Fast Algorithm for Modeling of Rain Events in Weather Radar Imagery

Weather radar imagery is important for several remote sensing applications including tracking of storm fronts and radar echo classification. In particular, tracking of precipitation events is useful for both forecasting and classification of rain/non-rain events since non-rain events usually appear to be static compared to rain events. Recent weather radar imaging-based forecasting approaches [3] consider that precipitation events can be modeled as a combination of localized functions using Radial Basis Function Neural Networks (RBFNNs). Tracking of rain events can be performed by tracking the parameters of these localized functions. The RBFNN-based techniques used in forecasting are not only computationally expensive, but also moderately effective in modeling small size precipitation events. In this thesis, an existing RBFNN technique [3] was implemented to verify its computational efficiency and forecasting effectiveness. The feasibility of modeling precipitation events using RBFNN effectively was evaluated, and several modifications to the existing technique have been proposed

Weather Radar image Based Forecasting using Joint Series Prediction

Accurate rainfall forecasting using weather radar imagery has always been a crucial and predominant task in the field of meteorology [1], [2], [3] and [4]. Competitive Radial Basis Function Neural Networks (CRBFNN) [5] is one of the methods used for weather radar image based forecasting. Recently, an alternative CRBFNN based approach [6] was introduced to model the precipitation events. The difference between the techniques presented in [5] and [6] is in the approach used to model the rainfall image. Overall, it was shown that the modified CRBFNN approach [6] is more computationally efficient compared to the CRBFNN approach [5]. However, both techniques [5] and [6] share the same prediction stage. In this thesis, a different GRBFNN approach is presented for forecasting Gaussian envelope parameters. The proposed method investigates the concept of parameter dependency among Gaussian envelopes. Experimental results are also presented to illustrate the advantage of parameters prediction over the independent series prediction

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

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed

Application of machine learning in operational flood forecasting and mapping

Considering the computational effort and expertise required to simulate 2D hydrodynamic models, it is widely understood that it is practically impossible to run these types of models during a real-time flood event. To allow for real-time flood forecasting and mapping, an automated, computationally efficient and robust data driven modelling engine - as an alternative to the traditional 2D hydraulic models - has been proposed. The concept of computationally efficient model relies heavily on replacing time consuming 2D hydrodynamic software packages with a simplified model structure that is fast, reliable and can robustly retains sufficient accuracy for applications in real-time flood forecasting, mapping and sequential updating. This thesis presents a novel data-driven modelling framework that uses rainfall data from meteorological stations to forecast flood inundation maps. The proposed framework takes advantage of the highly efficient machine learning (ML) algorithms and also utilities the state-of-the-art hydraulic models as a system component. The aim of this research has been to develop an integrated system, where a data-driven rainfall-streamflow forecasting model sets up the upstream boundary conditions for the machine learning based classifiers, which then maps out multi-step ahead flood extents during an extreme flood event. To achieve the aim and objectives of this research, firstly, a comprehensive investigation was undertaken to search for a robust ML-based multi-step ahead rainfall-streamflow forecasting model. Three potential models were tested (Support Vector Regression (SVR), Deep Belief Network (DBN) and Wavelet decomposed Artificial Neural Network (WANN)). The analysis revealed that SVR-based models perform most efficiently in forecasting streamflow for shorter lead time. This study also tested the portability of model parameters and performance deterioration rates. Secondly, multiple ML-based models (SVR, Random Forest (RF) and Multi-layer Perceptron (MLP)) were deployed to simulate flood inundation extents. These models were trained and tested for two geomorphologically distinct case study areas. In the first case of study, of the models trained using the outputs from LISFLOOD-FP hydraulic model and upstream flow data for a large rural catchment (Niger Inland Delta, Mali). For the second case of study similar approach was adopted, though 2D Flood Modeller software package was used to generate target data for the machine learning algorithms and to model inundation extent for a semi-urban floodplain (Upton-Upon-Severn, UK). In both cases, machine learning algorithms performed comparatively in simulating seasonal and event based fluvial flooding. Finally, a framework was developed to generate flood extent maps from rainfall data using the knowledge learned from the case studies. The research activity focused on the town of Upton-Upon-Severn and the analysis time frame covers the flooding event of October-November 2000. RF-based models were trained to forecast the upstream boundary conditions, which were systematically fed into MLP-based classifiers. The classifiers detected states (wet/dry) of the randomly selected locations within a floodplain at every time step (e.g. one hour in this study). The forecasted states of the sampled locations were then spatially interpolated using regression kriging method to produce high resolution probabilistic inundation (9m) maps. Results show that the proposed data centric modelling engine can efficiently emulate the outcomes of the hydraulic model with considerably high accuracy, measured in terms of flood arrival time error, and classification accuracy during flood growing, peak, and receding periods. The key feature of the proposed modelling framework is that, it can substantially reduce computational time, i.e. ~14 seconds for generating flood maps for a flood plain of ~4 km2 at 9m spatial resolution (which is significantly low compared to a fully 2D hydrodynamic model run time)

An Exploration of Neural Network Modelling Options for the Upper River Ping, Thailand

This thesis reports results from a systematic experimental approach to evaluating aspects of the neural network modelling process to forecast river stage for a large, 23,600 km2 catchment in northern Thailand. The research is prompted by the absence of evidenced recommendations as to which of the array of input processes, validations and modelling procedures might be selected by a neural network forecaster. The flood issue for forecasters at Chiang Mai derives from the monsoon rainfall, which leads to serious out-of-bank flooding two to four times a year. Data for stage and rainfall is limited as the instrumentation is sparse and the historical flood record is limited in length. Neural network forecasting models are potentially very powerful forecasters where the data are limited. The challenge of this catchment is to provide adequate forecasts from data for relatively few storm events using three stage gauges and one rain gauge. Previous studies have reported forecasts with lead times of up to 18 hours. Thus, one research driver is to extend this lead time to give more warning. Eight input determination methods were systematically evaluated through thousands of model runs. The most successful method was found to be correlation and stepwise regression although the pattern was not consistent across all model runs. Cloud radar imagery was available for a few storm events. Rainfall data from a network was not available so it was decided to explore the value of the raw cloud reflectivity data as a catchment-wide surrogate for rainfall, to enhance the data record and potentially improve the forecast. The limited number of events makes drawing conclusions difficult, but for one event the forecast lead time was extended to 24-30 hours. The modelling also indicates that for this catchment where the monsoon may come from the south west or the north east, the direction of storm travel is important, indicating that developing two neural network models may be more appropriate. Internal model training and parameterisation tests suggest that future models should use Bayesian Regularization, and average across 50 runs. The number of hidden nodes should be less than the number input variables although for more complex problems, this was not necessarily the case. Ranges of normalisation made little difference. However, the minimum and maximum values used for normalisation appear to more important. The strength of the conclusions to be drawn from this research was recognised from the start as being limited by the data, but the results suggest that neural networks are both helpful modelling processes and can provide valuable forecasts in catchments with extreme rainfall and limited hydrological data. The systematic investigation of the alternative input determination methods, algorithms and internal parameters has enabled guidance to be given on appropriate model structures

Crowdsourcing traffic data for travel time estimation

Travel time estimation is a fundamental measure used in routing and navigation applications, in particular in emerging intelligent transportation systems (ITS). For example, many users may prefer the fastest route to their destination and would rely on real-time predicted travel times. It also helps real-time traffic management and traffic light control. Accurate estimation of travel time requires collecting a lot of real-time data from road networks. This data can be collected using a wide variety of sources like inductive loop detectors, video cameras, radio frequency identification (RFID) transponders etc. But these systems include deployment of infrastructure which has some limitations and drawbacks. The main drawbacks in these modes are the high cost and the high probability of error caused by prevalence of equipment malfunctions and in the case of sensor based methods, the problem of spatial coverage.;As an alternative to traditional way of collecting data using expensive equipment, development of cellular & mobile technology allows for leveraging embedded GPS sensors in smartphones carried by millions of road users. Crowd-sourcing GPS data will allow building traffic monitoring systems that utilize this opportunity for the purpose of accurate and real-time prediction of traffic measures. However, the effectiveness of these systems have not yet been proven or shown in real applications. In this thesis, we study some of the current available data sets and identify the requirements for accurate prediction. In our work, we propose the design for a crowd-sourcing traffic application, including an android-based mobile client and a server architecture. We also develop map-matching method. More importantly, we present prediction methods using machine learning techniques such as support vector regression.;Machine learning provides an alternative to traditional statistical method such as using averaged historic data for estimation of travel time. Machine Learning techniques played a key role in estimation in the last two decades. They are proved by providing better accuracy in estimation and in classification. However, employing a machine learning technique in any application requires creative modeling of the system and its sensory data. In this thesis, we model the road network as a graph and train different models for different links on the road. Modeling a road network as graph with nodes and links enables the learner to capture patterns occurring on each segment of road, thereby providing better accuracy. To evaluate the prediction models, we use three sets of data out of which two sets are collected using mobile probing and one set is generated using VISSIM traffic simulator. The results show that crowdsourcing is only more accurate than traditional statistical methods if the input values for input data are very close to the actual values. In particular, when speed of vehicles on a link are concerned, we need to provide the machine learning model with data that is only few minutes old; using average speed of vehicles, for example from the past half hour, as is usually seen in many web based traffic information sources may not allow for better performance