Shah Newaz Alam Shuvo

Alltech (United States)

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Abu Selim

International Centre for Integrated Mountain Development

Nurunnahar Akter

University of Science and Technology

Masum Howlader

BRAC

Sanjida Afrin

RMIT University

Tania Sultana

Khulna University

Md. Monirul Islam

University of Dhaka

Md. Sabit Mia

Noakhali Science and Technology University

Molla Rahman Shaibur

University of Science and Technology

Mohammed Mizanur Rahman

Michigan State University

Jehan Y. Al‐Humaidi

Princess Nourah bint Abdulrahman University

Cooperative Institutions

Bangladesh Agricultural University

University of Dhaka

Bangladesh Council of Scientific and Industrial Research

University of Science and Technology

Dhaka Medical College and Hospital

Bangladesh University of Engineering and Technology

International University of Business Agriculture and Technology

University of Rajshahi

Noakhali Science and Technology University

King Abdulaziz University

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Evaluating cigarette butt pollution: Insights from Cox's Bazar Sea beach of Bangladesh

Marine Pollution Bulletin (2023)

Masum Howlader Abu Selim Shah Newaz Alam Shuvo Md. Monirul Islam Tania Sultana

Marine Pollution

10.1016/j.marpolbul.2023.115705

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Citations (11)

Exploring cigarette butts pollution in Saint Martin Island: A marine protected area

Marine Pollution Bulletin (2024)

Masum Howlader Abu Selim Mobin Hossain Shohan Shah Newaz Alam Shuvo Jehan Y. Al‐Humaidi

Marine Pollution

10.1016/j.marpolbul.2024.116439

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Citations (10)

Predictive Models for Dissolved Oxygen in an Urban Lake by Regression Analysis and Artificial Neural Network

Abu Selim Shah Newaz Alam Shuvo Mohammad Moniruzzaman M. M. Islam Sakifa Shah

The paper portrays predictive models for dissolved oxygen (DO) levels in an urban lake using common water quality parameters like Temperature, pH, Conductivity and ORP at a time. Data were sampled using three real-time, industry-standard sensors, OPTOD, CTZN, and PHEHT, and then interpolated using the ArcGIS kriging technique. Correlation studies were analyzed through the ML algorithm, the correlation study signified a highly positive correlation between DO and other water parameters and the model was corroborated by R-score in order to create the linear regression model. In addition, an artificial neural network- a machine learning method using the Levenberg-Marquardt algorithm was developed to build a model to predict the do as well. Then, the performance of the models was validated and also the R2 accuracy was checked of the predicted data against the actual data. Thus, the appropriateness of the ANN model for the forecasting of investigated attributes is indicated by the fact that the discrepancy between the forecasted and real ANN model is significantly lesser than that of the regression model. However, the model can be used to reveal DO data from unknown urban lake water.

Predictive modelling

10.20944/preprints202302.0083.v2

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Citations (2)

Abundance and Distribution of Anthropogenic Marine Litter on the Beaches of Sonadia Island: An Ecologically Critical Area

Masum Howlader Shah Newaz Alam Shuvo Abu Selim Jehan Y. Al‐Humaidi Md. Monirul Islam

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI

Marine debris

Litter

10.2139/ssrn.4767423

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Citations (4)

Linear Predictive Model for Dissolved Oxygen in a Protected Urban Lake using Machine Learning Techniques

Abu Selim Shah Newaz Alam Shuvo Mohammad Moniruzzaman M. M. Islam Sakifa Shah

The paper portrays a linear model for synchronous prediction of the dissolved oxygen (DO) level in a protected urban lake by using independent variables in real-time monitoring of the water quality parameters. Multi-linear regression and machine learning techniques were applied to the Dissolved Oxygen (DO) using the basic four parameters of Water Quality Index (WQI). Three real-time industry standard sensors PHEHT, CTZN, OPTOD were used for sampling, data was then interpolated through ArcGIS kriging method. 25 correlations were checked through the ML algorithm, a correlation heat map was produced and the top five relations were taken under consideration and validated by r-score and root mean squared error (RMSE) to develop the linear regression model. The performance of the model was validated through the RMSE, mean squared error (MSE), and mean absolute error (MAE) the r2 accuracy came to 0.983 when it is checked against the sample data. The suggested model can be used as a technique for unknown or malfunctioned sensor’s null DO data prediction.

Mean absolute error

Predictive modelling

10.20944/preprints202302.0083.v1

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Citations (2)

Predictive models for dissolved oxygen in an urban lake by regression analysis and artificial neural network

Total Environment Research Themes (2023)

Abu Selim Shah Newaz Alam Shuvo Md. Mohaimenul Islam Mohammad Moniruzzaman S. Shah

This paper portrays predictive models for Dissolved Oxygen (DO) levels in an urban lake using common water quality parameters like Temperature, pH, Conductivity and Oxidation Reduction Potential (ORP). Data were sampled using three real-time industry-standard sensors those are OPTOD, CTZN, and PHEHT, and then interpolated using the ArcGIS interpolation technique. Correlation studies were analyzed through the Machine Learning (ML) algorithm, the correlation study signified a positive linear correlation with DO against pH, temperature, salinity and conductivity and the model was corroborated by R-score which came to 0.687 and RMSE was 0.834. Multiple Linear Regression (MLR) model was developed to predict the DO with the correlated data of water parameters. In addition, an Artificial Neural Network (ANN) method using the Levenberg-Marquardt algorithm was developed to build a model to predict the DO as well. Then, the models’ performance was validated and the R2 accuracies were 0.963 for MLR and 0.93 for ANN and models were checked for the predicted data against the actual data. The appropriateness of the ANN model for forecasting investigated attributes is indicated by the fact that the discrepancy between the forecasted and real ANN model is significantly lesser than that of the regression model. The developed equation in this paper can be used to reveal DO data from unknown urban lake water.

Interpolation

Predictive modelling

10.1016/j.totert.2023.100066

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Citations (13)

Evaluating Cigarette Butt Pollution: Insights from Cox's Bazar Sea Beach of Bangladesh

Masum Howlader Abu Selim Shah Newaz Alam Shuvo Md. Monirul Islam Tania Sultana

Cigarette butts (CBs) have serious harmful effects on the environment and living organisms despite their small size. This research aims to investigate the abundance, densities and pollution status of CBs in Cox’s Bazar sea beach. A total of 13,988 CBs were collected, and the mean density was 0.388 m-2 and ranges from 0.195 – 0.689 m-2. The mean CBPI value was 7.78, which showed the “high pollution” status. According to CBPI analysis, 25%, 41.67%, 16.67%, and 16.67% were “severe pollution”, “considerable pollution”, “high pollution”, and “pollution” status, respectively. From HII values, 91.67% beach fell within the “Class III” category, indicating abundant toxic CBs in the study area. A total of 13 brands were identified, of which Derby (19.31%), B&H (18.54%), Star (11.26%) were more abundant brands. These findings from this study aim to increase awareness of the harmful effects of cigarette butts discarded intentionally or unintentionally by smokers.

10.2139/ssrn.4560905

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Anthropogenic Marine Litter Pollution Along the Coast of Sonadia Island: An Ecologically Critical Area (ECA) of Bangladesh

Masum Howlader Shah Newaz Alam Shuvo Abu Selim Md. Monirul Islam Sumaiya Sadaf

The utility of plastic undoubtedly enhances human life, yet its incursion into the natural environment exacts a significant toll on both humans and the ecosystem. The marine ecology, in particular, faces a dire threat from this plastic intrusion. To address this issue, we conducted a preliminary survey on the seven beaches of Sonadia Island, designated as an Ecologically Critical Area (ECA). Our research aim was to investigate the abundance of anthropogenic marine litter (AML) and assess environmental health using various metrics. A total of 13,410 AML items were collected, yielding an average density of 0.64 items/m2, with seven distinct litter categories constituting 76.41% of the total plastic. The Cleanliness Coast Index (CCI) revealed that 42.86% of beaches had 'moderate cleanliness,' 42.86% were 'dirty,' and 14.28% were 'extremely dirty.' The Plastic Abundance Index (PAI) indicated a 'moderate abundance' with an average score of 2.97. Furthermore, the Hazardous Item Index (HII) classified 85.72% of beaches as 'class II' and 14.28% as 'class III.' Also, overall, the Pollution Load Index (PLI) tallied at 21.34, signifying a 'hazard level III,' denoting high contamination with AML. Our research underscores the urgency of raising awareness regarding the detrimental impact of AML on marine biodiversity. We call upon government institutions, private organizations, and plastic manufacturers to adopt sustainable policies for AML management.

Marine Pollution

Litter

10.2139/ssrn.4606207

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Exploring Cigarette Butt Pollution in Saint Martin Island: A Marine Protected Area (Mpa)

Masum Howlader Abu Selim Mobin Hossain Shohan Shah Newaz Alam Shuvo Jehan Y. Al‐Humaidi

Littering cigarette butts (CBs) waste in an ecologically sensitive environment can have numerous adverse effects. The purpose of this research is to investigate the abundance and density of CBs in (SMI) and to assess the pollution status using the Cigarette Butt Pollution Index (CPBI). This study is conducted based on the visual survey method in the three types of land use zones of (SMI). A total of 4,481CBs items were counted and the density was ranged from 0.37 to 1.76 m-2 with an average value of 0.99 m-2 across 12 sampling campaigns. The highest density was observed at service zones and the fishing zones had the lowest density. The calculated CBPI values revealed that 75% of the sampling stations were in the “severe pollution” while 25% were classified as “high pollution” status, underscoring the prevalence of hazardous cigarette butts across most areas of SMI.

Marine Pollution

10.2139/ssrn.4715925

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Abundance and distribution of anthropogenic marine litter on the beaches of Sonadia Island: An Ecologically Critical Area

Regional Studies in Marine Science (2024)

Masum Howlader Shah Newaz Alam Shuvo Abu Selim Md. Monirul Islam Molla Rahman Shaibur

Marine debris

Litter

Marine protected area

10.1016/j.rsma.2024.103690

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Citations (1)