Abstract This paper investigates the impact of biohazards and pandemics on construc tion workers’ health and well-being. A proper selection of control methods for biohazards and pandemics like COVID-19 pandemic will result in im proved public health conditions. Occupational risks in the construction in dustry are also examined, with a focus on biohazards and pandemics, their containment, and the implications for health and safety. A safer work envi ronment in the construction industry is encouraged. In this study, some sta tistical methods were used to analyse the data obtained from sampled ques tionnaire. According to the findings, fewer people in poor developing coun tries get routine check-ups compared to developed countries. The construc tion industries studied have little or no insurance plans for staff. It also de monstrates that personal protective equipment (PPE) such as nasal masks,helmets, hand gloves, and work aprons can assist in the control of biohazards in the construction sector, such as asthma, cancer, and asbestosis. Thereshould be safety awareness programmes, conferences, and seminars, as well as first-aid kits and HSE and qualified health workers on all building sites. In addition, the government should examine the site for the usage of PPEs and verify that records of amily/personal medical history are maintained.

Abstract Agriculture and farming are mainly dependent on weather especially in Ma laysia as it received heavy rainfall throughout the years. An efficient crop or tree management system with a weather forecast needed for suitable planning of farming operation. Radial Basis Function Neural Network (RBFNN) algo rithm was used in this study to predict rainfall and the main focus of this study is to analyze the factor that affects the performance of neural model. This study found that the model works better the more hidden nodes and the optimum learning rate is 0.01 with the RMSE 49% and the percentage accu racy is 57%. Besides that, it is found that the meteorology data also affect the model performance. Future research can be conducted to improve the rainfall forecast of this study and improve the tree management system.

Abstract Incalaue is a tributary of Lugenda River in NSR (Niassa Special Reserve) in North-Eastern Mozambique. NSR is a data-poor remote area and there is a need for rainfall-runoff data to inform decisions on water resources management, and scientific methods are needed for this wide expanse of land. This study assessed the potential of a combination of NASA-POWER (National Aeronautics and Space Administration and Prediction of Worldwide Energy Resources) remotely sensed rainfall data and FAO (Food and Agriculture Organization of the United Nations) soil and land use/cover data for modelling rainfall-runoff in Incalaue river basin. DEM (Digital Elevation Model) of 1:250,000 scale and a grid resolution of 30 m × 30 m downloaded from USGS (the United States Geological Survey) website; clipped river basin FAO digital soil and land use/cover maps; and field-collected data were used. SWAT (Soil and Water Assessment Tool) model was used to assess rainfall -runoff data generated using the NASA-POWER dataset and gauged rainfall and river flow data collected during fieldwork. FAO soil and land use/cover datasets which are globally available and widely used in the region were used for comparison with soil data collected during fieldwork. Field collected data showed that soil in the area is predominantly sandy loam and only sand content and bulk density were uniformly distributed across the soil samples. SWAT model showed a good rainfall-runoff relationship using NASA-POWER data for the area (R2 = 0.7749) for the studied period (2019-2021). There was an equally strong rainfall-runoff relationship for gauged data (R2 = 0.8131). There were uniform trends for the rainfall, temperature, and relative humidity in NASA-POWER meteorological data. Timing of peaks and lows in rainfall and river flow observed in the field and modelled were confirmed by residents as the trend in the area. This approach was used because there was no historical rainfall and river flow data since the river basin is ungauged for hydrologic data. The study showed that NASA-POWER data has the potential for use for modelling the rainfall-runoff in the basin. The difference in rainfall-runoff relationship with field-collected data could be because of landscape characteristics or topsoil layer not catered for in the FAO soil data.