Explaining a century of Swiss regional development by deep learning and SHAP values

We use a graph convolutional neural network (GCN) for regional development prediction with population, railway network density, and road network density of each municipality as development indicators. By structuring the long-term time series data from 2833 municipalities in Switzerland during the years 1910–2000 as graphs over time, the GCN model interprets the indicators as node features and produces an acceptable prediction accuracy on their future values. Moreover, SHapley Additive exPlanations (SHAPs) are used to make the results of this approach explainable. We develop an algorithm to obtain SHAP values for the GCN and a sensitivity indicator to quantify the marginal contributions of the node features. This explainable GCN with SHAP decomposes the indicator into the contribution by the previous status of the municipality itself and the influence from other municipalities. We show that this provides valuable insights into understanding the history of regional development. Specifically, the results demonstrate that the impacts of geographical and economic constraints and urban sprawl on regional development vary significantly between municipalities and that the constraints are more important in the early 20th century. The model is able to include more information and can be applied to other regions and countries

Regional development prediction by deep learning using time series data from Switzerland

Prediction of complex regional development processes is significant for planners in order to make better decisions on providing infrastructures and services efficiently. Most of the existing prediction models are restricted to a local scale using detailed information or do not focus on long term development. This research therefore proposes a new methodology using a graph convolutional neural network (GCN) for regional development prediction, considering regional development as graph structures. Using long-term time series data from Switzerland during the years 1910 to 2000, it is demonstrated that the model shows better performance than time series analysis models. We apply Shapley Additive Explanations (SHAP) in order to gain insights into the influential factors in different municipalities and periods, and the interpretability of these SHAP values is demonstrated. The model is able to include more information and can be applied to other regions

Effect of Milling Processing Parameters on the Surface Roughness and Tool Cutting Forces of T2 Pure Copper

In this paper, the responses of machined surface roughness and milling tool cutting forces under the different milling processing parameters (cutting speed v, feed rate f, and axial cutting depth ap) are experimentally investigated to meet the increasing requirements for the mechanical machining of T2 pure copper. The effects of different milling processing parameters on cutting force and tool displacement acceleration are studied based on orthogonal and single-factor milling experiments. The three-dimensional morphologies of the workpieces are observed, and a white-light topography instrument measures the surface roughness. The results show that the degree of influence on Sa (surface arithmetic mean deviation) and Sq (surface root mean square deviation) from high to low level is the v, the f, and the ap. When v = 600 m/min, ap = 0.5 mm, f = 0.1 mm/r, Sa and Sq are 1.80 μm and 2.25 μm, respectively. The cutting forces in the three directions negatively correlate with increased cutting speed; when v = 600 m/min, Fx reaches its lowest value. In contrast, an increase in the feed rate and the axial cutting depth significantly increases Fx. The tool displacement acceleration amplitudes demonstrate a positive relationship. Variation of the tool displacement acceleration states leads to the different microstructure of the machined surfaces. Therefore, selecting the appropriate milling processing parameters has a positive effect on reducing the tool displacement acceleration, improving the machined surface quality of T2 pure copper, and extending the tool’s life. The optimal milling processing parameters in this paper are the v = 600 m/min, ap = 0.5 mm, and f = 0.1 mm/r

Rotating Bending Fatigue Behaviors of C17200 Beryllium Copper Alloy at High Temperatures

The purpose of this paper is to investigate the fatigue properties of C17200 alloy under the condition of quenching aging heat treatment at high temperatures, and to provide a design reference for its application in a certain temperature range. For this purpose, the tensile and rotary bending fatigue (RBF) tests were carried out at different temperatures (25 °C, 150 °C, 350 °C, and 450 °C). The tensile strength was obtained, and relationships between the applied bending stress levels and the number of fatigue fracture cycles were fitted to the stress-life (S-N) curves, and the related equations were determined. The fractured surfaces were observed and analyzed by a scanning electron microscopy (SEM). The results show that the RBF fatigue performance of C17200 alloy specimens is decreased with the increase in test temperature. When the temperature is below 350 °C, the performance degradation amplitudes of mechanical properties and RBF fatigue resistance are at a low level. However, compared to the RBF fatigue strength of 1 × 107 cycles at 25 °C, it is decreased by 38.4% when the temperature reaches 450 °C. It is found that the fatigue failure type of C17200 alloy belongs to surface defect initiation. Below 350 °C, the surface roughness of the fatigue fracture is higher, which is similar to the brittle fracture, so the boundary of the fracture regions is not obvious. At 450 °C, due to the further increase in temperature, oxidation occurs on the fracture surface, and the boundary of typical fatigue zone is obvious

Explaining a century of Swiss regional development by deep learning and SHAP values

We use a graph convolutional neural network (GCN) for regional development prediction with population, railway network density, and road network density of each municipality as development indicators. By structuring the long-term time series data from 2833 municipalities in Switzerland during the years 1910–2000 as graphs over time, the GCN model interprets the indicators as node features and produces an acceptable prediction accuracy on their future values. Moreover, SHapley Additive exPlanations (SHAPs) are used to make the results of this approach explainable. We develop an algorithm to obtain SHAP values for the GCN and a sensitivity indicator to quantify the marginal contributions of the node features. This explainable GCN with SHAP decomposes the indicator into the contribution by the previous status of the municipality itself and the influence from other municipalities. We show that this provides valuable insights into understanding the history of regional development. Specifically, the results demonstrate that the impacts of geographical and economic constraints and urban sprawl on regional development vary significantly between municipalities and that the constraints are more important in the early 20th century. The model is able to include more information and can be applied to other regions and countries.ISSN:2399-8083ISSN:2399-809

Explaining a century of Swiss regional development by deep learning and SHAP values

We use a graph convolutional neural network (GCN) for regional development prediction with population, railway network density, and road network density of each municipality as development indicators. By structuring the long-term time series data from 2833 municipalities in Switzerland during the years 1910–2000 as graphs over time, the GCN model interprets the indicators as node features and produces an acceptable prediction accuracy on their future values. Moreover, SHapley Additive exPlanations (SHAPs) are used to make the results of this approach explainable. We develop an algorithm to obtain SHAP values for the GCN and a sensitivity indicator to quantify the marginal contributions of the node features. This explainable GCN with SHAP decomposes the indicator into the contribution by the previous status of the municipality itself and the influence from other municipalities. We show that this provides valuable insights into understanding the history of regional development. Specifically, the results demonstrate that the impacts of geographical and economic constraints and urban sprawl on regional development vary significantly between municipalities and that the constraints are more important in the early 20th century. The model is able to include more information and can be applied to other regions and countries