With climate change, hydro-climatic hazards, i.e., floods in the Himalayas regions, are expected to worsen, thus, likely to affect humans and socio-economic growth. Precisely, the Koshi River basin (KRB) is often impacted by flooding over the year. However, studies on estimating and predicting floods still lack in this basin. This study aims at developing flood probability map using machine learning algorithms (MLAs): gaussian process regression (GPR) and support vector machine (SVM) with multiple kernel functions including Pearson VII function kernel (PUK), polynomial, normalized poly kernel, and radial basis kernel function (RBF). Historical flood locations with available topography, hydrogeology, and environmental datasets were further considered to build flood model. Two datasets were carefully chosen to measure the feasibility and robustness of MLAs: training dataset (location of floods between 2010 and 2019) and testing dataset (flood locations of 2020) with thirteen flood influencing factors. The validation of the MLAs was evaluated using a validation dataset and statistical indices such as the coefficient of determination (r2: 0.546~0.995), mean absolute error (MAE: 0.009~0.373), root mean square error (RMSE: 0.051~0.466), relative absolute error (RAE: 1.81~88.55%), and root-relative square error (RRSE: 10.19~91.00%). Results showed that the SVM-Pearson VII kernel (PUK) yielded better prediction than other algorithms. The resultant map from SVM-PUK revealed that 27.99% area with low, 39.91% area with medium, 31.00% with high, and 1.10% area with very high probabilities of flooding in the study area. The final flood probability map could add a greatt value to the effort of flood risk mitigation and planning processes in KRB.

Road safety continues to be one of the great challenges facing upstream/downstream oil & gas companies. Since 2001, ADNOC Onshore has consistently improved its road safety performance and achieved its best performance in 2008 by recording Vehicle Accident Frequency (VAF) of 0.11. However, due to increased number of vehicles, induction of new drivers and increasing journey distances, the road safety performance has become a challenge to both ADNOC Onshore and its contractors. Drilling operations are risky activities due to its nature and driving, as one of the support services, is one of the riskiest activities In 2014, various initiatives were adopted, following an increased number of road traffic accidents (RTAs), to enhance road safety performance for drilling function through a barrier analysis approach. This approach included: Identification of key barriersAssessing strength of barriersAssessing effectiveness of barriers after strengthening One of the major risks in Oil & Gas Operations is injuries from Road Traffic Accidents. Drilling locations are situated in remote locations with access through Blacktop, sand and Gatch Roads driving over 25 Million kilometers (65 trips to moon) annually using both light and heavy vehicles to run 24 hour operations. Since 2014, key efforts (monitoring, assurance & governance) exerted to strengthening of barriers to prevent road traffic accidents, which initiated a behavioural change among drivers and resulted on a sharp decline in Vehicle Accident Frequency (VAF) from 0.35 in 2014 to 0.1 in 2017. In 2018, Q1 due to weakening of these barriers, VAF increased.

With climate change, hydro-climatic hazards, i.e., floods in the Himalayas regions, are expected to worsen, thus, likely to affect humans and socio-economic growth. Precisely, the Koshi River basin (KRB) is often impacted by flooding over the year. However, studies on estimating and predicting floods still lack in this basin.This study aims at developing flood probability map using machine learning algorithms (MLAs): gaussian process regression (GPR) and support vector machine (SVM) with multiple kernel functions including Pearson VII function kernel (PUK), polynomial, normalized poly kernel, and radial basis kernel function (RBF). Historical flood locations with available topography, hydrogeology, and environmental datasets were further considered to build flood model.Two datasets were carefully chosen to measure the feasibility and robustness of MLAs: training dataset (location of floods between 2010 and 2019) and testing dataset (flood locations of 2020) with thirteen flood influencing factors. 2The validation of the MLAs was evaluated using a validation dataset and statistical indices such as the coefficient of determination (r 2 : 0.546~0.995), mean absolute error (MAE: 0.009~0.373), root mean square error (RMSE: 0.051~0.466), relative absolute error (RAE: 1.81~88.55%), and root-relative square error (RRSE: 10.19~91.00%).Results showed that the SVM-Pearson VII kernel (PUK) yielded better prediction than other algorithms. The resultant map from SVM-PUK revealed that 27.99% area with low, 39.91% area with medium, 31.00% with high, and 1.10% area with very high probabilities of flooding in the study area. The final flood probability map could add a greatt value to the effort of flood risk mitigation and planning processes in KRB.

Over the last 10 years, Oil & Gas operations have come under tremendous pressures due to increasing production demands and economic conditions, demanding higher performance and venturing into technically challenging operating conditions. Operating with aging facilities in harsh environmental conditions and higher crew turnaround, have resulted in higher number of serious incidents. Prevention of incidents remains high on the agenda of oil & gas companies and focus is placed on incident investigations to identify root causes of incidents and development of corrective actions. However, repetition of incidents with similar findings and causes have been observed, raising concerns if right root causes were identified and focused corrective actions were identified and/or implemented. An extended analysis of over one thousand (1000) incidents was conducted to assess degree of repetition of causes and regrouping of causes to assess linkage of human factors with organizational behaviours. It was found that 31% of incidents were triggered by human errors & mistakes whilst 27% were attributed to violations. All violations were deemed as intentional & routine and further investigation was not undertaken. Management Supervion & Employees Leadership was identified a leading root cause category of incidents and this category contributed 20% of incidents followed by Work Planning (18%) and behaviour (12%). 55 % of incidents were caused by human factors and hauman factors were triggered by errors and mistakes rather than violations. Often efforts are exerted to to influence individual's behaviour however human attitude (cognitive, emotional and commitment) is overlooked as linkage between attitude change leading to behavior change, not fully explored. However, linkage from behavior change to attitude change is much stronger. If worker consciously change their behavior, it requires re adjustment of associated attitudes to align with the new behavior. Positive reinforcement is an effective tool to influence individual's behaviour. If discipline and punishment are used to discourage unsafe behavior, the intended results are not achieved (e.g., incident or near miss are not reported for fear of sanctions). Assessment of non-compliant behaviors (Violations, mistakes and errors) & conditions and factors influencing such behaviors are often not evaluated and focused action plans to address abilities and motivations with due consideration to isolated or systemic conditions are instrumental in preventing incicidents.

The present study described the dendrochronological studies of Pinus roxburghii and Pinus wallichiana growing in Ghora Galli, Murree. Study area was subdivided into roadside and away from roadside. It was found that along road side the maximum diameter, age, growth rate and height of Pinus roxburghii were 71 inches, 304 age, 0.277 and 97 feet respectively. Similarly maximum diameter, age, growth rate and height of Pinus roxburghii growing away from road side were 93.5 inches, 328 years, 0.358 inches/year and 99 feet height respectively. Maximum DBH, age, growth rate and height of Pinus wallichiana growing along the roadside was 63 inches, 223 years, 0.392 inches/year and 83 feet height respectively. While, studying the trees of Pinus wallichiana growing away from road side it was found that the maximum DBH, age, growth rate and height were 56.5 inches, 158 years, 0.437 years/inches and 62 respectively. It was also found that growth rate of trees growing away from the road was greater than those growing along the road. A significant correlation between growth rate and height was found While studying the correlation between height and growth rate, it was found that there was no any significant correlation. In vegetation studies, it was found that dominant trees were from Pinaceae family. Shrubs were with three families while herbs contained eleven families and ferns were found with only two families. Maximum IVI from trees was of Pinus wallichiana with 11.07 belonging to Pinaceae family and 7.73 for Hedera nepalensis belonging to Araliaceae family. Similarly, 4.27 was maximum IVI for Artemisia vulgaris of Asteraceae family and 5.44 from ferns the maximum IVI of Adiantum capillus belonging to Adiantaceae family.