Flood risk assessment using multi-sensor remote sensing, geographic information system, 2D hydraulic and machine learning based models

University of Technology Sydney. Faculty of Engineering and Information Technology.Flooding events threaten the population, economy and environment worldwide. In recent years, several spatial methods have been developed to map flood susceptibility, hazard and risk for predicting and modelling flooding events. However, this research proposes multiple state-of-the-art approaches to assess, simulate and forecast flooding from recent satellite imagery. Firstly, a model was proposed to monitor changes in surface runoff and forecast future surface runoff on the basis of land use/land cover (LULC) and precipitation factors because the effects of precipitation and LULC dynamics have directly affected surface runoff and flooding events. Land transformation model (LTM) was used to detect the LULC changes. Moreover, an autoregressive integrated moving average (ARIMA) model was applied to analyse and forecast rainfall trends. The parameters of the ARIMA time series model were calibrated and fitted statistically to minimise prediction uncertainty through modern Taguchi method. Then, a GIS -based soil conservation service-curve number (SCS-CN) model was developed to simulate the maximum probable surface runoff. Results showed that deforestation and urbanisation have occurred upon a given time and have been predicted to increase. Furthermore, given negative changes in LULC, surface runoff increased and was forecasted to exceed gradually by 2020. In accordance with the implemented model calibration and accuracy assessment, the GIS-based SCS-CN combined with the LTM and ARIMA model is an efficient and accurate approach to detecting, monitoring and forecasting surface runoff. Secondly, a physical vulnerability assessment of flood was conducted by extracting detailed urban features from Worldview-3. Panchromatic sharpening in conjunction with atmospheric and topographic corrections was initially implemented to increase spatial resolution and reduce atmospheric distortion from satellite images. Dempster–Shafer (DS) fusion classifier was proposed in this part as a feature-based image analysis (FBIA) to extract urban complex objects. The DS-FBIA was investigated among two sites to examine the transferability of the proposed method. In addition, the DS-FBIA was compared with other common image analysis approaches (pixel- and object-based image analyses) to discover its accuracy and computational operating time. k-nearest neighbour, Bayes and support vector machine (SVM) classifiers were tested as pixel-based image analysis approaches, while decision tree classifier was examined as an object-based image analysis approach. The results showed improvements in detailed urban extraction obtained using the proposed FBIA with 92.2% overall accuracy and with high transferability from one site to another. Thirdly, an integrated model was developed for probability analysis of different types of flood using fully distributed GIS-based algorithms. These methods were applicable, particularly where annual monsoon rains trigger fluvial floods (FF) with pluvial flash flood (PFF) events occur simultaneously. A hydraulic 2D high-resolution sub-grid model of hydrologic engineering centre river analysis system was performed to simulate FF probability and hazard. Moreover, machine learning random forest (RF) method was used to model PFF probability and hazard. The RF was optimised by particle swarm optimisation (PSO) algorithm. Both models were verified and calibrated by cross validation and sensitivity analysis to create a coupled PFF– FF probability mapping. The results showed high accuracy in generating a coupled PFF–FF probability model that can discover the impact and contribution of each type to urban flood hazard. Furthermore, the results provided detailed flood information for urban managers to equip infrastructures, such as highways, roads and sewage network, actively. Fourthly, the risk of a flood can be assessed through different stages of flood probability, hazard and vulnerability. A total of 13 flood conditioning parameters were created to construct a geospatial database for flood probability estimation in two study areas. To estimate flood probability, five approaches, namely, logistic regression, frequency ratio (FR), SVM, analytical hierarchy process and combined FR–SVM, were adopted. Then, a flood risk map was generated by integrating flood hazard and vulnerability. The accuracy of flood probability indices indicated that the combined FR–SVM method achieved the highest accuracy among the other approaches. The reliability of the results obtained from this research was also verified in the field. The most effective parameters that would trigger flood occurrence were rainfall and flood inundation depth. In this research, transferable residency from one study area to another was verified through all the implemented methods. Therefore, the proposed approaches would be effectively and easily replicated in other regions with a similar climate condition, that condition that is, having a sufficient amount of flooding inventory events. Moreover, the results of the proposed approaches provided solid-detailed information that would be used for making favourable decisions to reduce and control future flood risks

Soil erosion prediction based on land cover dynamics at the Semenyih watershed in Malaysia using LTM and USLE models

This study attempts to identify and forecast future land cover (LC) by using the Land Transformation Model (LTM), which considers pixel changes in the past and makes predictions using influential spatial features. LTM applies the Artificial Neural Networks algorithm) in conducting the analysis. In line with these objectives, two satellite images (Spot 5 acquired in 2004 and 2010) were classified using the Maximum Likelihood method for the change detection analysis. Consequently, LC maps from 2004 to 2010 with six classes (forest, agriculture, oil palm cultivations, open area, urban, and water bodies) were generated from the test area. A prediction was made on the actual soil erosion and the soil erosion rate using the Universal Soil Loss Equation (USLE) combined with remote sensing and GIS in the Semenyih watershed for 2004 and 2010 and projected to 2016. Actual and potential soil erosion maps from 2004 to 2010 and projected to 2016 were eventually generated. The results of the LC change detections indicated that three major changes were predicted from 2004 to 2016 (a period of 12 years): (1) forest cover and open area significantly decreased at rates of almost 30 and 8 km2, respectively; (2) cultivated land and oil palm have shown an increment in sizes at rates of 25.02 and 5.77 km2, respectively; and, (3) settlement and Urbanization has intensified also by almost 5 km2. Soil erosion risk analysis results also showed that the Semenyih basin exhibited an average annual soil erosion between 143.35 ton ha−1 year−1 in 2004 and 151 in 2010, followed by the expected 162.24 ton ha−1 year−1. These results indicated that Semenyih is prone to water erosion by 2016. The wide range of erosion classes were estimated at a very low level (0–1 t/ha/year) and mainly located on steep lands and forest areas. This study has shown that using both LTM and USLE in combination with remote sensing and GIS is a suitable method for forecasting LC and accurately measuring the amount of soil losses in the future

Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. In this paper, an ensemble method, which demonstrated efficiency in GIS based flood modeling, was used to create flood probability indices for the Damansara River catchment in Malaysia. To estimate flood probability, the frequency ratio (FR) approach was combined with support vector machine (SVM) using a radial basis function kernel. Thirteen flood conditioning parameters, namely, altitude, aspect, slope, curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, distance from river, geology, soil, surface runoff, and land use/cover (LULC), were selected. Each class of conditioning factor was weighted using the FR approach and entered as input for SVM modeling to optimize all the parameters. The flood hazard map was produced by combining the flood probability map with flood-triggering factors such as; averaged daily rainfall and flood inundation depth. Subsequently, the hydraulic 2D high-resolution sub-grid model (HRS) was applied to estimate the flood inundation depth. Furthermore, vulnerability weights were assigned to each element at risk based on their importance. Finally flood risk map was generated. The results of this research demonstrated that the proposed approach would be effective for flood risk management in the study area along the expressway and could be easily replicated in other areas

Antibacterial Efficacy of Different Concentrations of Sodium Hypochlorite Gel and Solution on Enterococcus faecalis Biofilm

Introduction: This in vitro study compared the antibacterial efficacy of 2.5% sodium hypochlorite gel and 2.5% and 5.25% sodium hypochlorite solutions on Enterococcus faecalis (E. faecalis) biofilm. Methods and Materials: The root canals of 60 extracted human single-rooted teeth were contaminated with E. faecalis and incubated for 6 weeks. The samples were randomly assigned to three experimental groups and one control group (n=15). The study protocol in the experimental groups consisted of injection of 5 mL of each irrigant into the root canals. Samples were collected from the root canal walls and 1:10 serial dilutions were prepared and added to Muller Hinton Agar (MHA) plates and incubated at 37°C for 48 h. A classic colony counting technique was used for determining vital E. faecalis bacterial counts in MHA plates. The Kruskal-Wallis test was used for statistical analysis of the data. The level of significance was set at 0.05. Results: The antibacterial effect of the irrigants in all three experimental groups was significantly greater than the control group (P<0.05), with no significant difference between 2.5% and 5.25% NaOCl solutions (P>0.05). The effect of 2.5% and 5.25% NaOCl solutions were significantly superior to 2.5% NaOCl gel (P<0.05). Conclusion: Under the limitations of this study, 2.5% NaOCl gel was effective in reducing E. faecalis counts; however this effect was less than that of NaOCl solutions.Keywords: Antibacterial; Biofilm; Enterococcus Faecalis; Sodium Hypochlorit

Urban Planning Using a Geospatial Approach: A Case Study of Libya

Large scale developmental projects firstly require the selection of one or more cities to be developed. In Libya, the selection process is done by selected organizations, which is highly influenced by human judgement that can be inconsiderate of socioeconomic and environmental factors. In this study, we propose an automated selection process, which takes into consideration only the important factors for city (cities) selection. Specifically, a geospatial decision-making tool, free of human bias, is proposed based on the fuzzy overlay (FO) and technique for order performance by similarity to ideal solution (TOPSIS) techniques for development projects in Libya. In this work, a dataset of 17 evaluation criteria (GIS factors) across five urban conditioning factors were prepared. The dataset served as input to the FO model to calculate weights (importance) for each criterion. A support vector machine (SVM) classifier was then trained to refine weights from the FO model. TOPSIS was then applied on the refined results to rank the cities for development. Experimental results indicate promising overall accuracy and kappa statistics. Our findings also show that highest and lowest success rates are 0.94 and 0.79, respectively, while highest and lowest prediction rates are 0.884 and 0.673, respectively

Effect of Platelet-Rich Plasma on Differentiation of Osteoblasts and Osteoclasts in the Presence of Three-Dimensional Scaffold

Background: Osteoblasts’ activity is prerequisite for prevention from and treatment of apical periodontitis and a relatively high proportion of endodontically treated teeth will require retrograde treatment in future. Therefore, the aim of the present study was to evaluate the effect of platelet-rich plasma (PRP) on differentiation of stem cells into osteoblasts and osteoclasts. Methods: Mesenchymal stem cells were isolated from human fetal umbilical cord and cultured on two polycaprolacton/hydroxyapatite (PCL/HA) polymer scaffolds. In addition to differentiation agents, 10% PRP was added to PRP containing subgroups. After 10 days, osteoblast differentiation was assessed evaluating the osteocalcin and osterix gene levels where, in the osteoclast differentiation group the expression of tartarate-resistant acid phosphatase (TRAP) gene was evaluated. Results: Expression of TRAP gene did not reveal any significant differences between the study and control groups. There was a significant difference in osterix expression between the control and the PRP-treated groups (p < 0.01) as well as osteocalcin gene (p < 0.05). Conclusion: The results showed that PRP increased the osteoblastic differentiation, while it does not cause any significant increase in osteoclastic differentiation

Oil Palm Counting and Age Estimation from WorldView-3 Imagery and LiDAR Data Using an Integrated OBIA Height Model and Regression Analysis

The current study proposes a new method for oil palm age estimation and counting. A support vector machine algorithm (SVM) of object-based image analysis (OBIA) was implemented for oil palm counting. It was integrated with height model and multiregression methods to accurately estimate the age of trees based on their heights in five different plantation blocks. Multiregression and multi-kernel size models were examined over five different oil palm plantation blocks to achieve the most optimized model for age estimation. The sensitivity analysis was conducted on four SVM kernel types (sigmoid (SIG), linear (LN), radial basis function (RBF), and polynomial (PL)) with associated parameters (threshold values, gamma γ, and penalty factor (c)) to obtain the optimal OBIA classification approaches for each plantation block. Very high-resolution imageries of WorldView-3 (WV-3) and light detection and range (LiDAR) were used for oil palm detection and age assessment. The results of oil palm detection had an overall accuracy of 98.27%, 99.48%, 99.28%, 99.49%, and 97.49% for blocks A, B, C, D, and E, respectively. Moreover, the accuracy of age estimation analysis showed 90.1% for 3-year-old, 87.9% for 4-year-old, 88.0% for 6-year-old, 87.6% for 8-year-old, 79.1% for 9-year-old, and 76.8% for 22-year-old trees. Overall, the study revealed that remote sensing techniques can be useful to monitor and detect oil palm plantation for sustainable agricultural management

Optimized hierarchical rule-based classification for differentiating shallow and deep-seated landslide using high-resolution LiDAR data

Landslide is one of the most devastating natural disasters across the world with serious negative impact on its inhabitants and the environs. Landslide is considered as a type of soil erosion which could be shallow, deep-seated, cut slope, bare soil, and so on. Distinguishing between these types of soil erosions in dense vegetation terrain like Cameron Highlands Malaysia is still a challenging issue. Thus, it is difficult to differentiate between these erosion types using traditional techniques in locations with dense vegetation. Light detection and ranging (LiDAR) can detect variations in terrain and provide detailed topographic information on locations behind dense vegetation. This paper presents a hierarchical rule-based classification to obtain accurate map of landslide types. The performance of the hierarchical rule set classification using LiDAR data, orthophoto, texture, and geometric features for distinguishing between the classes would be evaluated. Fuzzy logic supervised approach (FbSP) was employed to optimize the segmentation parameters such as scale, shape, and compactness. Consequently, a correlation-based feature selection technique was used to select relevant features to develop the rule sets. In addition, in other to differentiate between deep-seated cover under shadow and normal shadow, the band ration was created by dividing the intensity over the green band. The overall accuracy and the kappa coefficient of the hierarchal rule set classification were found to be 90.41 and 0.86%, respectively, for site A. More so, the hierarchal rule sets were evaluated using another site named site B, and the overall accuracy and the kappa coefficient were found to be 87.33 and 0.81%, respectively. Based on these results, it is demonstrated that the proposed methodology is highly effective in improving the classification accuracy. The LiDAR DEM data, visible bands, texture, and geometric features considerably influence the accuracy of differentiating between landslide types such as shallow and deep-seated and soil erosion types like cut slope and bare soil. Therefore, this study revealed that the proposed method is efficient and well-organized for differentiating among landslide and other soil erosion types in tropical forested areas

Surface runoff estimation and prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA and distributed-GIS-based SCS-CN models at tropical region

The integration of precipitation intensity and LULC forecasting have played a significant role in prospect surface runoff, allowing for an extension of the lead time that enables a more timely implementation of the control measures. The current study proposes a full-package model to monitor the changes in surface runoff in addition to forecasting the future surface runoff based on LULC and precipitation factors. On one hand, six different LULC classes from Spot-5 satellite image were extracted by object-based Support Vector Machine (SVM) classifier. Conjointly, Land Transformation Model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020. On the other hand, ARIMA model was applied to the analysis and forecasting the rainfall trends. The parameters of ARIMA time series model were calibrated and fitted statistically to minimize the prediction uncertainty by latest Taguchi method. Rainfall and streamflow data recorded in eight nearby gauging stations were engaged to train, forecast, and calibrate the climate hydrological models. Then, distributed-GIS-based SCS-CN model was applied to simulate the maximum probable surface runoff for 2000, 2010, and 2020. The comparison results showed that first, deforestation and urbanization have occurred upon the given time and it is anticipated to increase as well. Second, the amount of rainfall has been nonstationary declined till 2015 and this trend is estimated to continue till 2020. Third, due to the damaging changes in LULC and climate, the surface runoff has also increased till 2010 and it is forecasted to gradually exceed