Reaction profile (kcal mol−1) for (CF3)2CHOCHFO radical at the M06-2X/6-311++G(d,p) level.

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Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results.

A Theoretical study on the mechanism of the reactions of CF2ClC(O)OCH3 with the OH radical and Cl atom is presented. Geometry optimization and frequency calculations have been performed at the MPWB1K/6-31+G(d,p) level of theory and energetic information is further refined by calculating the energy of the species using G2(MP2) theory. Transition states are searched on the potential energy surface involved during the reaction channels and each of the transition states are characterized by presence of only one imaginary frequency. The existence of transition states on the corresponding potential energy surface is ascertained by performing intrinsic reaction coordinate (IRC) calculation. Theoretically calculated rate constants at 298 K and atmospheric pressure using the canonical transition state theory (CTST) are found to be in good agreement with the experimentally measured ones. Using group-balanced isodesmic reactions as working chemical reactions, the standard enthalpies of formation for CF2ClC(O)OCH3, CF2ClC(O)OCH2 and CF3C(O)OCH3 are also reported for the first time.

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