Journal ArticleDOI
Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination
Sani Isah Abba,Sinan Jasim Hadi,Saad Sh. Sammen,Sinan Q. Salih,Sinan Q. Salih,R. A. Abdulkadir,Quoc Bao Pham,Zaher Mundher Yaseen +7 more
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TLDR
In this article, the applicability of Extreme Gradient Boosting (XGB) and Genetic Programming (GP) in obtaining feature importance, and abstracted input variables were imposed into the predictive model (the Extreme Learning Machine (ELM)) for the prediction of water quality index (WQI).About:
This article is published in Journal of Hydrology.The article was published on 2020-08-01. It has received 78 citations till now.read more
Citations
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Journal ArticleDOI
Ensemble machine learning paradigms in hydrology: A review
TL;DR: In this article, a review of ensemble learning methodologies in various areas of hydrology for simulation and prediction purposes has been presented, and the general findings demonstrate the absolute superiority of using ensemble strategies over the regular (individual) model learning in hydrology.
Journal ArticleDOI
Facile synthesis of crosslinked chitosan-tripolyphosphate/kaolin clay composite for decolourization and COD reduction of remazol brilliant blue R dye: Optimization by using response surface methodology
TL;DR: In this paper, a facile synthesis of crosslinked chitosan-tripolyphosphate/kaolin clay (CS-TPP/KC) composite was performed by two subsequent steps involving modification of chitosa (CS) with an inorganic clay (kaolin, KC), followed by ionic cross-linking reaction by tripolyphophosphat (TPP).
Journal ArticleDOI
Prediction of surface water total dissolved solids using hybridized wavelet-multigene genetic programming: New approach
TL;DR: In this article, the authors investigated the potential of a novel computer aid approach based on the hybridization of wavelet pre-processing with multigene genetic programming (W-MGGP) for monthly TDS prediction at the Sefid Rud River in Northern Iran.
Journal ArticleDOI
A deep learning algorithm for multi-source data fusion to predict water quality of urban sewer networks
TL;DR: A deep learning algorithm—which includes recurrent neural network, long-short term memory, and gated recurrent unit (GRU)— has good predictive performance, in which GRU shows superior ability in predicting the chemical index of water quality and the learning curve is faster.
Journal ArticleDOI
Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction
Ali Omran Al-Sulttani,Mustafa Al-Mukhtar,Ali B. Roomi,Aitazaz A. Farooque,Khaled Mohamed Khedher,Zaher Mundher Yaseen +5 more
TL;DR: In this paper, five different ensemble machine learning (ML) models including Quantile regression forest (QRF), Random Forest (RF), radial support vector machine (SVM), Stochastic Gradient Boosting (GBM).
References
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Journal ArticleDOI
Greedy function approximation: A gradient boosting machine.
TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.
Proceedings ArticleDOI
XGBoost: A Scalable Tree Boosting System
Tianqi Chen,Carlos Guestrin +1 more
TL;DR: XGBoost as discussed by the authors proposes a sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning to achieve state-of-the-art results on many machine learning challenges.
Proceedings ArticleDOI
XGBoost: A Scalable Tree Boosting System
Tianqi Chen,Carlos Guestrin +1 more
TL;DR: This paper proposes a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning and provides insights on cache access patterns, data compression and sharding to build a scalable tree boosting system called XGBoost.
Journal ArticleDOI
Extreme learning machine: Theory and applications
TL;DR: A new learning algorithm called ELM is proposed for feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs which tends to provide good generalization performance at extremely fast learning speed.
Journal ArticleDOI
Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations
Daniel N. Moriasi,Jeffrey G. Arnold,M. W. Van Liew,Ronald L. Bingner,R. D. Harmel,Tamie L. Veith +5 more
TL;DR: In this paper, the authors present guidelines for watershed model evaluation based on the review results and project-specific considerations, including single-event simulation, quality and quantity of measured data, model calibration procedure, evaluation time step, and project scope and magnitude.