M. Berlin

Bio: M. Berlin is an academic researcher from National Institute of Technology, Arunachal Pradesh. The author has contributed to research in topics: Extreme learning machine & Vadose zone. The author has an hindex of 9, co-authored 19 publications receiving 157 citations. Previous affiliations of M. Berlin include Indian Institute of Technology Madras & Indian Institutes of Technology.

Modeling suspended sediment load in a river using extreme learning machine and twin support vector regression with wavelet conjunction

Abstract: Forecasting the sediment load in a river is difficult due to different parameters viz., heavy rainfall and precipitation, tropical climate, transportation of sediment, and so on. The wavelet transformations model helps to analyze the time and frequency information to estimate sediment load by decomposing data over several phases. Inspired from this idea, based on extreme learning machine (ELM) and twin support vector regression (TSVR), this work proposes two coiflet wavelet-based models as, coiflet wavelet-based ELM and coiflet wavelet-based TSVR for sediment load estimation. The results are compared with conventional ELM and TSVR. The performances of the algorithms are examined using five performance evaluation techniques i.e. root mean square error, mean absolute error, ratio between sum of squares error and total sum of squares, symmetric mean absolute percentage error and mean absolute scaled error. The experimental outcomes reveal that the hybrid models based on the coiflet wavelet offer good performance.

Robust regularized extreme learning machine with asymmetric Huber loss function

Abstract: Sediment transport is one of the major challenging fields in hydrology. The tropical atmosphere, complex topography and occasional extreme precipitation are the fundamental explanations behind this challenge. Thus, the rivers in this situation contain a huge quantity of sediment, which may affect the river hydraulics. Hence, it is required to collect various parameters such as discharge, velocity, rainfall and sediment concentration to analyze the impact of sediment for river engineering practices and management. Therefore, the dataset which is collected from the river may contain outliers and noises. For improving the prediction accuracy of sediment load, we present robust regularized extreme learning machine frameworks to reduce the effect of noise by using the asymmetric Huber loss function named as AHELM and $$ \varepsilon {-} $$ insensitive Huber loss function named as $$ \varepsilon {-} $$ AHELM. Further, the problems are rewritten in the form of strongly convex minimization problems whose solutions are acquired by simple function iterative schemes. To ensure the effectiveness of the proposed approach, we have considered the real-world datasets with two types of noises. Furthermore, the proposed schemes are applied on real sediment load datasets (SLDs) which are collected from the Tawang Chu river of Arunachal Pradesh, India. The results reveal that proposed AHELM and $$ \varepsilon {-} $$ AHELM with multiquadric activation function are performed better for real-world datasets, whereas AHELM and $$ \varepsilon {-} $$ AHELM with sigmoid activation function perform efficiently and effectively for the sediment load prediction. In overall, the experimental results clearly exhibit the applicability as well as the usability of the proposed extreme learning machine with asymmetric Huber loss functions.

Artificial intelligence for suspended sediment load prediction: a review

Abstract: The estimation of sediment yield concentration is crucial for the development of stream ventures, watershed management, toxins estimation, soil disintegration, floods, and so on. In this study, we summarize various existing artificial intelligence (AI)-based suspended sediment load (SSL) estimation models to calculate the suspended sediment load, to our knowledge to date. The artificial neural network (ANN), generalized regression neural network (GRNN), neuro-fuzzy (NF), genetic algorithm (GA), gene expression programming (GEP), classification and regression tree (CART), linear regression (LR), multilinear regression (MLR), Chi-squared automatic interaction detection (CHAID), extreme learning machine (ELM), and support vector machine (SVM) are among the many AI-based models that have been successfully implemented for sediment load prediction. In this paper, we describe a few popular AI-based models that have been used for SSL prediction. ANN, SVM, and NF had overcome each other in different circumstances of prediction; and all three can be said as good predictors. Models using ANN with ELM or wavelet analysis in some ways are good predictors as their predicted values generally lie closer to the measured value. Performances of the algorithms are usually evaluated by applying various types of performance assessment methods most commonly RMSE, R2, MAE, etc. This review is required to bear some significance to the researchers and hydrologists while seeking models that have been effectively actualized inSSLestimation or in hydrology related aspects, however, mainly focused on the researches between January 2015 and November 2020.

Numerical modeling of biological clogging on transport of nitrate in an unsaturated porous media

Abstract: In order to better understand nitrogen species transport and transformation in the saturated zone, it is essential to predict the contaminant concentration in the unsaturated zone as the concentration profiles estimated from unsaturated zone forms as the input for estimation of contaminant concentration in the saturated zone. Since the nitrogen transformations occur in the presence of bacteria, there is a need to account the influence of biological clogging to develop comprehensive model of nitrogen species transport. In this study, a one-dimensional numerical model is developed to investigate the nitrogen species transport in an unsaturated porous media along with microbial clogging process. Results suggest that hydraulic conductivity is enhanced initially due to increase in the water content followed by a significant reduction at larger simulation time resulting from bioclogging. As bioclogging mitigates the hydraulic conductivity significantly, a considerable delay is observed in the ammonium nitrogen and nitrate nitrogen transport with reference to the system without bioclogging. In addition, the effect of oxygen mass transfer coefficient on biological clogging was evaluated. Numerical results suggest that as the oxygen mass transfer is decreased the ammonium nitrogen and nitrate nitrogen concentration travels a larger depth. Further numerical results strongly suggest that nitrogen species transport in the unsaturated zone is significantly affected by the presence of biological clogging.

Wastewater Engineering Treatment Disposal And Reuse

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River water quality index prediction and uncertainty analysis: A comparative study of machine learning models

Abstract: The Water Quality Index (WQI) is the most common indicator to characterize surface water quality. This study introduces a new ensemble machine learning model called Extra Tree Regression (ETR) for predicting monthly WQI values at the Lam Tsuen River in Hong Kong. The ETR model performance is compared with that of the classic standalone models, Support Vector Regression (SVR) and Decision Tree Regression (DTR). The monthly input water quality data including Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Electrical Conductivity (EC), Nitrate-Nitrogen ( NO 3 -N), Nitrite-Nitrogen ( NO 2 -N), Phosphate ( P O 4 3 - ), potential for Hydrogen (pH), Temperature (T) and Turbidity (TUR) are used for building the prediction models. Various input data combinations are investigated and assessed in terms of prediction performance, using numerical indices and graphical comparisons. The analysis shows that the ETR model generally produces more accurate WQI predictions for both training and testing phases. Although including all the ten input variables achieves the highest prediction performance ( R 2 t e s t = 0.98 , R M S E t e s t = 2.99 ), a combination of input parameters including only BOD, Turbidity and Phosphate concentration provides the second highest prediction accuracy ( R 2 t e s t = 0.97 , R M S E t e s t = 3.74 ). The uncertainty analysis relative to model structure and input parameters highlights a higher sensitivity of the prediction results to the former. In general, the ETR model represents an improvement on previous approaches for WQI prediction, in terms of prediction performance and reduction in the number of input parameters.

Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization

Abstract: Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes. In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla watershed in northern Pakistan by integrating particle swarm optimization (PSO) and grey wolf optimization (GWO) with extreme learning machine (ELM) as ELM-PSOGWO. The proposed method was compared with the standalone ELM, hybrid of ELM-PSO, and binary hybrid PSOGSA (hybrid of PSO with gravitational search algorithm) methods. Monthly precipitation and runoff data were used as inputs to the models to examine their accuracy in terms of different statistical indexes. Test results showed that the proposed ELM-PSOGWO provided more accurate results than the standalone ELM, hybrid ELM-PSO, ELM-GWO nd binary hybrid PSOGSA methods in monthly runoff prediction. ELM-PSOGWO reduced the RMSE in prediction of ELM, ELM-PSO, ELM-GWO and ELM-PSOGSA by 38.2, 22.8, 22.4 and 16.7%, respectively. The PSO and GWO based ELM models also performed better than standalone ELM models, with an improvement in RMSE by 19.9 to 20.3%, respectively. Results also showed that adding precipitation as input enhanced the prediction accuracy of models. ELM-PSOGWO was also able to provide more precise estimates of peak runoff with the lowest absolute mean relative error compared to other methods. The results indicate the potential of ELM-PSOGWO model to be recommended for monthly runoff prediction.