Geological Resource Planning and Environmental Impact Assessments Based on GIS

A Machine Learning-Based Water Potability Prediction Model by Using Synthetic Minority Oversampling Technique and Explainable AI

During the last few decades, the quality of water has deteriorated significantly due to pollution and many other issues. As a consequence of this, there is a need for a model that can make accurate projections about water quality. This work shows the comparative analysis of different machine learning approaches like Support Vector Machine (SVM), Decision Tree (DT), Random Forest, Gradient Boost, and Ada Boost, used for the water quality classification. The model is trained on the Water Quality Index dataset available on Kaggle. Z-score is used to normalize the dataset before beginning the training process for the model. Because the given dataset is unbalanced, Synthetic Minority Oversampling Technique (SMOTE) is used to balance the dataset. Experiments results depict that Random Forest and Gradient Boost give the highest accuracy of 81%. One of the major issues with the machine learning model is lack of transparency which makes it impossible to evaluate the results of the model. To address this issue, explainable AI (XAI) is used which assists us in determining which features are the most important. Within the context of this investigation, Local Interpretable Model-agnostic Explanations (LIME) is utilized to ascertain the significance of the features.

AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities

As the number of vehicles increases, road accidents are on the rise every day. According to the World Health Organization (WHO) survey, 1.4 million people have died, and 50 million people have been injured worldwide every year. The key cause of death is the unavailability of medical care at the accident site or the high response time in the rescue operation. A cognitive agent-based collision detection smart accident alert and rescue system will help us to minimize delays in a rescue operation that could save many lives. With the growing popularity of smart cities, intelligent transportation systems (ITS) are drawing major interest in academia and business, and are considered as a means to improve road safety in smart cities. This article proposed an intelligent accident detection and rescue system which mimics the cognitive functions of the human mind using the Internet of Things (IoTs) and the Artificial Intelligence system (AI). An IoT kit is developed that detects the accident and collects all accident-related information, such as position, pressure, gravitational force, speed, etc., and sends it to the cloud. In the cloud, once the accident is detected, a deep learning (DL) model is used to validate the output of the IoT module and activate the rescue module. Once the accident is detected by the DL module, all the closest emergency services such as the hospital, police station, mechanics, etc., are notified. Ensemble transfer learning with dynamic weights is used to minimize the false detection rate. Due to the dataset’s unavailability, a personalized dataset is generated from the various videos available on the Internet. The proposed method is validated by a comparative analysis of ResNet and InceptionResnetV2. The experiment results show that InceptionResnetV2 provides a better performance compared to ResNet with training, validation, and a test accuracy of 98%, respectively. To measure the performance of the proposed approach in the real world, it is validated on the toy car.

Development of a webGIS Application to Assess Conflicting Activities in the Framework of Marine Spatial Planning

Marine spatial planning (MSP) has been established as the appropriate policy framework to study and resolve conflicts that arise among various activities. A pre-requisite for the successful implementation of MSP is the availability of efficient tools to support decision-makers and enhance stakeholders’ engagement. In this paper, a webGIS application is proposed that is able to assess the intensity of conflicts among marine activities; the area of the Cyclades in the Aegean Sea was used as a case study. The webGIS application allows the visualization of existing activities, the delineation of conflicting activities, the detection of areas where multiple conflicts co-exist, and the delineation of areas of conflicts based on specific criteria. The webGIS application is available via a user-friendly interface as well as allowing interaction with users by providing them the opportunity to comment on the results and/or exchange ideas with other users of various groups; therefore, the participatory process, a creative stage in MSP, is further supported. The usefulness of such tools in coastal and marine planning and the decision-making process are further discussed.