Internet of Things early flood warning system with ethology input and fuzzy logic

Flood is considered as a serious natural disaster in Asia. Flood has affected millions of people in Asia in the recent years including Malaysia and its neighboring countries. The severity of the problems resulted from flood has significantly affected the government in terms of economic and social. Information Communication Technology (ICT) can be utilized in addressing flood challenge by contributing in the aspects of early flood warning as well as alerting the affected community. Early flood warning systems face several challenges in terms of warning dissemination that is not timely, people centered, accessible and explainable. Thus, this study developed an Internet of Thing (IoT) early flood warning system (IEFWS) with ethological input using fuzzy logic in order to come up with a timely, precise and low cost flood warning system. The IEFWS of fuzzy logic application included several nature input data membership functions specifically temperature, humidity, rainfall intensity, water raise rate, sound, and motion indicators were all being updated on the internet simultaneously in less then 0:00:05 seconds. This study also included an ethological input data of fish by analyzing the behavior of sound and movement of fish as indicators to early warning before flood occurrence. The system was tested and evaluated in terms of timely and preciseness of it to update sensor data to the internet and apply fuzzy logic to intelligently alert flood warning. The results showed that the system was able to update ubiquitous data for a better monitoring system platform. In addition, the system is low cost and easy to handle. In conclusion, the IoT early flood warning system is timely and precise as the data are updated at a very minimum delay and it could easily monitor the changes of climate

A cloned linguistic decision tree controller for real-time path planning in hostile environments

AbstractThe idea of a Cloned Controller to approximate optimised control algorithms in a real-time environment is introduced. A Cloned Controller is demonstrated using Linguistic Decision Trees (LDTs) to clone a Model Predictive Controller (MPC) based on Mixed Integer Linear Programming (MILP) for Unmanned Aerial Vehicle (UAV) path planning through a hostile environment. Modifications to the LDT algorithm are proposed to account for attributes with circular domains, such as bearings, and discontinuous output functions. The cloned controller is shown to produce near optimal paths whilst significantly reducing the decision period. Further investigation shows that the cloned controller generalises to the multi-obstacle case although this can lead to situations far outside of the training dataset and consequently result in decisions with a high level of uncertainty. A modification to the algorithm to improve the performance in regions of high uncertainty is proposed and shown to further enhance generalisation. The resulting controller combines the high performance of MPC–MILP with the rapid response of an LDT while providing a degree of transparency/interpretability of the decision making

A linguistic decision tree approach to predicting storm surge

A linguistic decision tree algorithm (LID3) is applied to the problem of predicting storm surge. Of particular interest is the prediction of large positive storm surge for flood warning purposes. The application site is the North Sea which has a well-understood physical system for the generation and progression of storm surge, which lends itself to testing of the LID3 algorithm on a real-world prediction problem. Using available water level and meteorological data, the decision tree provides predictions of surge on the Thames Estuary up to View the MathML source in advance, accurate to the order of View the MathML source, which is comparable to alternative data driven methods. However, the success of the data driven approaches applied here are all limited by the sparsity of training data for extreme events (which by their nature are rare). A major benefit of the decision tree approach is the ability to make inference from the resulting IF–THEN rules of the tree structure. In this application of the LID3 algorithm, clear and plausible model rules can be deduced from the tree structure that are consistent with our understanding of the physical drivers of storm surge at this location. The label semantic framework is interpreted probabilistically, allowing the user to employ standard statistical approaches to identify statistically significant rules. It is demonstrated that the rules can successfully discriminate between surges that may pose a threat and those that should not, based on tide gauge measurements available up to View the MathML source prior to the surge signal reaching the Thames Estuary. This is promising for the potential application of such computationally efficient and easy to implement rule learning algorithms for the further investigation of complex environmental systems