Optimization of Monitoring Network to the Rainfall Distribution by Using Stochastic Search Algorithms: Lesson from Pakistan

Agricultural production is greatly influenced by environmental parameters such as temperature, rainfall, humidity, and wind speed. The accurate information about plays a vital useful role when making policies for the agriculture sector well other sectors. Pakistan meteorological department observed these at more than 90 stations. allocation of monitoring stations not made systematically correct. This leads to inaccurate predictions unobserved locations. study aims propose network which prediction errors can be minimized. well-known techniques named, model-based ordinary kriging universal (UK) with known Matheron variogram model are used purposes. We investigate rainfall focus on both optimal deletion/addition from/to this network. two stochastic search algorithms, spatial simulated annealing, genetic algorithm optimization Furthermore, minimization Average Kriging Variance (AKV) taken interpolation accuracy measure. annealing exhibits lower AKV compared Genetic adding/removing optimal/redundant locations from