scholarly journals Can Machine Learning and PS-InSAR Reliably Stand in for Road Profilometric Surveys?

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3377
Author(s):  
Nicholas Fiorentini ◽  
Mehdi Maboudi ◽  
Pietro Leandri ◽  
Massimo Losa
Keyword(s):  
Machine Learning ◽  
Central Italy ◽  
Vertical Displacement ◽  
Machine Learning Algorithms ◽  
International Roughness Index ◽  
Endogenous Factors ◽  
The Road ◽  
Persistent Scatterer ◽  
Interferometric Sar

This paper proposes a methodology for correlating products derived by Synthetic Aperture Radar (SAR) measurements and laser profilometric road roughness surveys. The procedure stems from two previous studies, in which several Machine Learning Algorithms (MLAs) have been calibrated for predicting the average vertical displacement (in terms of mm/year) of road pavements as a result of exogenous phenomena occurrence, such as subsidence. Such algorithms are based on surveys performed with Persistent Scatterer Interferometric SAR (PS-InSAR) over an area of 964 km2 in the Tuscany Region, Central Italy. Starting from this basis, in this paper, we propose to integrate the information provided by these MLAs with 10 km of in situ profilometric measurements of the pavement surface roughness and relative calculation of the International Roughness Index (IRI). Accordingly, the aim is to appreciate whether and to what extent there is an association between displacements estimated by MLAs and IRI values. If a dependence exists, we may argue that road regularity is driven by exogenous phenomena and MLAs allow for the replacement of in situ surveys, saving considerable time and money. In this research framework, results reveal that there are several road sections that manifest a clear association among these two methods, while others denote that the relationship is weaker, and in situ activities cannot be bypassed to evaluate the real pavement conditions. We could wrap up that, in these stretches, the road regularity is driven by endogenous factors which MLAs did not integrate during their training. Once additional MLAs conditioned by endogenous factors have been developed (such as traffic flow, the structure of the pavement layers, and material characteristics), practitioners should be able to estimate the quality of pavement over extensive and complex road networks quickly, automatically, and with relatively low costs.

scholarly journals Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms

2020 ◽  
Vol 12 (22) ◽  
pp. 3776
Author(s):  
Andrea Tassi ◽  
Marco Vizzari
Keyword(s):  
Machine Learning ◽  
Central Italy ◽  
Object Oriented ◽  
Google Earth ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Landsat 8 ◽  
Good Reliability ◽  
Google Earth Engine ◽  
Occurrence Matrix

Google Earth Engine (GEE) is a versatile cloud platform in which pixel-based (PB) and object-oriented (OO) Land Use–Land Cover (LULC) classification approaches can be implemented, thanks to the availability of the many state-of-art functions comprising various Machine Learning (ML) algorithms. OO approaches, including both object segmentation and object textural analysis, are still not common in the GEE environment, probably due to the difficulties existing in concatenating the proper functions, and in tuning the various parameters to overcome the GEE computational limits. In this context, this work is aimed at developing and testing an OO classification approach combining the Simple Non-Iterative Clustering (SNIC) algorithm to identify spatial clusters, the Gray-Level Co-occurrence Matrix (GLCM) to calculate cluster textural indices, and two ML algorithms (Random Forest (RF) or Support Vector Machine (SVM)) to perform the final classification. A Principal Components Analysis (PCA) is applied to the main seven GLCM indices to synthesize in one band the textural information used for the OO classification. The proposed approach is implemented in a user-friendly, freely available GEE code useful to perform the OO classification, tuning various parameters (e.g., choose the input bands, select the classification algorithm, test various segmentation scales) and compare it with a PB approach. The accuracy of OO and PB classifications can be assessed both visually and through two confusion matrices that can be used to calculate the relevant statistics (producer’s, user’s, overall accuracy (OA)). The proposed methodology was broadly tested in a 154 km2 study area, located in the Lake Trasimeno area (central Italy), using Landsat 8 (L8), Sentinel 2 (S2), and PlanetScope (PS) data. The area was selected considering its complex LULC mosaic mainly composed of artificial surfaces, annual and permanent crops, small lakes, and wooded areas. In the study area, the various tests produced interesting results on the different datasets (OA: PB RF (L8 = 72.7%, S2 = 82%, PS = 74.2), PB SVM (L8 = 79.1%, S2 = 80.2%, PS = 74.8%), OO RF (L8 = 64%, S2 = 89.3%, PS = 77.9), OO SVM (L8 = 70.4, S2 = 86.9%, PS = 73.9)). The broad code application demonstrated very good reliability of the whole process, even though the OO classification process resulted, sometimes, too demanding on higher resolution data, considering the available computational GEE resources.

scholarly journals IoT Bus Navigation System with Optimized Routing using Machine Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 1-15
Author(s):  
Samer I. Mohamed ◽  
◽  
Muhamed Abdelhadi
Keyword(s):  
Machine Learning ◽  
Public Transportation ◽  
Operating Cost ◽  
Machine Learning Algorithms ◽  
Layered System ◽  
The Road ◽  
Chip Technology ◽  
On The Road ◽  
On Chip ◽  
Maintenance Schedules

As the population in Egypt is ever expanding, it is reflected in the increase of the number of vehicles on the road. Public transportation is the solution and the number of available buses can cover a significant amount of the population demand. However, the outdated state of the transportation infrastructure, the static nature of the lines and indistinct schedules create a confounding and unappealing user experience which prompts the users to stray to cars for their needs. So, an Intelligent Urban Transportation System (IUTS) is a must. IUTS is a multi-layered system which provides the solution for most of these problems. It operates on different layers starting from a real time vehicle tracking for transparent and efficient management of assets, cash-less ticketing done through RFID cards, vehicle health and diagnostic data for creation of automated maintenance schedules and a friendly interactive driver interface. In this paper an approach based on combining all these technologies is discussed where the hardware component is implemented based on System-on-Chip technology with custom hardware to interface with the vehicle. The data collected from the on-board unit is sent to the cloud, and with the help of machine learning algorithms the dynamic responsiveness of the system is guaranteed. The proposed system outperforms other existing ones through the dynamic and optimized routing feature for the bus navigation to optimize the operating cost but still satisfy the passengers’demand.

scholarly journals Surface Motion Prediction and Mapping for Road Infrastructures Management by PS-InSAR Measurements and Machine Learning Algorithms

2020 ◽  
Vol 12 (23) ◽  
pp. 3976
Author(s):  
Nicholas Fiorentini ◽  
Mehdi Maboudi ◽  
Pietro Leandri ◽  
Massimo Losa ◽  
Markus Gerke
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Central Italy ◽  
Machine Learning Algorithms ◽  
Boosted Regression Trees ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Surface Motion ◽  
Persistent Scatterers ◽  
Taylor Diagram

This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) measurements, geospatial analyses, and Machine Learning Algorithms (MLAs) are employed for achieving the purpose. Two single learners, i.e., Regression Tree (RT) and Support Vector Machine (SVM), and two ensemble learners, i.e., Boosted Regression Trees (BRT) and Random Forest (RF) are utilized for estimating the surface motion ratio in terms of mm/year over the Province of Pistoia (Tuscany Region, central Italy, 964 km2), in which strong subsidence phenomena have occurred. The interferometric process of 210 Sentinel-1 images from 2014 to 2019 allows exploiting the average displacements of 52,257 Persistent Scatterers as output targets to predict. A set of 29 environmental-related factors are preprocessed by SAGA-GIS, version 2.3.2, and ESRI ArcGIS, version 10.5, and employed as input features. Once the dataset has been prepared, three wrapper feature selection approaches (backward, forward, and bi-directional) are used for recognizing the set of most relevant features to be used in the modeling. A random splitting of the dataset in 70% and 30% is implemented to identify the training and test set. Through a Bayesian Optimization Algorithm (BOA) and a 10-Fold Cross-Validation (CV), the algorithms are trained and validated. Therefore, the Predictive Performance of MLAs is evaluated and compared by plotting the Taylor Diagram. Outcomes show that SVM and BRT are the most suitable algorithms; in the test phase, BRT has the highest Correlation Coefficient (0.96) and the lowest Root Mean Square Error (0.44 mm/year), while the SVM has the lowest difference between the standard deviation of its predictions (2.05 mm/year) and that of the reference samples (2.09 mm/year). Finally, algorithms are used for mapping surface motion over the study area. We propose three case studies on critical stretches of two-lane rural roads for evaluating the reliability of the procedure. Road authorities could consider the proposed methodology for their monitoring, management, and planning activities.

scholarly journals Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data

2020 ◽  
Vol 12 (18) ◽  
pp. 3104
Author(s):  
Gangqiang An ◽  
Minfeng Xing ◽  
Binbin He ◽  
Chunhua Liao ◽  
Xiaodong Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chlorophyll Content ◽  
Precision Agriculture ◽  
Rate Of Change ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Training Set ◽  
Validation Set

Chlorophyll is an essential pigment for photosynthesis in crops, and leaf chlorophyll content can be used as an indicator for crop growth status and help guide nitrogen fertilizer applications. Estimating crop chlorophyll content plays an important role in precision agriculture. In this study, a variable, rate of change in reflectance between wavelengths ‘a’ and ‘b’ (RCRWa-b), derived from in situ hyperspectral remote sensing data combined with four advanced machine learning techniques, Gaussian process regression (GPR), random forest regression (RFR), support vector regression (SVR), and gradient boosting regression tree (GBRT), were used to estimate the chlorophyll content (measured by a portable soil–plant analysis development meter) of rice. The performances of the four machine learning models were assessed and compared using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results revealed that four features of RCRWa-b, RCRW551.0–565.6, RCRW739.5–743.5, RCRW684.4–687.1 and RCRW667.9–672.0, were effective in estimating the chlorophyll content of rice, and the RFR model generated the highest prediction accuracy (training set: RMSE = 1.54, MAE =1.23 and R2 = 0.95; validation set: RMSE = 2.64, MAE = 1.99 and R2 = 0.80). The GPR model was found to have the strongest generalization (training set: RMSE = 2.83, MAE = 2.16 and R2 = 0.77; validation set: RMSE = 2.97, MAE = 2.30 and R2 = 0.76). We conclude that RCRWa-b is a useful variable to estimate chlorophyll content of rice, and RFR and GPR are powerful machine learning algorithms for estimating the chlorophyll content of rice.

scholarly journals A Novel Ensemble Machine Learning Approach for Bioarchaeological Sex Prediction

Technologies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 23
Author(s):  
Evan Muzzall
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
Central Italy ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Low Rank ◽  
Future Research ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Metric Distances

I present a novel machine learning approach to predict sex in the bioarchaeological record. Eighteen cranial interlandmark distances and five maxillary dental metric distances were recorded from n = 420 human skeletons from the necropolises at Alfedena (600–400 BCE) and Campovalano (750–200 BCE and 9–11th Centuries CE) in central Italy. A generalized low rank model (GLRM) was used to impute missing data and Area under the Curve—Receiver Operating Characteristic (AUC-ROC) with 20-fold stratified cross-validation was used to evaluate predictive performance of eight machine learning algorithms on different subsets of the data. Additional perspectives such as this one show strong potential for sex prediction in bioarchaeological and forensic anthropological contexts. Furthermore, GLRMs have the potential to handle missing data in ways previously unexplored in the discipline. Although results of this study look promising (highest AUC-ROC = 0.9722 for predicting binary male/female sex), the main limitation is that the sexes of the individuals included were not known but were estimated using standard macroscopic bioarchaeological methods. However, future research should apply this machine learning approach to known-sex reference samples in order to better understand its value, along with the more general contributions that machine learning can make to the reconstruction of past human lifeways.

scholarly journals Machine Learning to Classify Driving Events Using Mobile Phone Sensors Data

2021 ◽  
Vol 15 (02) ◽  
pp. 124
Author(s):  
Yazan Alqudah ◽  
Belal Sababha ◽  
Esam Qaralleh ◽  
Tarek Yousseff
Keyword(s):  
Machine Learning ◽  
Road Safety ◽  
Autonomous Vehicles ◽  
Machine Learning Algorithms ◽  
Motion Sensors ◽  
The Road ◽  
Crowd Sensing ◽  
Innovative Solution ◽  
On The Road ◽  
Self Driving Cars

With the ever-increasing vehicle population and introduction of autonomous and self-driving cars, innovative research is needed to ensure safety and reliability on the road. This work introduces an innovative solution that aims at understanding vehicle behavior based on sensors data. The behavior is classified according to driving events. Understanding driving events can play a significant role in road safety and estimating the expense and risks of driving and consuming a vehicle. Rather than relying on the distance and time driven, driving events can provide a more accurate measure of vehicle driving consumption.  This measure will become more valuable as more autonomous vehicles and more ride sharing applications are introduced to roads around the world. Estimating driving events can also help better design the road infrastructure to reduce energy consumption.  By sharing data from official vehicles and volunteers, crowd sensing can be used to better understand congestion and road safety. This work studies driving events and proposes using machine learning to classify these events into different categories. The acquired data is collected using embedded mobile device motion sensors and are used to train machine learning algorithms to classify the events.

scholarly journals Machine Learning Models for Spring Discharge Forecasting

Geofluids ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Francesco Granata ◽  
Michele Saroli ◽  
Giovanni de Marinis ◽  
Rudy Gargano
Keyword(s):  
Machine Learning ◽  
Central Italy ◽  
Regression Tree ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Spring Discharge ◽  
Learning Models ◽  
Promising Alternative ◽  
Cross Correlation Analysis ◽  
Machine Learning Models

Nowadays, drought phenomena increasingly affect large areas of the globe; therefore, the need for a careful and rational management of water resources is becoming more pressing. Considering that most of the world’s unfrozen freshwater reserves are stored in aquifers, the capability of prediction of spring discharges is a crucial issue. An approach based on water balance is often extremely complicated or ineffective. A promising alternative is represented by data-driven approaches. Recently, many hydraulic engineering problems have been addressed by means of advanced models derived from artificial intelligence studies. Three different machine learning algorithms were used for spring discharge forecasting in this comparative study: M5P regression tree, random forest, and support vector regression. The spring of Rasiglia Alzabove, Umbria, Central Italy, was selected as a case study. The machine learning models have proven to be able to provide very encouraging results. M5P provides good short-term predictions of monthly average flow rates (e.g., in predicting average discharge of the spring after 1 month, R2=0.991, RAE=14.97%, if a 4-month input is considered), while RF is able to provide accurate medium-term forecasts (e.g., in forecasting average discharge of the spring after 3 months, R2=0.964, RAE=43.12%, if a 4-month input is considered). As the time of forecasting advances, the models generally provide less accurate predictions. Moreover, the effectiveness of the models significantly depends on the duration of the period considered for input data. This duration should be close to the aquifer response time, approximately estimated by cross-correlation analysis.

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