Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.
Blockchain technology, as a distributed digital ledger technology that ensures traceability, security, and transparency is displaying potential for easing some comprehensive supply chain problems. Scholars have started analyzing systematically the potential benefits and effects of block-chain on numerous activities of an organization. This paper presents the barricades in the adoption of blockchain technology in supply chain management. The potential benefits of blockchain adoption such as quality, cost, speed, transparency, durability, and immutability are also discussed in this paper. We present the early literature discussing the use of blockchain in the field of the supply chain to enhance accountability and transparency. This study explains the several mechanisms by which supply chain managers can prepare their organizational structure to adopt the latest technology. It further highlights the mechanisms to achieve supply chain objectives. Part of this paper also discusses how blockchains, a potentially disruptive solution that is on its early evolution, can overcome several potential barricades. Future research directions are proposed which can further provide insights into overcoming barriers and adoption of blockchain technology in the field of supply chain management.
Artificial intelligence (AI)-based multispectral remote sensing has been the best supporting tool using limited resources to enhance the lithological mapping abilities with accuracy, supported by ground truthing through traditional mapping techniques. The availability of the dataset, choice of algorithm, cost, accuracy, computational time, data labeling, and terrain features are some crucial considerations that researchers continue to explore. In this research, support vector machine (SVM) and artificial neural network (ANN) were applied to the Sentinel-2 MSI dataset for classifying lithologies having subtle compositional differences in the Kohat Basin’s remote, inaccessible regions within Pakistan. First, we used principal component analysis (PCA), minimum noise fraction (MNF), and available maps for reliable data annotation for training SVM and (ANN) models for mapping ten classes (nine lithological units + water). The ANN and SVM results were compared with the previously conducted studies in the area and ground truth survey to evaluate their accuracy. SVM mapped ten classes with an overall accuracy (OA) of 95.78% and kappa coefficient of 0.95, compared to 95.73% and 0.95 by ANN classification. The SVM algorithm was more efficient concerning computational efficiency, accuracy, and ease due to available features within Google Earth Engine (GEE). Contrarily, ANN required time-consuming data transformation from GEE to Google Cloud before application in Google Colab.
Planning and scheduling problems are omnipresent, not only in logistics, but also in timetabling human resources, and underneath in software platforms. These problems can have static nature, i.e. there is no change in the problem information after schedule is generated. In contrast, dynamic problems constantly need to deal with unexpected events (e.g., traffic jams, accidents, new tasks).There are two common approaches to deal with these problems: static approaches, which generate a schedule in one shot; and dynamic approaches, which keep running like an ongoing concern. With the advent of research on smart cities, smart scheduling techniques are in focus. They address the dynamic problems using smart technologies for generating and executing schedules. Without proper means to compare, it is hard, if not impossible, to select the best approach for a given problem. Due to lack of comparison between the static and the dynamic approaches, currently industry relies almost solely on the static approaches. But moving towards smart cities, with every device being interconnected and real time events required to be handled, makes it necessary to adapt to dynamic approaches.In this paper, we argue the need for comparison of the static and the dynamic planning approaches. We have identified the key challenges for realizing such comparison and propose two techniques for making such comparisons. These techniques are applicable in a wide variety of applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.