Interacted Multiple Ant Colonies Optimization (IMACO) is a newly proposed framework. In this framework several colonies of artificial ants are utilized. These colonies are working cooperatively to solve an optimization problem using some interaction technique. Exploration technique is doing an essential job in this framework. This technique is responsible for directing the activity of utilized colonies towards the different parts of the huge search space. This paper describes the newly proposed IMACO framework and proposes an effective exploration technique. Computational tests show that the new exploration technique can furthermore improve the IMACO performance. These tests also show the capability of IMACO to outperform other well known ant algorithms like ant colony system and max-min ant system.

Reservoir is one of the emergency environments that required fast an accurate decision to reduce flood risk during heavy rainfall and contain water during less rainfall. Typically, during heavy rainfall, the water level increase very fast, thus decision of the water release is timely and crucial task. In this paper, intelligent decision support model based on neural network (NN) is proposed. The proposed model consists of situation assessment, forecasting and decision models. Situation assessment utilized temporal data mining technique to extract relevant data and attribute from the reservoir operation record. The forecasting model utilize NN to perform forecasting of the reservoir water level, while in the decision model, NN is applied to perform classification of the current and changes of reservoir water level. The simulations have shown that the performances of NN for both forecasting and decision models are acceptably good.

Reservoir is one of the structural approaches for flood mitigation and water supply. During heavy raining season, reservoir operator has to determine fast and accurate decision in order to maintain both reservoir and downstream river water level. In contrast to less rainfall season, the reservoir needs to impound water for the water supply purposes. This study is aimed to model human expert decision making specifically on reservoir water release decision. Reservoir water release decision is crucial as reservoir serve multi purposes. The reservoir water release decision pattern that comprises of upstream rainfall and current reservoir water level has been form using sliding window technique. The computational intelligence method called artificial neural network was used to model the decision making.