An early warning system is one of the best solutions for flood management. It enhances the reservoir floodgate operation, flood proofing, flood insurance, and flood plain zoning processes. The timeline and accuracy of the decision to open the reservoir gate are critical. Any delay might damage the dam caused by the excess water, leading to flash flood to the surrounding areas. Consequently, huge losses could be incurred, such as the loss of lives, properties, and financial ruins. At this point, the reservoir floodgate operator would sometimes be hesitant to make an early decision due to inconsistency in getting heavy rainfall data readings, which are under manual control. Therefore, this paper proposes the use of an autonomous agent-based system based on the Artificial Immune Systems (AIS). This system would immediately send readings to the reservoir operator so that gradual release of water from the reservoir can be done to prevent it from bursting. This task requires a certain level of experience and expertise in decision making to ensure no water shortage due to the volume of water to be released. This proposed early warning system is based on the immune negative selection and intelligent agent to monitor the changes in the reservoir water level. This autonomous early detection mechanism can support the domain expert’s job to recognise the threats early on, which is required to improve the monitoring procedure.

Reservoir water level monitoring is an important process during heavy or light rainfall to determine the volume of reserved water. Mistakes in data recording by the dam operator can lead to disasters. Data from different gauging stations are collected to determine whether to release water in the dam or not. The decision to release water is critical because it can affect the volume of water left in the dam for both drought and flood seasons. Constant water level monitoring is difficult because of the changes in water level. To overcome this issue, intelligent agent-based architecture is proposed for reservoir water level monitoring by imitating the artificial immune system. This paper presents the agent technology where agents communicate with each other concurrently by sending online data from different gauging stations to the main reservoir. One of the techniques in the artificial immune system is known as negative selection and this technique has been chosen as a water level monitoring model.