Browsing by Author "Reza, Abu"

Now showing 1 - 5 of 5

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.
Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification
(Scopus, 2024-09-14) Khan, Bodruzzaman; Das, Subhabrata; Fahim, Nafis Shahid; Banerjee, Santanu; Khan, Salma; Sadoon, Mohammad Khalid Al; Otaibi, Hamad S. Al; Reza, Abu; Islam, Md. Towfiqul
Manual identification of tomato leaf diseases is a time consuming and laborious process that may lead to inaccurate results without professional assistance. Therefore, an automated, early, and precise leaf disease recognition system is essential for farmers to ensure the quality and quantity of tomato production by providing timely interventions to mitigate disease spread. In this study, we have proposed seven robust Bayesian optimized deep hybrid learning models leveraging the synergy between deep learning and machine learning for the automated classification of ten types of tomato leaves (nine diseased and one healthy). We customized the popular Convolutional Neural Network (CNN) algorithm for automatic feature extraction due to its ability to capture spatial hierarchies of features directly from raw data and classical machine learning techniques [Random Forest (RF), XGBoost, GaussianNB (GNB), Support Vector Machines (SVM), Multinomial Logistic Regression (MLR), K Nearest Neighbor (KNN)], and stacking for classifications. Additionally, the study incorported a Boruta feature filtering layer to capture the statistically significant features. The standard, research oriented PlantVillage dataset was used for the performance testing, which facilitates benchmarking against prior research and enables meaningful comparisons of classification performance across different approaches. We utilized a variety of statistical classification metrics to demonstrate the robustness of our models. Using the CNN Stacking model, this study achieved the highest classification performance among the seven hybrid models. On an unseen dataset, this model achieved average precision, recall, f1 score, mcc, and accuracy values of 98.527%, 98.533%, 98.527%, 98.525%, and 98.268%, respectively. Our study requires only 0.174 s of testing time to correctly identify noisy, blurry, and transformed images. This indicates our approach’s time efficiency and generalizability in images captured under challenging lighting conditions and with complex backgrounds. Based on the comparative analysis, our approach is superior and computationally inexpensive compared to the existing studies. This work will aid in developing a smartphone app to offer farmers a real time disease diagnosis tool and management strategies.
Impact of anthropogenic activities and the associated heavy metal pollution in Sundarbans waterways: threats to commercial fish and human health
(2024) Anik, Amit Hasan; Ali, Mir Mohammad; Islam, Md. Saiful; Reza, Abu; Islam, Md. Towfiqul; Saha, Shantanu Kumar; Siddique, Md. Abu Bakar
The exposure of fish to heavy metals is a significant concern for human health and natural ecosystems. Despite being a critical issue, the extent of contamination in tropical fish from developing countries like Bangladesh remains somewhat unexplored. In this study, ten economically vital fish species (Osteogeneiosus militaris, Arius gagora, Har‑ padon nehereus, Mugil ephalus, Pseudapocryptes elongates, Apocryptes bato, Labeo bata, Tenualosa toil, Notopterus notopterus, and Pampus chinensis) from the Pasur River, Bangladesh, were analyzed by atomic absorption spectrometer for the concentrations of four concerned heavy metals, viz., As, Cr, Cd, and Pb, and the associated human health risks. The mean concentrations (mg/kg) followed the order of As (3.30±1.43)>Pb (2.32±0.73)>Cr (0.63±0.29)>Cd (0.37±0.24). Additionally, the bioaccumulation factor of the metals in the investigated fish species followed a decreasing trend of As (824.75)>Cr (781.25)>Cd (744)>Pb (385.83). While most species fell below the minimum bioaccumulation line, a few exceptions were noted for some species specific to metals. Health risk assessments indicated no significant carcinogenic and non-carcinogenic risks for both children and adults, although children exhibited greater vulnerability to both types of health effects. Multivariate analysis and local perceptions supported the conclusion that heavy metals primarily originated from anthropogenic sources related to development activities adjacent to the riverine areas.
Quantitative analysis and modeling of groundwater flow using visual MODFLOW: a case from subtropical coal mine, northwest Bangladesh
(Scopus, 2023-11-03) Khan, M. Selimul Hasnian; Haque, Md. Emdadul; Ahmed, Mohd.; Mallick, Javed; Reza, Abu; Islam, Md. Towfiqul; Fattah, Md. Abdul
Despite being a vital agricultural zone and livable land for millions of people, the northwest region of Bangladesh is facing a scarcity of groundwater, which has become a major environmental stress in recent years. To this end, the present study intends to evaluate the current groundwater condition and simulate it to predict groundwater flow in the Phulbari and Parbatipur upazilas in the Dinajpur district of subtropical coal mine, northwest Bangladesh, by applying the Visual MODFLOW model. Water table data was analyzed to assess the linear trends of groundwater levels. The exploration of coal mining influenced the groundwater resources in the study area, where the groundwater table declined at a rate of 0.142 m/year. During the last 35 years (1985–2020), the groundwater table decreased by 2.28 m at Parbatipur Upazila. In Phulbari Upazila, the water table has been declining at a rate of 0.201 m/year and has declined by 4.58 m over the last 35 years. The average recharge and discharge of 658,207.56 m3/day and 658,209.81 m3/day, respectively, indicate a deficit in recharge of 2.25 m3/day or 2250 L/day in the study area. The prediction results show that the shortage of groundwater will increase to 246,375,000 L annually by 2050. The progressive decline of the groundwater table is possibly due to a lack of replenishment, overexploitation of groundwater resources, and underground coal mining impacts. Overall, the study will help in policy planning for sustainable water resource management, waste supply, environmental protection and disaster preparedness.
The Novel Study On Arsenic Contamination, Health Risk, and Approaches to Its Mitigation From Water Resource of a Developing Country: A potential review
(Water Air Soil Pollut, 2024-11-02) Islam, Md.Saiful; Bakky, Abdullah Al; Reza, Abu; Islam, Md. Towiqul; Ali, Mir Mohammad; Islam, Md. Towhidul; Ismail, Zulhilmi; Hossain, Md. Tanvir bin; Ahmed, Sujat; Ibrahim, Khalid A.; Idris, Abubakr M.
The pollution and contamination by arsenic (As) in the water resources is a worldwide concern due to its adverse toxic effects on the environment and public health. The current study aimed to investigate arsenic levels in the groundwater system with the possible health risk, and sustainable mitigation strategies. The data on arsenic in the water system were collected from the Web of Science and Scopus databases. The published data showed that arsenic concentration (0.0002–19.0 mg/kg) in the water system in Bangladesh was higher than the permissible standards and data from other countries, indicating severe contamination of water resources by arsenic. The study concluded that the water resource in Bangladesh is not safe for human consumption. The review has also identified the research gaps in various strategies for controlling the arsenic problem and their impact on the ecosystems. The present study suggested future research directions on sustainable intervention, impacts assessment of arsenic on humans, and formulating existence policy that helps to combat arsenic contamination.