Floods are among the natural disasters that cause financial and human losses all over the world every year. By production of a flood risk map and determination of potential flood risk areas, the ...possible damages of this phenomenon can be reduced. To map the flood extend in Calcasieu Parish, Louisiana, US, conditioning factors affecting the flood occurrence including elevation, slope, plan curvature, land use, distance from rivers, density of rivers, rainfall, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), and normalized difference built-up index (NDBI) were identified and their information layers produced using the Google Earth Engine (GEE) cloud platform. Then, for flood risk mapping, Random Forest (RF) and support vector machine (SVM) as two machine learning models have been implemented and their results compared. RF and SVM models have been validated based on the maximum absolute error (MAE) index with an accuracy of 0.043 and 0.097, respectively. Visualization of the predicted values in QGIS software confirms that the RF model has provided better outputs than that of the SVM model. By analysing the features importance of the layers in the RF model, it was verified that the elevation, slope, and plan curvature layers have the highest degree of influence on the flood risk with degrees of importance of 0.197, 0.135, and 0.123.
Principal objectives of the Intelligent Transportation Systems (ITS) are to improve traffic safety, facilitate informed traffic decision making, and enhance quality of life and services in a smart ...traffic environment. Vehicle crashes at urban traffic intersections are among the rudimentary sources of injuries and fatalities in the cities. According to the report of the World Health Organization (WHO), in every 25 seconds, one vulnerable road-user is being killed by a vehicle crash. Therefore, it is necessary to take a novel and smart approach for improving the safety and reducing vehicle crashes. This leads to a contextual perception and spatial awareness of driver to increase security and safety for the driver, vehicle, and road users. Autonomous vehicles collects the information from the environment through equipped sensors on the vehicle such as camera, laser, radar, and Global Navigation Satellite Systems (GNSS). The main challenge arises when the person or objects are located beyond the driver's Field of View (FOV) and cannot be detected by embedded sensors on the vehicles. This paper proposes an Advanced Driver Assistance System (ADAS) to increase the safety on road intersections by taking advantage of existing infrastructures (e.g road camera) being used for traffic control. The aim of this research is improving the driver's FOV using a computer vision approach (e.g background subtraction algorithm) and Location Based Service (LBS). The case study results at Tehran metropolitan demonstrate the reduction in traffic collision risk and improvement of pedestrian safety using the proposed system.
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Cadastral and urban map enrichment/upgrading is an essential requirement for smart urban management. The high pace of development and change in megacities can cause different challenges for urban ...organizations to reproduce their maps based on their need. New urban management aims and plans need new cadastral and urban maps with different standards and elements which may have existed in the other urban organization. Producing an original map or checking the maps of different organizations visually in a megacity is very costly and time-consuming. These challenges require an advanced integration approach to overcome them. Therefore, enriching maps with concerned organizations' maps and intelligent and automatically identifying as well as applying the changes in urban and cadastral maps will save time and cost for informed urban decision-making. This paper has employed the data of the third zone of the District six of the Municipality of Tehran, the capital of Iran, and identifies changes in the parcel’s geometry of the cadastre maps in comparison with the recently produced maps of the municipality of Tehran. After pre-processing the data, some spatial and attribute information are added to each feature, and the land parcels are enriched. By matching the algorithm and comparing the parcels geometry and attributes, suspicious parcels are identified by the logistic regression algorithm. The Accuracy and F1-Score of this model were 0.845 and 0.780, respectively. Finally, the suspicious parcels are checked and the parcels are located, deleted, merged, splitted and geometrically modified in the base map and the base map is enriched. This paper has successfully proposed a new framework for cadastral and urban map enrichment intelligently.
Floods have caused significant socio-economic damage and are extremely dangerous for human lives as well as infrastructures. The aim of this study is to use machine learning models including ...regularized random forest (RRF) and Naïve Bayes (NB) algorithms to predict flood susceptibility areas using 410 sample points (205 flood points and 205 non-flood points). Ten flood influencing factors including elevation, topographic wetness index, rainfall, normalized difference vegetation index, curvature, land use, distance to river, slope, lithology, and aspect have been used in the modelling process. For this purpose, 70% of the data was used for training and the rest employed for testing the models. Accuracy (ACC), sensitivity, specificity, negative predictive value (NPV), and the area under the curve (AUC) of the receiver operating characteristic (ROC) were used to validate and compare the performance of the models. The results showed that the RRF model on the testing dataset had the highest performance (AUC = 0.94, ACC = 90%, Sensitivity = 0.89, Specificity = 0.92, NPV = 0.89) compared to that of the NB model (AUC = 0.93, ACC = 89%, Sensitivity = 0.84, Specificity = 0.96, NPV = 0.81). The employed models can be used as an efficient tool for flood susceptibility mapping with the purpose of planning to reduce the damages.
Land subsidence (LS) is one of the most challenging natural disasters that has potential consequences such as damage to infrastructures and buildings, creating sinkholes, and leading to soil ...destruction. To mitigate the damages caused by LS, it is necessary to determine the LS-prone areas. In this paper, LS susceptibility was assessed for Kashan Plain in Iran using Random Forest (RF) and XGBoost machine learning algorithms. For the susceptibility analysis, twelve influential factors including elevation, slope, aspect, curvature, topographic wetness index (TWI), groundwater drawdown (GWD), normalized difference vegetation index (NDVI), distance to stream (DtS), distance to road (DtR), distance to fault (DtF), lithology, and land use were taken into account. 291 LS points were used in this study which was divided into two parts of 70% and 30% for training and testing the models, respectively. The prediction power of the models and their produced LS susceptibility maps (LSSMs) were validated using the Root Mean Square Error (RMSE), R-Squared (R2), and Mean Absolute Error (MAE) values. The results showed that the XGBoost had a higher R² equal to 0.9032 compared to that of the RF which was equal to 0.8355. XGBoost model had an RMSE equal to 0.3764 cm compared to that of the RF model which was equal to 0.4906 cm. MAE for the XGBoost model was 0.1217 cm and for the RF model was 0.3050 cm. Therefore, the achieved results proved that XGBoost had better performance in this research for predicting LS values based on the measured ones.
Estimating real estate prices helps to adapt informed policies to regulate the real estate market and assist sellers and buyers to have a fair business. This study aims to estimate the price of ...residential properties in District 5 of Tehran, Capital of Iran, and model its associated uncertainty. The study implements the Stacking technique to model uncertainties by integrating the outputs of basic models. Basic models must have a good performance for their combinations to have acceptable results. This study employs four statistical and machine learning models as basic models: Random Forest (RF), Ordinary Least Squares (OLS), Weighted K-Nearest Neighbour (WKNN), and Support Vector Regression (SVR) to estimate the price of residential properties. The results show that the integrated output is more accurate for the quadruple combination mode than for any of the binary and triple combinations of the basic models. Comparing the Stacking technique with the Voting technique, it is shown that the Mean Absolute Percentage Error (MAPE) reduces from 10.18% to 9.81%. Hence we conclude that our method performs better than the Voting technique.
Groundwater is one of the most important sources of regional water supply for humans. In recent years, several factors have contributed to a significant decline in groundwater levels (GWL) in certain ...regions. As a result of climate change, such as temperature increase, rainfall decrease, and changes in relative humidity, it is necessary to investigate and model the effects of these factors on GWL. Although a number of researches have been conducted on GWL modeling with machine learning (ML) and deep learning (DL) algorithms, only a limited number of studies have reported model uncertainty. In this paper, GWL modeling of some piezometric wells has been conducted by considering the effects of the meteorological parameters with Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. The models were trained on one piezometric well data and predictions were executed on six other wells. To perform an uncertainty assessment, the models were run 10 times and their means were calculated. Subsequently, their standard deviations were considered to evaluate the outcomes. In addition, the prediction power of the models was validated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and R-Squared (R2). Finally, for all the six wells that did not participate in the training phase, the prediction functions of the trained models were run 10 times and their accuracy was assessed. The results indicate that LSTM (R2=95.6895, RMSE=0.4744 m, NRMSE=0.0558, MAE=0.3383 m) had a better performance compared to that of GRU (R2=95.2433, RMSE=0.4984 m, NRMSE=0.0586, MAE=0.3658 m) on the GWL modeling.
Research on determination of spatial patterns in urban car accidents plays an important role in improving urban traffic safety. While traditional methods of spatial clustering of car accidents mostly ...rely on the two dimensional assumption, many spatial events defy this assumption. For instance, car accidents are constrained by the road network and rely on the one dimensional assumption of street network. The aim of this study is to detect and statistically prioritize the car accident-prone segments of an urban road network by a network-based point pattern analysis. The first step involves estimating the density of car accidents in the one dimensional space of the road network using the network kernel density estimation (NKDE) method with equal-split continuous and discontinuous kernel functions. In the second step, due to the lack of statistical prioritization of the accident-prone segments with NKDE method, the output of the NKDE method is integrated with network-constrained Getis-Ord Gi* statistics to measure and compare the accident-prone segments based on the statistical parameter of Z-Score. The integration of these two methods can improve identification of accident-prone segments which is effective in the enhancing of urban safety and sustainability. These methods were tested using the data of damage car accidents in Tehran District 3 during 2013–2017. We also performed the Network K-Function to display the significant clustering of damage car accident points in the network space at different scales. The results have demonstrated that the damage car accidents are significantly clustered.
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