) and Support Vector Machine (SVM-GA) for hydraulic conductivity prediction.
Journal ArticleDOI
Development of new computational machine learning models for longitudinal dispersion coefficient determination: case study of natural streams, United States
Hai Tao,Sinan Q. Salih,Atheer Yousif Oudah,Sani Isah Abba,Ameen Mohammed Salih Ameen,Salih M. Awadh,Omer A. Alawi,Reham R. Mostafa,U. Surendran,Zaher Mundher Yaseen +9 more
TL;DR: XGboost-Grid reported the best prediction results over the training and testing phase compared to RF and GTB models, and the development of the newly established machine learning model revealed an excellent computed-aided technology for the Kx simulation.
Journal ArticleDOI
Integrative artificial intelligence models for Australian coastal sediment lead prediction: An investigation of in-situ measurements and meteorological parameters effects.
Suraj Kumar Bhagat,Tiyasha Tiyasha,Adarsh Kumar,Tabarak Malik,Ali H. Jawad,Khaled Mohamed Khedher,Ravinesh C. Deo,Zaher Mundher Yaseen +7 more
TL;DR: In this paper , three feature selection algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia.
Journal ArticleDOI
Conjunction of cluster ensemble - model ensemble techniques for spatiotemporal assessment of groundwater depletion in semi-arid plains
TL;DR: In this paper , three different types of clustering algorithms were applied to monthly groundwater level (GWL) data sets of the piezometers, and the best structures of all clustering methods were integrated by Combining Multiple Clusterings via Similarity Graph (COMUSA) method to obtain the most homogenous patterns of GWL.
Journal ArticleDOI
Modelling of suspended sediment load by Bayesian optimized machine learning methods with seasonal adjustment
Shi Tao Li,James Yang +1 more
TL;DR: Wang et al. as mentioned in this paper integrated seasonal adjustment (SA) and Bayesian optimization (BOP) into a machine learning (ML) model for sediment load prediction in the Yangtze River, and evaluated its performance by statistical criteria of Nash-Sutcliffe efficiency (NSE), correlation coefficient (CC), root mean squared error (RMSE), and mean absolute error (MAE).
References
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