Prediction of Groundwater Arsenic Contamination using Geographic Information System and Artificial Neural Network

Ground water arsenic contamination is a well known health and environmental problem in Bangladesh. Sources of this heavy metal are known to be geogenic, however, the processes of its release into groundwater are poorly understood phenomena. In quest of mitigation of the problem it is necessary to predict probable contamination before it causes any damage to human health. Hence our research has been carried out to find the factor relations of arsenic contamination and develop an arsenic contamination prediction model. Researchers have generally agreed that the elevated concentration of arsenic is affected by several factors such as soil reaction (pH), organic matter content, geology, iron content, etc. However, the variability of concentration within short lateral and vertical intervals, and the inter-relationships of variables among themselves, make the statistical analyses highly non-linear and difficult to converge with a meaningful relationship. Artificial Neural Networks (ANN) comes in handy for such a black box type problem. This research uses Back propagation Neural Networks (BPNN) to train and validate the data derived from Geographic Information System (GIS) spatial distribution grids. The neural network architecture with (6-20-1) pattern was able to predict the arsenic concentration with reasonable accuracy

A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities

Abstract The concept of Autonomous Taxis (ATs) has witnessed a remarkable surge in popularity in recent years, paving the way toward future smart cities. However, accurately forecasting passenger demand for ATs remains a significant challenge. Traditional approaches for passenger demand forecasting often rely on centralized data collection and analysis, which can raise privacy concerns and incur high communication costs. To address these challenges, We propose a collaborative model using Federated Learning (FL) for passenger demand forecasting in smart city transportation systems. Our proposed approach enables ATs in different regions of the smart city to collaboratively learn and improve their demand forecasting models through FL while preserving the privacy of passenger data. We use several backpropagation neural networks as local models for collaborating to train the global model without directly sharing their data. The local model shares only the model updates with a global model that aggregates them, which is then sent back to local models to improve them. Our collaborative approach reduces privacy concerns and communication costs by facilitating learning from each other’s data without direct data sharing. We evaluate our approach using a real-world dataset of over 4500 taxis in Bangkok, Thailand. By utilizing MATLAB2022b, the proposed approach is compared with popular baseline methods and existing research on taxi demand forecasting systems. Results demonstrate that our proposed approach outperforms in passenger demand forecasting, surpassing existing methods in terms of model accuracy, privacy preservation, and performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared ( $$R^2$$ R 2 ). Furthermore, our approach exhibits improved performance over time through the collaborative learning process as more data becomes available

Optimizing Shared E-Scooter Operations Under Demand Uncertainty: A Framework Integrating Machine Learning and Optimization Techniques

The emergence of dockless shared e-scooters as a new form of shared micromobility offers a viable solution to specific urban transportation problems, including the first-mile–last-mile issue, parking constraints, and environmental emissions. However, this sharing service faces several challenges in daily operation, particularly related to demand volatility, battery recharging, maintenance, and regulations, owing to their trip and physical characteristics. Therefore, this study proposed a new data-driven rebalancing framework for dockless shared e-scooters that incorporates demand and variance prediction, and Monte Carlo sampling to simulate the expected demand. Thus, demand uncertainty and the collection of low-battery and broken e-scooters were included in the rebalancing formulation to minimize user dissatisfaction and operating costs. Rebalancing optimization is an NP-hard problem; in this study, the small-size problem was solved using the integer linear programming (ILP) solver GNU Linear Programming Kit, and the large-size problem was solved using the proposed hybrid ant colony optimization–ILP algorithm (ACO–ILP). This framework was evaluated on a real-world dataset from Minneapolis, Minnesota, which demonstrated that the demand and variance prediction efficiently allocated the uncertainty while reducing the overall uncertainty, leading to shorter driving distances and lower rebalancing costs relative to baseline cases

Assessing Economic Impacts of Thailand’s Fiscal Reallocation between Biofuel Subsidy and Transportation Investment: Application of Recursive Dynamic General Equilibrium Model

This study examined the economy-wide effects of reallocating the biofuel subsidy to invest in transportation using a recursive dynamics computable general equilibrium model. The constructed model consists of 35 sectors, 42 commodities, and 3 institutions (household, government, and the rest of the world). Three scenarios were simulated. In the first scenario, the subsidy of gasohol and biodiesel is completely removed, whereas, in the second and third scenarios, half of the removed subsidy is reallocated to finance investment in road freight transportation and road public transportation. The simulation results show that reallocating the biofuel subsidy to invest in road public transportation can lead to the highest long-term economic growth and has the lowest impact on the consumer price index (CPI). All findings suggest that policymakers should maintain continuous investment in transportation and prioritize this investment policy over the fuel price distortion scheme

Letter from King Mongkut of Siam to Stephen Mattoon, January 1, 1857

Letter wishing the American missionaries a happy and hopeful new year

Suphāsit rō̜i bot : rơ̄ wayākānsataka.

Introduction by Prince Damrong: p. [2]-[6]Phim nai ngān plong sop Nāng Lek Suwannatēmī.In Thai and Sanskrit (in Thai script).Mode of access: Internet.From the William J. Gedney Collection.Seal of Hō̜phrasamut Wachirayān at head of title

Čhotmāihēt rư̄ang Sœ̄ Chēmsa Bruk khao mā khō̜ tham sanyā nai Ratchakān thī 3 mư̄a pī čhō̜ Phō̜.Sō̜. 2393.

Introduction by Prince Damrong: p. [1]-[19].With biography and portrait of the crematee.Phim nai ngān phrarātchathān phlœ̄ng sop Phračhaophīyāthœ̄ Krommalūang Chumphō̜nkhētʻudomsak na phramēn thō̜ng Sanam Lūang mơ̄a dơ̄an Thamwākhom Ph.S. 2466.In Thai.Mode of access: Internet.From the William J. Gedney Collection

R̄ưang Saiyōk pēn yāngrai /

Seal of Hō̜phrasamut Wachirayān at head of title.Phim nai ngān plong sop Nāng Naiyawičhān (Sangūan Ditsayabut).In Thai.Mode of access: Internet.From the William J. Gedney Collection