Predictive analytics applied to firefighter response, a practical approach

Time is a crucial factor for the outcome of emergencies, especially those that involve human lives. This paper looks at Lisbon’s firefighter’s occurrences and presents a model,based on city characteristics and climacteric data, to predict whether there will be an occurrence at a certain location, according to the weather forecasts. In this study three algorithms were considered, Logistic Regression, Decision Tree and Random Forest.Measured by the AUC, the best performant modelwasa random forestwith random under-sampling at 0.68. This model was well adjusted across the city and showed that precipitation and size of the subsection are themost relevant featuresin predicting firefighter’s occurrences.The work presented here has clear implications on the firefighter’s decision-makingregarding vehicle allocation, as now they can make an informed decision considering the predicted occurrences

Numerical simulation of flooding from multiple sources using adaptive anisotropic unstructured meshes and machine learning methods

Over the past few decades, urban floods have been gaining more attention due to their increase in frequency. To provide reliable flooding predictions in urban areas, various numerical models have been developed to perform high-resolution flood simulations. However, the use of high-resolution meshes across the whole computational domain causes a high computational burden. In this thesis, a 2D control-volume and finite-element (DCV-FEM) flood model using adaptive unstructured mesh technology has been developed. This adaptive unstructured mesh technique enables meshes to be adapted optimally in time and space in response to the evolving flow features, thus providing sufficient mesh resolution where and when it is required. It has the advantage of capturing the details of local flows and wetting and drying front while reducing the computational cost. Complex topographic features are represented accurately during the flooding process. This adaptive unstructured mesh technique can dynamically modify (both, coarsening and refining the mesh) and adapt the mesh to achieve a desired precision, thus better capturing transient and complex flow dynamics as the flow evolves. A flooding event that happened in 2002 in Glasgow, Scotland, United Kingdom has been simulated to demonstrate the capability of the adaptive unstructured mesh flooding model. The simulations have been performed using both fixed and adaptive unstructured meshes, and then results have been compared with those published 2D and 3D results. The presented method shows that the 2D adaptive mesh model provides accurate results while having a low computational cost. The above adaptive mesh flooding model (named as Floodity) has been further developed by introducing (1) an anisotropic dynamic mesh optimization technique (anisotropic-DMO); (2) multiple flooding sources (extreme rainfall and sea-level events); and (3) a unique combination of anisotropic-DMO and high-resolution Digital Terrain Model (DTM) data. It has been applied to a densely urbanized area within Greve, Denmark. Results from MIKE 21 FM are utilized to validate our model. To assess uncertainties in model predictions, sensitivity of flooding results to extreme sea levels, rainfall and mesh resolution has been undertaken. The use of anisotropic-DMO enables us to capture high resolution topographic features (buildings, rivers and streets) only where and when is needed, thus providing improved accurate flooding prediction while reducing the computational cost. It also allows us to better capture the evolving flow features (wetting-drying fronts). To provide real-time spatio-temporal flood predictions, an integrated long short-term memory (LSTM) and reduced order model (ROM) framework has been developed. This integrated LSTM-ROM has the capability of representing the spatio-temporal distribution of floods since it takes advantage of both ROM and LSTM. To reduce the dimensional size of large spatial datasets in LSTM, the proper orthogonal decomposition (POD) and singular value decomposition (SVD) approaches are introduced. The performance of the LSTM-ROM developed here has been evaluated using Okushiri tsunami as test cases. The results obtained from the LSTM-ROM have been compared with those from the full model (Fluidity). Promising results indicate that the use of LSTM-ROM can provide the flood prediction in seconds, enabling us to provide real-time flood prediction and inform the public in a timely manner, reducing injuries and fatalities. Additionally, data-driven optimal sensing for reconstruction (DOSR) and data assimilation (DA) have been further introduced to LSTM-ROM. This linkage between modelling and experimental data/observations allows us to minimize model errors and determine uncertainties, thus improving the accuracy of modelling. It should be noting that after we introduced the DA approach, the prediction errors are significantly reduced at time levels when an assimilation procedure is conducted, which illustrates the ability of DOSR-LSTM-DA to significantly improve the model performance. By using DOSR-LSTM-DA, the predictive horizon can be extended by 3 times of the initial horizon. More importantly, the online CPU cost of using DOSR-LSTM-DA is only 1/3 of the cost required by running the full model.Open Acces

Streaming Feature Grouping and Selection (Sfgs) For Big Data Classification

Real-time data has always been an essential element for organizations when the quickness of data delivery is critical to their businesses. Today, organizations understand the importance of real-time data analysis to maintain benefits from their generated data. Real-time data analysis is also known as real-time analytics, streaming analytics, real-time streaming analytics, and event processing. Stream processing is the key to getting results in real-time. It allows us to process the data stream in real-time as it arrives. The concept of streaming data means the data are generated dynamically, and the full stream is unknown or even infinite. This data becomes massive and diverse and forms what is known as a big data challenge. In machine learning, streaming feature selection has always been a preferred method in the preprocessing of streaming data. Recently, feature grouping, which can measure the hidden information between selected features, has begun gaining attention. This dissertation’s main contribution is in solving the issue of the extremely high dimensionality of streaming big data by delivering a streaming feature grouping and selection algorithm. Also, the literature review presents a comprehensive review of the current streaming feature selection approaches and highlights the state-of-the-art algorithms trending in this area. The proposed algorithm is designed with the idea of grouping together similar features to reduce redundancy and handle the stream of features in an online fashion. This algorithm has been implemented and evaluated using benchmark datasets against state-of-the-art streaming feature selection algorithms and feature grouping techniques. The results showed better performance regarding prediction accuracy than with state-of-the-art algorithms

Optimization of firefighter response with predictive analytics : practical application to Lisbon, Portugal

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceTime is a crucial factor for the outcome of emergencies, especially those that involve human lives. This paper looks at Lisbon’s firefighter’s occurrences and presents a model, based on city characteristics and climacteric data, to predict whether there will be an occurrence at a certain location, according to the weather forecasts. In this study three algorithms were considered, Logistic Regression, Decision Tree and Random Forest, as well as four techniques to balance the data – random over-sampling, SMOTE, random under-sampling and Near Miss –, which were compared to the baseline, the imbalanced data. Measured by the AUC, the best performant model was a random forest with random under-sampling at 0.68. This model was well adjusted across the city and showed that precipitation and size of the subsection are the most relevant features in predicting firefighter’s occurrences. The work presented here has clear implications on the firefighter’s decision-making regarding vehicle allocation, as now they can make an informed decision considering the predicted occurrences

Hybrid approach on multi- spatiotemporal data framework towards analysis of long-lead upstream flood: a case of Niger State, Nigeria

Floods have become a global concern because of the vast economic and ecological havoc that ensue. Thus, a flood risk mitigation strategy is used to reduce flood-related consequences by a long-lead identification of its occurrence. A wide range of causative factors, including the adoption of hybrid multi-spatiotemporal data framework is considered in implementing the strategy. Besides the structural or homogenous non-structural factors, the adoption of various Information Systems-based tools are also required to accurately analyse the multiple natural causative factors. Essentially, this was needed to address the inaccurate flood vulnerability classifications and short time of flood prediction. Thus, this study proposes a framework named: Hybrid Multi-spatiotemporal data Framework for Long-lead Upstream Flood Analysis (HyM-SLUFA) to provide a new dimension on flood vulnerability studies by uncovering the influence of multiple factors derived from topography, hydrology, vegetal and precipitation features towards regional flood vulnerability classification and long-lead analysis. In developing the proposed framework, the spatial images were geometrically and radiometrically corrected with the aid of Quantum Geographic Information System (QGIS). The temporal data were cleaned by means of winsorization methods using STATA statistical tool. The hybrid segment of the framework classifies flood vulnerability and performs long-lead analysis. The classification and analysis were conducted using the corrected spatial images to acquire better understanding on the interaction between the extracted features and rainfall in inducing flood as well as producing various regional flood vulnerabilities within the study area. Additionally, with the aid of regression technique, precipitation and water level data were used to perform long-lead flood analysis to provide a foresight of any potential flooding event in order to take proactive measures. As to confirm the reliability and validity of the proposed framework, an accuracy assessment was conducted on the outputs of the data. This study found the influence of various Flood Causative Factors (FCFs) used in the developed HyM-SLUFA framework, by revealing the spatial disparity indicating that the slope of a region shows a more accurate level of flood vulnerability compared to other FCFs, which generally causes severe upstream floods when there is low volume of precipitation within regions of low slope degree. Theoretically, the HyM-SLUFA will serve as a guide that can be adopted or adapted for similar studies. Especially, by considering the trend of precipitation and the pattern of flood vulnerability classifications depicted by various FCFs. These classifications will determine the kind(s) of policies that will be implemented in town planning, and the Flood Inducible Precipitation Volumes can provide a foresight of any potential flooding event in order to take practical proactive measures by the local authority

Global Ensemble Streamflow and Flood Modeling with Application of Large Data Analytics, Deep learning and GIS

ABSTRACTFlooding is one of the most dangerous natural disasters that repeatedly occur globally, and flooding frequently leads to major urban, financial, anthropogenic, and environmental impacts in the subjected area. Therefore, developing flood susceptibility maps to identify flood zones in the catchment is necessary for improved flood management and decision making. Streamflow and flood forecasting can provide important information for various applications including optimization of water resource allocations, water quality assessment, cost analysis, sustainable design of hydrological infrastructures, improvement in agriculture and irrigation practices. Compared to conventional or physically based hydrological modeling, which needs a large amount of historical data and parameters, the recent data-driven models which require limited amounts of data, have received growing attention among researchers due to their high predictive performance. This makes them more appropriate for hydrological forecasting in basin-scale and data-scarce regions. In this context, the main objective of this study was to evaluate the performance of various data-driven modeling approaches in flood and streamflow forecasting. One of the significant desires in daily streamflow prediction in today’s world is recognizing possible indicators and improving their applicability for effective water management strategies. In this context, the authors proposed an ensemble data mining algorithm coupled with various machine learning methods to perform data cleaning, dimensionality reduction, and feature subset selection. To perform the task of data mining, three data cleaning approaches: Principle Component Analysis (PCA), Tensor Flow (TF) and Tensor Flow K-means clustering(TF-k-means clustering) have been used. For the feature selection, four different machine learning approaches including K Nearest Neighbor (KNN), Bootstrap aggregating, Random Forest (RF) and Support Vector Machin (SVM) have been investigated. Out of twelve different combinations of data mining and machine learning, the best ensemble model was TF-k-means clustering coupled with RF, which outperformed the other methods with 96.52% classification accuracy. Thereafter, a modified Nonlinear Echo State Networks Multivariate Polynomial (NESN-MP) named in the current study as Robust Nonlinear Echo State Network (RNESN) was utilized for daily streamflow forecasting. The RNESN decreases the size of the reservoir (hidden layer which performs random weigh initialization), reduces the computational burden compared with NESN-MP, and increases the interactions between the internal states. The model is thus simple and user-friendly with better learning ability and more accurate forecasting performance. The method has been tested with data provided by the United States Geological Survey (USGS), Natural Resource Conservation Service (NRCS), National Weather Service Climate Prediction Center (NOAA) and Daymet Data Set from NASA through the Earth Science Data and Information System (ESDIS). Each data set includes the daily records of the local observed hydrological and large-scale weather/climate variability parameters. The efficiency of the proposed method has been evaluated in three regions namely Berkshire County (MA), Tuolumne County (CA), and Wasco County (OR). These basins were designated based upon the wide range of climatic conditions across the US that they represent. The simulation results were compared with NESN-MP and Adaptive Neuro-Fuzzy Inference System (ANFIS). The results validate the superiority of the proposed modeling approach compared to NESN-MP and ANFIS. The proposed RNESN approaches outperform the other methods with an RMSE = 0.98. For flood forecasting, an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, has been proposed to prepare the flood susceptibility map. In in this study, we proposed a new ensemble of models of Bootstrap aggregating as a Meta classifier based upon the K-Nearest Neighbor (KNN) functions including coarse, cosine, cubic and weighted as base classifiers to perform spatial prediction of the flood. We first selected 10 conditioning factors to spatial prediction of floods and then their prediction capability using the relief-F attribute evaluation (RFAE) method was assessed. Model validation was performed using two statistical error-indexes and the area under the curve (AUC). Results concluded that the Bootstrap aggregating -cubic KNN ensemble model outperformed the other ensemble models. Therefore, the Bootstrap aggregating -cubic KNN model can be used as a promising technique for the sustainable management of flood-prone areas. Furthermore, the AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%. The results showed that the EBF model had the highest accuracy in predicting the flood susceptibility map, in which 14% of the total areas were located in high and very high susceptibility classes and 62% were located in low and very low susceptibility classes

Prediction of Flood Severity Level Via Processing IoT Sensor Data Using Data Science Approach

The ‘riverine flooding’ is deemed a catastrophic phenomenon caused by extreme climate changes and other ecological factors (e.g., amount of sunlight), which are difficult to predict and monitor. However, the use of internet of things (IoT), various types of sensing including social sensing, 5G wireless communication and big data analysis have devised advanced tools for early prediction and management of distrust events. To this end, this paper amalgamates machine learning models and data analytics approaches along-with IoT sensor data to investigate attribute importance for the prediction of risk levels in flood. The paper presents three river levels: normal, medium and high-risk river levels for machine learning models. Performance is evaluated with varying configurations and evaluations setup including training and testing of support vector machine and random forest using principal components analysis-based dimension reduced dataset. In addition, we investigated the use of synthetic minority over-sampling technique to balance the class representations within dataset. As expected, the results indicated that a “balanced” representation of data samples achieved high accuracy (nearly 93%) when benchmarked with “imbalanced” data samples using random forest classifier 10-folds cross-validation

Machine Learning-Based Models for Assessing Impacts Before, During and After Hurricane Events

Social media provides an abundant amount of real-time information that can be used before, during, and after extreme weather events. Government officials, emergency managers, and other decision makers can use social media data for decision-making, preparation, and assistance. Machine learning-based models can be used to analyze data collected from social media. Social media data and cloud cover temperature as physical sensor data was analyzed in this study using machine learning techniques. Data was collected from Twitter regarding Hurricane Florence from September 11, 2018 through September 20, 2018 and Hurricane Michael from October 1, 2018 through October 18, 2018. Natural language processing models were developed to demonstrate sentiment among the data. Forecasting models for future events were developed for better emergency management during extreme weather events. Relationships among data were explored using social media data and physical sensor data to analyze extreme weather events as these events become more prevalent in our lives. In this study, social media sentiment analysis was performed that can be used by emergency managers, government officials, and decision makers. Different machine learning algorithms and natural language processing techniques were used to examine sentiment classification. The approach is multi-modal, which will help stakeholders develop a more comprehensive understanding of the social impacts of a storm and how to help prepare for future storms. Of all the classification algorithms used in this study to analyze sentiment, the naive Bayes classifier displayed the highest accuracy for this data. The results demonstrate that machine learning and natural language processing techniques, using Twitter data, are a practical method for sentiment analysis. The data can be used for correlation analysis between social sentiment and physical data and can be used by decision makers for better emergency management decisions