In the recent years, the use of ensembles model in forecasting and assessing the uncertainty in engineering field has become more common, especially in weather forecasting. The main idea of ensemble model is to reduce the model and measurement error caused by the measurement data noises or even the structural uncertainty. This issue has been studied as an uncertainty quantification problem and the main goal is to assess the uncertainty from either model or measurement device so that they can be anticipated and reduced to obtain the almost optimal result. One of the ground breaking techniques to deal with this is, called filtering, which used for data assimilation method. Kalman Filter had been studied and developed throughout the years and has touches many fields in the engineering field such as weather forecasting, GPS system, and even leak detection techniques. It has gained popularity because of its simple conceptual formulation, relative ease implementation and versatile to be implemented and adjusted in certain problem. This research uses a modified Kalman Filter for ensemble models to calibrate hydraulic parameters. Water is the most essential element for human and every living creature. Water scarcity becomes one of the global major problem. Because of this, the need of a well-developed leak detection system is to reduce the water loss due to leak is needed for a better water distribution system. Depart from this issue, this research implements a modified Kalman Filter combined with the Symbiotic Organism Search Algorithm for an internal leak detection system technique in a pipeline system.

In the recent years, the use of ensembles model in forecasting and assessing the uncertainty in engineering field has become more common, especially in weather forecasting. The main idea of ensemble model is to reduce the model and measurement error caused by the measurement data noises or even the structural uncertainty. This issue has been studied as an uncertainty quantification problem and the main goal is to assess the uncertainty from either model or measurement device so that they can be anticipated and reduced to obtain the almost optimal result. One of the ground breaking techniques to deal with this is, called filtering, which used for data assimilation method. Kalman Filter had been studied and developed throughout the years and has touches many fields in the engineering field such as weather forecasting, GPS system, and even leak detection techniques. It has gained popularity because of its simple conceptual formulation, relative ease implementation and versatile to be implemented and adjusted in certain problem. This research uses a modified Kalman Filter for ensemble models to calibrate hydraulic parameters. Water is the most essential element for human and every living creature. Water scarcity becomes one of the global major problem. Because of this, the need of a well-developed leak detection system is to reduce the water loss due to leak is needed for a better water distribution system. Depart from this issue, this research implements a modified Kalman Filter combined with the Symbiotic Organism Search Algorithm for an internal leak detection system technique in a pipeline system.

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