<p dir="ltr">Methodologies for optimizing UAVs' control for varied environmental conditions have become crucial in the recent development for UAV control sector, yet they are lacking. This research focuses on the dynamism of the Gazebo simulator and PX4 Autopilot flight controller, frequently referenced in academic sectors for their versatility in generating close-to-reality digital environments. This thesis proposed an integrated simulation system that ensures realistic wind and gust interactions in the digital world and efficient data extraction by employing an industrial standard control communication protocol called MAVLink with the also the industry standard ground control software QGroundControl, using real and historical weather information from NOAA database. This study also looks into the potential of reinforcement learning, namely the DDPG algorithm, in determining optimal UAV safety distance, trajectory prediction, and mission planning under wind disruption. The overall goal is to enhance UAV stability and safety in various wind-disturbed conditions. Mainly focusing on minimizing potential collision risks in areas such as streets, valleys, tunnels, or really anywhere has winds and obstacles. The ROS network further enhanced these components, streamlining UAV response analysis in simulated conditions. This research presents a machine-learning approach to UAV flight safety and efficiency in dynamic environments by synthesizing an integrated simulation system with reinforcement learning. And the results model has a high accuracy, reaching 91%, 92%, and 97% accuracy on average in prediction of maximum shifting displacement, and left/right shifting displacement, when testing with real wind parameters from KLAF airport. </p>

This research was aimed to evaluate the possibility of using satellite precipitation products (SPPs) and Numerical Weather Prediction (NWP) of precipitation for better hydrologic simulations and flood forecasting in the trans-boundary Chenab River Basin (CRB) in Pakistan. This research was divided into three parts. In the first part, two renowned SPPs, i.e., global precipitation mission (GPM) IMERG-F v6 and tropical rainfall measuring mission (TRMM) 3B42 v7, were incorporated in a semidistributed hydrological model, i.e., the soil and water assessment tool (SWAT), to assess the daily and monthly runoff pattern in Chenab River at the Marala Barrage gauging site in Pakistan. The results exhibit higher correlation between observed and simulated discharges at monthly timescale simulations rather than daily timescale simulations. Moreover, results show that IMERG-F is superior to 3B42 by indicating higher R2, higher Nash–Sutcliffe efficiency (NSE), and lower percent bias (PBIAS) at both monthly and daily timescale. In the second part, three latest half-hourly (HH) and daily (D) SPPs, i.e., 'IMERG-E', 'IMERGL', and 'IMERG-F', were evaluated for daily and monthly flow simulations in the SWAT model. The study revealed that monthly flow simulation performance is better than daily flow simulation in all sub-daily and daily SPPs-based models. Results depict that IMERGHHF and IMERG-DF yield the best performance among the other latency levels of SPPs. However, the IMERG-HHF based model has a reasonably higher daily correlation coefficient (R) and lower daily root mean square error (RMSE) than IMERG-DF. IMERG-HHF displays the lowest PBIAS for daily and monthly flow validations and it also represents relatively higher values of R2 and NSE than any other model for daily and monthly model validation. Moreover, the sub-daily IMERG based model outperformed the daily IMERG based model for all calibration and validation scenarios. IMERG-DL based model demonstrates poor performance among all of the SPPs, in daily and monthly flow validation, with low R2, low NSE, and high PBIAS. Additionally, the IMERG-HHE model outperformed IMERG-HHL. In the third and last part of this research, coupled hydro-meteorological precipitation information was used to forecast the 2016 flood event in the Chenab River Basin. The gaugecalibrated SPP, i.e., Global Satellite Mapping of Precipitation (GSMaP_Gauge), was selected to calibrate the Integrated Flood Analysis System (IFAS) model for the 2016 flood event. Precipitation from the Global Forecast System (GFS) NWP, with nine different lead times up to 4 days, was used in the calibrated IFAS model. This study revealed that the hydrologic simulations in IFAS, with global GFS forecasts, were unable to predict the flood peak for all lead times. Later, the Weather Research and Forecasting (WRF) model was used to downscale the precipitation forecasts with one-way and two-way nesting approaches. It was found in this study that the simulated hydrographs in the IFAS model, at different lead times, from the precipitation of two-way WRF nesting exhibited superior performance with the highest R2, NSE and the lowest PBIAS compared with one-way nesting. Moreover, it was concluded that the combination of GFS forecast and two-way WRF nesting can provide high-quality precipitation prediction to simulate flood hydrographs with a remarkable lead time of 96 h when applying coupled hydrometeorological flow simulation.