An artificial neural network approach for soil moisture retrieval using passive microwave data

Soil moisture is a key variable that defines land surface-atmosphere (boundary layer) interactions, by contributing directly to the surface energy and water balance. Soil moisture values derived from remote sensing platforms only accounts for the near surface soil layers, generally the top 5cm. Passive microwave data at L-band (1.4 GHz, 21cm wavelength) measurements are shown to be a very effective observation for surface soil moisture retrieval. The first space-borne L-band mission dedicated to observing soil moisture, the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, was launched on 2nd November 2009.Artificial Neural Network (ANN) methods have been used to empirically ascertain the complex statistical relationship between soil moisture and brightness temperature in the presence of vegetation cover. The current problem faced by this method is its inability to predict soil moisture values that are 'out-of-range' of the training data.In this research, an optimization model is developed for the Backpropagation Neural Network model. This optimization model utilizes the combination of the mean and standard deviation of the soil moisture values, together with the prediction process at different pre-determined, equal size regions to cope with the spatial and temporal variation of soil moisture values. This optimized model coupled with an ANN of optimum architecture, in terms of inputs and the number of neurons in the hidden layers, is developed to predict scale-to-scale and downscaling of soil moisture values. The dependency on the accuracy of the mean and standard deviation values of soil moisture data is also studied in this research by simulating the soil moisture values using a multiple regression model. This model obtains very encouraging results for these research problems.The data used to develop and evaluate the model in this research has been obtained from the National Airborne Field Experiments in 2005

Machine Learning Approaches for Traffic Flow Forecasting

Intelligent Transport Systems (ITS) as a field has emerged quite rapidly in the recent years. A competitive solution coupled with big data gathered for ITS applications needs the latest AI to drive the ITS for the smart and effective public transport planning and management. Although there is a strong need for ITS applications like Advanced Route Planning (ARP) and Traffic Control Systems (TCS) to take the charge and require the minimum of possible human interventions. This thesis develops the models that can predict the traffic link flows on a junction level such as road traffic flows for a freeway or highway road for all traffic conditions. The research first reviews the state-of-the-art time series data prediction techniques with a deep focus in the field of transport Engineering along with the existing statistical and machine leaning methods and their applications for the freeway traffic flow prediction. This review setup a firm work focussed on the view point to look for the superiority in term of prediction performance of individual statistical or machine learning models over another. A detailed theoretical attention has been given, to learn the structure and working of individual chosen prediction models, in relation to the traffic flow data. In modelling the traffic flows from the real-world Highway England (HE) gathered dataset, a traffic flow objective function for highway road prediction models is proposed in a 3-stage framework including the topological breakdown of traffic network into virtual patches, further into nodes and to the basic links flow profiles behaviour estimations. The proposed objective function is tested with ten different prediction models including the statistical, shallow and deep learning constructed hybrid models for bi-directional links flow prediction methods. The effectiveness of the proposed objective function greatly enhances the accuracy of traffic flow prediction, regardless of the machine learning model used. The proposed prediction objective function base framework gives a new approach to model the traffic network to better understand the unknown traffic flow waves and the resulting congestions caused on a junction level. In addition, the results of applied Machine Learning models indicate that RNN variant LSTMs based models in conjunction with neural networks and Deep CNNs, when applied through the proposed objective function, outperforms other chosen machine learning methods for link flow predictions. The experimentation based practical findings reveal that to arrive at an efficient, robust, offline and accurate prediction model apart from feeding the ML mode with the correct representation of the network data, attention should be paid to the deep learning model structure, data pre-processing (i.e. normalisation) and the error matrices used for data behavioural learning. The proposed framework, in future can be utilised to address one of the main aims of the smart transport systems i.e. to reduce the error rates in network wide congestion predictions and the inflicted general traffic travel time delays in real-time

Multisensor Fusion Remote Sensing Technology For Assessing Multitemporal Responses In Ecohydrological Systems

Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing-based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction

Integrating Precipitation Nowcasting in a Deep Learning-Based Flash Flood Prediction Framework and Assessing the Impact of Rainfall Forecasts Uncertainties

Flash floods are among the most immediate and destructive natural hazards. To issue warnings on time, various attempts were made to extend the forecast horizon of flash floods prediction models. Particularly, introducing rainfall forecast into process-based hydrological models was found effective. However, integrating precipitation predictions into flash flood data-driven models has not been addressed yet. In this endeavor, we propose a modeling framework that integrates rainfall nowcasts and assesses the impact of rainfall predictions uncertainties on a Deep Learning-based flash flood prediction model. Compared to the Persistence and ARIMA models, the LSTM model provided better rainfall nowcasting performance. Further, we proposed an Encoder-Decoder LSTM-based model architecture for short-term flash flood prediction that supports rainfall forecasts. Computational experiments showed that future rainfall values improved flash floods predictability for extended lead times. We also found that rainfall underestimation had a significant adverse effect on the models performance compared to rainfall overestimation

Integrating Precipitation Nowcasting in a Deep Learning-Based Flash Flood Prediction Framework and Assessing the Impact of Rainfall Forecasts Uncertainties

Flash floods are among the most immediate and destructive natural hazards. To issue warnings on time, various attempts were made to extend the forecast horizon of flash floods prediction models. Particularly, introducing rainfall forecast into process-based hydrological models was found effective. However, integrating precipitation predictions into flash flood data-driven models has not been addressed yet. In this endeavor, we propose a modeling framework that integrates rainfall nowcasts and assesses the impact of rainfall predictions uncertainties on a Deep Learning-based flash flood prediction model. Compared to the Persistence and ARIMA models, the LSTM model provided better rainfall nowcasting performance. Further, we proposed an Encoder-Decoder LSTM-based model architecture for short-term flash flood prediction that supports rainfall forecasts. Computational experiments showed that future rainfall values improved flash floods’ predictability for extended lead times. We also found that rainfall underestimation had a significant adverse effect on the model’s performance compared to rainfall overestimation

On the derivation of spatially highly resolved precipitation climatologies under consideration of radar-derived precipitation rates

In this cumulative dissertation, different features and methods are presented to assess and process multi-sensor derived radar data for climatological analysis. The overall objectives were to appraise the limitations of an hourly radar-based quantitative precipitation estimate (QPE) product and to develop and apply reasonable approaches to process these data. Hence the spatial and temporal limitations of radar-derived precipitation rates are discussed in the context of climatological applications, and two types of climatologies are obtained, first a climatology of daily precipitation fields and second a long term precipitation climatology. These relate to questions concerning the methodologies rather than climatological significance or assessment of precipitation and its role in the water balance. Current radar data availability limits such a hydro-climatic analysis. The thesis consists of three peer-reviewed publications. All investigations in this thesis are based on the RADOLAN rw-product of the German Weather Service (DWD) for an extended study region including the Free State of Saxony, Germany, for the period from April 2004 to November 2011. The first publication is dedicated to the classification of daily precipitation fields by unsupervised neural networks. In the presented work, the quality of the radar-derived precipitation rates is analysed by a temporal comparison between recording and non-recording gauges and the corresponding pixels of the RADOLAN rw-product on hourly and daily bases. The analysis shows that a temporal aggregation of the original product should be limited to a temporal scale up to 24 h because of the processing algorithms and the reappearance of previously suppressed errors. Nevertheless, an unsupervised neural network was successfully used for the classification of daily patterns. The derived daily precipitation classes and corresponding precipitation patterns could be assigned to properties of the associated weather patterns and seasonal dependencies. Hence, it could be shown that the classified patterns not only occurred by chance but by statistically proven properties of the atmosphere and of the season. The second publication is primarily concerned with two tasks: first, the pixel-wise fitting of mixture distributions on the bases of the obtained patterns from the first publication, and second, the analysis of spatial consistency of the radar-derived precipitation data set. The fitted parametric distribution functions were analysed in terms of Akaike\'s information criterion and the Kolmogorov-Smirnov test. These benchmarks showed, that the performances are best for mixture distributions derived by an initial classification by an unsupervised neural network and cluster analysis, and by gamma distributions. These results underline the significance of the derived precipitation classes obtained in the first publication. Furthermore, the Kolmogorov-Smirnov test indicates that independent of the distribution function, the radar-derived daily precipitation rates under the assumption of the deployed parametric distribution function has the best or most natural order of precipitation rates at spatial scales from 2 to 4 km for daily precipitation fields. Thus, it is recommended to use the original radar product at these scales rather than at 1 km resolution for daily precipitation sums. In the last publication, the focus shifts from daily to long-term precipitation climatology. The work introduces a rapid and simple approach for processing radar-derived precipitation rates for long-term climatologies. The method could successfully be applied to the radar-derived precipitation rates by excluding or correcting the errors that reappear due to temporal aggregation. Despite the fact that the approach is empirical, the introduced parameters could almost be objectively derived by means of simulation and optimisation. This could be achieved by utilising the reasonable relationship between elevation and precipitation rates for longer periods. Finally, the obtained results are compared to two independently derived precipitation data sets. The comparison shows good agreement of the precipitation fields and illustrates a reasonable application of the introduced procedure. The presented results support the application of the approach for precipitation aggregates of, at least, annual or longer periods. However the derivation of climatologies led to satisfactory results at the respective temporal scales, though the influence of radar-specific errors can only be minimized to a certain degree. Further studies have to prove if an application independent processing of radar-derived precipitation rates leads to higher qualities and validities of the derived data in time and space

COBE's search for structure in the Big Bang

The launch of Cosmic Background Explorer (COBE) and the definition of Earth Observing System (EOS) are two of the major events at NASA-Goddard. The three experiments contained in COBE (Differential Microwave Radiometer (DMR), Far Infrared Absolute Spectrophotometer (FIRAS), and Diffuse Infrared Background Experiment (DIRBE)) are very important in measuring the big bang. DMR measures the isotropy of the cosmic background (direction of the radiation). FIRAS looks at the spectrum over the whole sky, searching for deviations, and DIRBE operates in the infrared part of the spectrum gathering evidence of the earliest galaxy formation. By special techniques, the radiation coming from the solar system will be distinguished from that of extragalactic origin. Unique graphics will be used to represent the temperature of the emitting material. A cosmic event will be modeled of such importance that it will affect cosmological theory for generations to come. EOS will monitor changes in the Earth's geophysics during a whole solar color cycle