Countries around the world have already been experiencing the repercussions of climate change. Bangladesh is cited as one of the most vulnerable countries among them. Due to the utmost contribution ...to the country’s economy and continuous exposure to climatic extremes, climate change scenarios for the largest division in the country, the Chittagong Division, have a major concern. This study analyzed the potential climatic changes by the downscaling approach for the Chittagong Division under Representative Concentration Pathways (RCPs) i.e., RCP 2.5, RCP 4.5 and RCP 8.5, and Special Report on Emission Scenarios (SRES) i.e., A2, A1B, and B2 scenarios. Statistical Downscaling Model (SDSM) was used for downscaling three General Circulation Models (GCMs) viz. HadCM3, CanESM2, and CGCM3. A quantitative approach was used for both calibration and validation, where the results indicated the suitability of SDSM for downscaling daily mean temperature and precipitation under different scenarios for three future time horizons, i.e., early-twenty-first, mid-twenty-first, and late-twenty-first century. Additionally, bias correction was applied to downscaled climate variables. The downscaled projection showed increasing trends in mean annual temperature and precipitation for all the scenarios by the end of the century. Under CanESM2, the highest increase in temperature and precipitation were projected as 1.1 °C and 1.7 mm for the RCP 8.5. On the other hand, the highest increase in temperature and precipitation were projected as 0.5 °C and 1.4 mm for the SRES scenario A2 under CGCM3 and HadCM3. The spatial distribution of projections shows that the southern coastal part of the division is marked by remarkable future changes. The downscaled pathways have set a basis for assessing the impacts of future climate change on different sectors for the Chittagong Division and other areas in the country.
Atmospheric particle pollution causes acute and chronic health effects. Predicting the concentrations of PM
2.5
and PM
10
, therefore, is a prerequisite to avoid the consequences and mitigate the ...complications. This research utilized the machine learning (ML) models such as linear-support vector machine (L-SVM), medium Gaussian-support vector machine (M-SVM), Gaussian process regression (GPR), artificial neural network (ANN), random forest regression (RFR), and a time series model namely PROPHET. Atmospheric NO
X
, SO
2
, CO, and O
3
, along with meteorological variables from Dhaka, Chattogram, Rajshahi, and Sylhet for the period of 2013 to 2019, were utilized as exploratory variables. Results showed that the overall performance of GPR performed better particularly for Dhaka in predicting the concentration of both PM
2.5
and PM
10
while ANN performed best in case of Chattogram and Sylhet for predicting PM
2.5
. However, in terms of predicting PM
10
, M-SVM and RFR were selected respectively. Therefore, this study recommends utilizing “ensemble learning” models by combining several best models to advance application of ML in predicting pollutants’ concentration in Bangladesh.
•Monitoring body composition is a crucial aspect for improving livestock management.•Conventional methods to determine animals' body composition have certain limitations.•Three machine learning ...models for predicting pigs' body composition were developed.•SVR outperformed MLR and RFR models in predicting fat mass and fat-free mass in pigs.•Combination of MP, FI and STP as parameters can predict body composition accurately.
Timely monitoring and precise estimation of body composition parameters, such as fat mass (FM) and fat-free mass (FFM), are crucial for pig production. Therefore, this study aimed to utilize three machine learning models, namely multiple linear regression (MLR), random forest regression (RFR), and support vector regression (SVR), to predict FM and FFM in growing-finishing pigs using four input combinations of three variables, i.e., mass of pigs, feed intake, and surface temperature of pigs. An ultrasound-based back-fat depth measurement approach was used to determine FM and FFM, and these measurements were compared with reference measurements obtained from slaughtered pigs. Data from two experimental periods in 2021 and 2022 were used for training and testing these models. Performance metrics, including the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the models' performance and stability. The results showed that the SVR model had the highest accuracy in predicting FM and FFM, with the ability to explain the relationship between input and target variables up to 94.4% in FM and 94.6% in FFM prediction. Additionally, the SVR model consistently outperformed the RFR and MLR models in predicting FM, with an increase in R2 of up to 6.72% and 27.96%, respectively, and a reduction in RMSE of up to 24.06% and 36.82%, respectively, across different input combinations. Similar results were obtained in FFM prediction, where the SVR model showed an increase in R2 of up to 6.47% and 22.45%, and a reduction in RMSE of up to 23.96% and 36.57% compared to RFR and MLR models, respectively. Moreover, the SVR model demonstrated the highest stability, with only 2.9% to 3.3% decrease in R2 during the testing phase compared to the training phase, while the RFR model exhibited the worst stability. Findings of the present study suggested that the SVR model was the most stable and reliable, along with the ultrasound-based back-fat depth approach for measuring FM and FFM in growing-finishing pigs. This approach could aid in monitoring meat quality and providing a rapid overview of body composition for pig farmers.
Atmospheric particulate matter (PM) has major threats to global health, especially in urban regions around the world. Dhaka, Narayanganj and Gazipur of Bangladesh are positioned as top ranking ...polluted metropolitan cities in the world. This study assessed the performance of the application of hybrid models, that is, Autoregressive Integrated Moving Average (ARIMA)-Artificial Neural Network (ANN), ARIMA-Support Vector Machine (SVM) and Principle Component Regression (PCR) along with Decision Tree (DT) and CatBoost deep learning model to predict the ambient PM2.5 concentrations. The data from January 2013 to May 2019 with 2342 observations were utilized in this study. Eighty percent of the data was used as training and the rest of the dataset was employed as testing. The performance of the models was evaluated by R2, RMSE and MAE value. Among the models, CatBoost performed best for predicting PM2.5 for all the stations. The RMSE values during the test period were 12.39 µg m−3, 13.06 µg m−3 and 12.97 µg m−3 for Dhaka, Narayanganj and Gazipur, respectively. Nonetheless, the ARIMA-ANN and DT methods also provided acceptable results. The study suggests adopting deep learning models for predicting atmospheric PM2.5 in Bangladesh.
The study represents the estimation of energy-based CO2 emission and the health risks of workers involved in the shipbreaking industries in Sitakunda, Bangladesh. To calculate the carbon emission ...(CE) from three shipbreaking activities, i.e., metal gas cutting (GC), diesel fuel (FU) and electricity consumption (EC), we used the Intergovernmental Panel on Climate Change (IPCC) guidelines and Environmental Protection Agency (EPA)’s Emission and Generation Resource Integrated Database (eGRID) emission factors. Moreover, the geographic weighted regression (GWR) model was applied to assess the contribution of influencing factors of CE throughout the sampling points. To assess the workers’ health condition and their perceptions on environmental degradation, a semi-structured questionnaire survey among 118 respondents were performed. The results showed that total CO2 emissions from GC were 0.12 megatons (MT), 11.43 MT, and 41.39 MT for daily, monthly, and yearly respectively, and the values were significantly higher than the surrounding control area. Emissions from the FU were estimated as daily: 0.85 MT, monthly: 1.92 MT, and yearly: 17.91 MT, which were significantly higher than EC. The study also revealed that workers were very susceptible to accidental hazards especially death (91%), and pollution (79%). Environmental consequences and health risks of the workers in shipbreaking industry warrant more attention nationally and internationally at the industry-level.
•Factors influencing drinking water intake in swine buildings are not fully understood.•Monitoring and evaluating drinking water are crucial for farm profitability.•Statistical and machine learning ...models were developed for drinking water estimation.•The accuracy of the random forest model was higher than other tested models.•Body mass and feed intake mostly influence the drinking water intake in pigs.
Effective monitoring and management of drinking water in swine buildings is a crucial aspect for promoting pigs' health and productivity. Therefore, this study aimed to quantify and model drinking water intake (DWI) in growing-finishing pigs by providing them with three concentrated diets in experimental pig barns. Two independent experiments were conducted in three experimental barns between 2021 and 2022. One statistical (multiple linear regression) and four machine learning algorithms (elastic net, random forest regression, support vector regression, and multilayer perceptron) were employed, with feed intake (FI), mass of pigs (MP), pigs' body temperature (PBT), room temperature (RT), CO2 concentration (RCO2), and temperature-humidity index (RTHI) as input parameters. The results revealed that pigs with a body mass of 30 to 60 kg consumed approximately 3.58 L of drinking water and 2.10 kg of concentrated diet per day. Additionally, strong positive correlations were observed between MP, FI, and DWI (correlation coefficient (r) > 90) during both experimental periods. The findings indicated that the random forest regression algorithm performed the best, explaining over 90% and 80% of the observed and predicted data during the training and testing phases, respectively. However, during the testing phase, the multiple linear regression methods performed the worst (R2 < 0.79 and RMSE > 0.89 L pig−1 day−1) when compared to the other models. Sensitivity analysis indicated that among all the variables, MP had the greatest impact on predicting DWI, followed by FI, RCO2, RTHI, and RT. The study concluded that random forest regression could predict DWI precisely, which can assist pig farmers in enhancing their water monitoring capabilities and promptly assessing the availability of drinking water.
Pig farming is one of the major sources of greenhouse gas (GHG) emissions in the agricultural sector; nevertheless, few studies have been undertaken to directly measure or estimate GHGs, particularly ...carbon dioxide (CO
2
) from pig barns. Therefore, the main objective of the present research was to estimate and predict CO
2
emission rate as a function of the mass of pigs and feed consumption. Two identical experiments were carried out in experimental pig barns in 2020 and 2021 to develop and evaluate the performance of CO
2
emission model. The CO
2
emission data (ppm) were collected utilizing Livestock Environment Management Systems (LEMS) and weather sensors, respectively within the pig barns and the outside environment. The models were built using seven statistical and machine learning–based regression algorithms, i.e., linear, multiple linear, polynomial, exponential, ridge, lasso, and elastic net. The findings of the study revealed that among the seven models, the exponential-based regression model performed the best, with a coefficient of determination (
R
2
) greater than 0.78 in the training stage and 0.75 in the testing stage being suitable to describe the relationship between the feed intake and the rate of CO
2
emission. However, when compared to the other models in the testing stage, the lasso model had the worst performance (
R
2
< 0.65 and RMSE > 20.00 ppm). In conclusion, this study recommends employing an exponential-based regression model by taking feed intake as an input variable in predicting CO
2
for a small number of the experimental dataset.
The study represents the estimation of energy-based CO.sub.2 emission and the health risks of workers involved in the shipbreaking industries in Sitakunda, Bangladesh. To calculate the carbon ...emission (CE) from three shipbreaking activities, i.e., metal gas cutting (GC), diesel fuel (FU) and electricity consumption (EC), we used the Intergovernmental Panel on Climate Change (IPCC) guidelines and Environmental Protection Agency (EPA)'s Emission and Generation Resource Integrated Database (eGRID) emission factors. Moreover, the geographic weighted regression (GWR) model was applied to assess the contribution of influencing factors of CE throughout the sampling points. To assess the workers' health condition and their perceptions on environmental degradation, a semi-structured questionnaire survey among 118 respondents were performed. The results showed that total CO2 emissions from GC were 0.12 megatons (MT), 11.43 MT, and 41.39 MT for daily, monthly, and yearly respectively, and the values were significantly higher than the surrounding control area. Emissions from the FU were estimated as daily: 0.85 MT, monthly: 1.92 MT, and yearly: 17.91 MT, which were significantly higher than EC. The study also revealed that workers were very susceptible to accidental hazards especially death (91%), and pollution (79%). Environmental consequences and health risks of the workers in shipbreaking industry warrant more attention nationally and internationally at the industry-level.
PDF
