Browsing by Author "Arabameri, Alireza"

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A New Approach from Public Behavioral Attitudes and Perceptions Towards Microplastics: Influencing Factors, and Policy Proposals
(Elsevier, 2024-07-01) Al Masud, Abdulla; Islam, Abu Reza Md Towfiqul; Al Mamun, Abdullah; Alam, G.M. Monirul; Arabameri, Alireza; Bari, A.B.M. Mainul; Pal, Subodh Chandra; Rakib, Md Refat Jahan; Senapathi, Venkatramanan; Bodrud-Doza, Md.; Idris, Abubakr M.; Malafaia, Guilherme
This research paper addresses the urgent environmental concern of microplastic (MP) emissions, focusing on the behavioral attitudes and perceptions of the general populace in Shyamnagar Upazila, Bangladesh. Against the backdrop of escalating MP pollution globally, this study investigates the level of awareness and the factors influencing public engagement in mitigating MP prevalence. Leveraging survey data from 350 respondents, the ordered logistic regression (OLR) and boosted regression tree (BRT) models are employed for comprehensive data analysis. The findings expose a concerning lack of awareness about MPs, as only 12% of respondents possessed prior knowledge, and a notable 63% remained uninformed about MP pollution. The OLR model reveals a positive correlation between heightened awareness of MPs and an increased willingness to take action. Gender differences become evident, with women exhibiting greater willingness than men to mitigate MP emissions, and environmental practitioners displaying heightened motivation. The BRT model underscores construction materials and industrial pollution as the primary influential factors amplifying MP pollution. These insights not only illuminate the existing scenario but also provide a basis for fostering favorable behavioral attitudes and perceptions to mitigate the prevalence of MPs within the coastal milieu.
A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models
(Scopus, 2024) Alam, Md Mahfuz; Akter, Mst. Yeasmin; Reza, Abu; Islam, Md Towfiqul; Mallick, Javed; Kabir, Zobaidul; Chu, Ronghao; Arabameri, Alireza; Pal, Subodh Chandra; Masud, Md Abdullah Al; Costache, Romulus; Senapathi, Venkatramanan
Evapotranspiration (ETo) is a complex and non-linear hydrological process with a significant impact on efficient water resource planning and long-term management. The Penman-Monteith (PM) equation method, developed by the Food and Agriculture Organization of the United Nations (FAO), represents an advancement over earlier approaches for estimating ETo. Eto though reliable, faces limitations due to the requirement for climatological data not always available at specific locations. To address this, researchers have explored soft computing (SC) models as alternatives to conventional methods, known for their exceptional accuracy across disciplines. This critical review aims to enhance understanding of cutting-edge SC frameworks for ETo estimation, highlighting advancements in evolutionary models, hybrid and ensemble approaches, and optimization strategies. Recent applications of SC in various climatic zones in Bangladesh are evaluated, with the order of preference being ANFIS > Bi-LSTM > RT > DENFIS > SVR-PSOGWO > PSO–HFS due to their consistently high accuracy (RMSE and ). This review introduces a benchmark for incorporating evolutionary computation algorithms (EC) into ETo modeling. Each subsection addresses the strengths and weaknesses of known SC models, offering valuable insights. The review serves as a valuable resource for experienced water resource engineers and hydrologists, both domestically and internationally, providing comprehensive SC modeling studies for ETo forecasting. Furthermore, it provides an improved water resources monitoring and management plans.
Flood Hazard Potential Evaluation Using Decision Tree State-of-the-Art Models
(John Wiley & Sons, 2023-07-25) Costache, Romulus; Arabameri, Alireza; Costache, Iulia; Crăciun, Anca; Islam, Abu Reza Md. Towfiqul; Abba, Sani Isah; Sahana, Mehebub; Pandey, Manish; Tin, Tran Trung; Pham, Binh Thai
Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research. With the development of modern computerized methods such as geographic information system and machine learning models, and as a result of the possibility of combining them, the determination of areas susceptible to floods has become increasingly accurate, and the algorithms used are increasingly varied. Some of the most used and highly accurate machine learning algorithms are the decision tree models. Therefore, in the present study focusing on flood susceptibility zonation mapping in the Trotus River basin, the following algorithms were applied: forest by penalizing attribute—weights of evidence (forest-PA-WOE), best first decision tree—WOE, alternating decision tree—WOE, and logistic regression—WOE. The best performant, characterized by a maximum accuracy of 0.981, proved to be forest-PA-WOE, whereas in terms of flood exposure, an area of over 16.22% of the Trotus basin is exposed to high and very high floods susceptibility. The performances applied models in the present work are higher than the models applied in the previous studies in the same study area. Moreover, it should be noted that the accuracy of the models is similar with the accuracies of the decision tree models achieved in the studies focused on other areas across the world. Therefore, we can state that the models applied in the present research can be successfully used in by the researchers in other case studies. The findings of this research may substantially map the flood risk areas and further aid watershed managers in limiting and remediating flood damage in the data-scarce regions. Moreover, the results of this study can be a very useful for the hazard management and planning authorities.
Flood hazard potential evaluation using decision tree state-of-the-art models
(2023-06-25) Costache, Romulus; Arabameri, Alireza; Costache, Iulia; Crăciun, Anca; Md. Towfiqul Islam, Abu Reza; Abba, Sani Isah; Sahana, Mehebub; Pandey, Manish; Tin, Tran Trung; Pham, Binh Thai
Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research. With the development of modern computerized methods such as geographic information system and machine learning models, and as a result of the possibility of combining them, the determination of areas susceptible to floods has become increasingly accurate, and the algorithms used are increasingly varied. Some of the most used and highly accurate machine learning algorithms are the decision tree models. Therefore, in the present study focusing on flood susceptibility zonation mapping in the Trotus River basin, the following algorithms were applied: forest by penalizing attribute—weights of evidence (forest-PA-WOE), best first decision tree—WOE, alternating decision tree—WOE, and logistic regression—WOE. The best performant, characterized by a maximum accuracy of 0.981, proved to be forest-PA-WOE, whereas in terms of flood exposure, an area of over 16.22% of the Trotus basin is exposed to high and very high floods susceptibility. The performances applied models in the present work are higher than the models applied in the previous studies in the same study area. Moreover, it should be noted that the accuracy of the models is similar with the accuracies of the decision tree models achieved in the studies focused on other areas across the world. Therefore, we can state that the models applied in the present research can be successfully used in by the researchers in other case studies. The findings of this research may substantially map the flood risk areas and further aid watershed managers in limiting and remediating flood damage in the data-scarce regions. Moreover, the results of this study can be a very useful for the hazard management and planning authorities.
Spatio-Temporal Assessment of Water Quality of a Tropical Decaying River in India for Drinking Purposes and Human Health Risk Characterization
(Springer Nature, 2023-09-01) Hoque, Md. Mofizul; Islam, Aznarul; Islam, Abu Reza Md Towfiqul; Das, Balai Chandra; Pal, Subodh Chandra; Arabameri, Alireza; Khan, Rituparna
River water pollution and water-related health problems are common issues across the world. The present study aims to examine the Jalangi River’s water quality to assess its suitability for drinking purposes and associated human health risks. The 34 water samples were collected from the source to the mouth of Jalangi River in 2022 to depict the spatial dynamics while another 119 water samples (2012–2022) were collected from a secondary source to portray the seasonal dynamics. Results indicate better water quality in the lower reach of the river in the monsoon and post-monsoon seasons. Principal component analysis reveals that K+, NO3−, and total alkalinity (TA) play a dominant role in controlling the water quality of the study region, while, CaCO3, Ca2+, and EC in the pre-monsoon, EC, TDS, Na+, and TA in the monsoon, and EC, TDS and TA in the post-monsoon controlled the water quality. The results of ANOVA reveal that BOD, Ca2+, and CaCO3 concentrations in water have significant spatial dynamics, whereas pH, BOD, DO, Cl−, SO42−, Na+, Mg2+, Ca2+, CaCO3, TDS, TA, and EC have seasonal dynamics (p < 0.05). The water quality index depicts that the Jalangi River’s water quality ranged from 6.23 to 140.83, i.e., excellent to unsuitable for drinking purposes. Human health risk analysis shows that 32.35% of water samples have non-carcinogenic health risks for all three groups of people, i.e., adults, children, and infants while only 5.88% of water samples have carcinogenic health risks for adults and children. The gradual decay of the Jalangi River coupled with the disposal of urban and agricultural effluents induces river pollution that calls for substantial attention from the various stakeholders to restore the water quality.