A novel hybrid swarm optimized multilayer neural network for spatial prediction of flash floods in tropical areas using sentinel-1 SAR imagery and geospatial data

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Novel Approaches in Landslide Monitoring and Data Analysis

Significant progress has been made in the last few years that has expanded the knowledge of landslide processes. It is, therefore, necessary to summarize, share and disseminate the latest knowledge and expertise. This Special Issue brings together novel research focused on landslide monitoring, modelling and data analysis

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Multisource Remote Sensing based Impervious Surface Mapping

Impervious surface (IS) not only serves as a key indicator of urbanization, but also affects the micro-ecosystem. Therefore, it is essential to monitor IS distribution timely and accurately. Remote sensing is an effective approach as it can provide straightforward and consistent information over large area with low cost. This thesis integrates multi-source remote sensing data to interpretate urban patterns and provide more reliable IS mapping results. Registration of optical daytime and nighttime lights (NTL) data is developed in the first contribution. An impervious surface based optical-to-NTL image registration algorithm with iterative blooming effect reduction (IS_iBER) algorithm is proposed. This coarse-to-fine procedure investigates the correlation between optical and NTL features. The iterative registration and blooming effect reduction method obtains precise matching results and reduce the spatial extension of NTL. Considering the spatial transitional nature of urban-rural fringes (URF) areas, the second study proposed approach for URF delineation, namely optical and nighttime lights (NTL) data based multi-scale URF (msON_URF).The landscape heterogeneity and development vitality derived from optical and NTL features are analyzed at a series of scales to illustrate the urban-URF-rural pattern. Results illustrate that msON_URF is effective and practical for not only concentric, but also polycentric urban patterns. The third study proposes a nighttime light adjusted impervious surface index (NAISI) to detect IS area. Parallel to baseline subtraction approaches, NAISI takes advantage of features, rather than spectral band information to map IS. NAISI makes the most of independence between NTL-ISS and pervious surface to address the high spectral similarity between IS and bare soil in optical image. An optical and NTL based spectral mixture analysis (ON_SMA) is proposed to achieve sub-pixel IS mapping result in the fourth study. It integrates characteristics of optical and NTL imagery to adaptively select local endmembers. Results illustrate the proposed method yields effective improvement and highlight the potential of NTL data in IS mapping. In the fifth study, GA-SVM IS mapping algorithm is investigated with introduction of the achieved urban-URF-rural spatial structure. The combination of optical, NTL and SAR imagery is discussed. GA is implemented for feature selection and parameter optimization in each urban scenario

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

A geometrical framework for forecasting cost uncertainty in innovative high value manufacturing.

Increasing competition and regulation are raising the pressure on manufacturing organisations to innovate their products. Innovation is fraught by significant uncertainty of whole product life cycle costs and this can lead to hesitance in investing which may result in a loss of competitive advantage. Innovative products exist when the minimum information for creating accurate cost models through contemporary forecasting methods does not exist. The scientific research challenge is that there are no forecasting methods available where cost data from only one time period suffices for their application. The aim of this research study was to develop a framework for forecasting cost uncertainty using cost data from only one time period. The developed framework consists of components that prepare minimum information for conversion into a future uncertainty range, forecast a future uncertainty range, and propagate the uncertainty range over time. The uncertainty range is represented as a vector space representing the state space of actual cost variance for 3 to n reasons, the dimensionality of that space is reduced through vector addition and a series of basic operators is applied to the aggregated vector in order to create a future state space of probable cost variance. The framework was validated through three case studies drawn from the United States Department of Defense. The novelty of the framework is found in the use of geometry to increase the amount of insights drawn from the cost data from only one time period and the propagation of cost uncertainty based on the geometric shape of uncertainty ranges. In order to demonstrate its benefits to industry, the framework was implemented at an aerospace manufacturing company for identifying potentially inaccurate cost estimates in early stages of the whole product life cycle