Single Estimation Method Research Articles

Using a Triple Sensor Collocation Approach to Evaluate Small-Holder Irrigation Scheme Performances in Northern Ethiopia

This study uses a triple-sensor collocation approach to evaluate the performance of small-holder irrigation schemes in the Zamra catchment of Northern Ethiopia. Crop water productivity (CWP), as an integrator of biomass production and water use, was used to compare the overall efficiencies of three types of irrigation systems: traditional and modern diversions, and dam-based irrigation water supply. Farmer-reported data often rely on observations, which can introduce human estimation and measurement errors. As a result, the evaluation of irrigation scheme performance has frequently been insufficient to fully explain crop water productivity. To overcome the challenges of using one single estimation method, we used a triple-sensor collocation approach to evaluate the efficiency of three small-scale irrigation schemes, using water productivity as an indicator. It employed three independent methods: remotely sensed data, a model-based approach, and farmer in-situ estimates to assess crop yields and water consumption. To implement the triple collocation appraisal, we first applied three independent evaluation methods, i.e., remotely sensed, model-based, and farmer in-situ estimates of crop yields and water consumption, to assess the crop water productivities of the systems. Triple-sensor collocation allows for the appraisal and comparison of estimation errors of measurement sensor systems, and enables the ranking of the estimators by their quality to represent the de-facto unknown true value, in our case: crop yields, water use, and its ratio CWP, in small-holder irrigated agriculture. The study entailed four main components: (1) collecting in-situ information and data from small-holder farmers on crop yields and water use; (2) derivation of remote sensing-based CWP from the FAO WaPOR open database and time series; (3) evaluation of biomass, crop yields and water use (evapotranspiration) using the AquaCrop model, integrating climate, soil data, and irrigation management practices; (4) performing and analysis of a categorical triple collocation analysis of the independent estimator data and performance ranking of the three sensing and small-holder irrigation systems. Maize and vegetables were used as main crops during three consecutive irrigation seasons (2017/18, 2018/19, 2019/20). Civil war prevented further field surveying, in-situ research, and data collection. The results indicate that remote sensing products are performed best in the modern and dam irrigation schemes for maize. For vegetables, AquaCrop performed best in the dam irrigation scheme.

A high-resolution map of diffuse groundwater recharge rates for Australia

Abstract. Estimating groundwater recharge rates is important to understand and manage groundwater. Numerous studies have used collated recharge datasets to understand and project regional- or global-scale groundwater recharge rates. However, recharge estimation methods all have distinct assumptions, quantify different recharge components and operate over different temporal scales. We use over 200 000 groundwater chloride measurements to estimate groundwater recharge rates using an improved chloride mass balance (CMB) method across Australia. Groundwater recharge rates were produced stochastically using gridded chloride deposition, runoff and precipitation datasets. After filtering out groundwater recharge rates where the assumptions of the method may have been compromised, 98 568 estimates of recharge were produced. The resulting groundwater recharge rates and 17 spatial datasets were integrated into a random forest regression algorithm, generating a high-resolution (0.05°) model of groundwater recharge rates across Australia. The regression reveals that climate-related variables, including precipitation, rainfall seasonality and potential evapotranspiration, exert the most significant influence on groundwater recharge rates, with vegetation (the normalised difference vegetation index or NDVI) also contributing significantly. Importantly, the mean values of both the recharge point dataset (43.5 mm yr−1) and the spatial recharge model (22.7 mm yr−1) are notably lower than those reported in previous studies, underscoring the prolonged timescale of the CMB method, the potential disparities arising from distinct recharge estimation methodologies and limited averaging across climate zones. This study presents a robust and automated approach to estimate recharge using the CMB method, offering a unified model based on a single estimation method. The resulting datasets, the Python script for recharge rate calculation and the spatial recharge models collectively provide valuable insights for water resource management across the Australian continent, and similar approaches can be applied globally.

Longitudinal-lateral-cooperative estimation algorithm for vehicle dynamics states based on adaptive-square-root-cubature-Kalman-filter and similarity-principle

It is infeasible to measure vehicle dynamics states (VDS) directly without expensive measurement instruments, especially for the tire-road peak adhesion coefficient (μmax). However, four-wheel-independent-drive-electric-vehicle (4WIDEV) provides convenience for the observation of these dynamic states, because the rotation rate and torque of the in-wheel motor can be acquired directly. Vehicle nonlinear longitudinal-lateral dynamics, the single estimation method for all VDS and the time-varying measurement noise of sensors cause difficulties for the observation. Common the extended-Kalman-filter (EKF) is unsuitable to estimate VDS in strong nonlinear region. This paper propose a longitudinal-lateral cooperative estimation algorithm based on adaptive-square-root-cubature-Kalman-filter (ASRCKF) and partitioned similarity-principle (SP) to estimate the vehicle states and the tire-road peak adhesion coefficient sequentially for 4WIDEV. Firstly, a nonlinear 7-degree-of-freedom (7DOF) vehicle model and magic-formula (MF) tire model are built as the base of the successive estimation scheme. Then, recursive-least-squares (RLS) is adopted to estimate the tire longitudinal force. With the estimated tire longitudinal force, an ASRCKF which can be adjusted adaptively by the feedback dynamics states, is designed for the estimation of vehicle states. Next, the SP algorithm combined with the characteristic of longitudinal-lateral dynamics, which is benefit for μmax estimation when tire dynamics enters the nonlinear region, is proposed. Finally, experiment and simulation results show that excellent performance can be achieved with the proposed estimation method in varying driving conditions.

Real-time estimation of road slope based on multiple models and multiple data fusion

Although driving conditions of vehicles are complex, a single model is used in conventional slope estimation methods. This reduces the ability to track changes in actual road slopes in real time, resulting in low estimation accuracy. Furthermore, slope estimation based on a single estimation method are affected by braking, shifting, and sensor failure. This reduces the robustness and reliability of the slope estimation. To address these problems, we propose a slope estimation algorithm based on multi-model and multi-data fusion. First, for each estimation method (kinematics-based and dynamics-based), a two-layer interacting multiple model (TLIMM) slope estimation algorithm is developed. Second, the decision-level estimation fusion is based on the slope data estimated by the two methods. The experimental results show that the proposed TLIMM algorithm and multi-data estimation fusion method based on TLIMM can effectively improve the accuracy, robustness and reliability of the slope estimation.

Legal framework and current techniques for age estimation in puppy trade

Dog breeding and dog trade are strictly regulated by EU-legislation, as well as by national and regional legislations. Nevertheless, it is believed that part of the puppies traded within the EU are younger than the legal minimum age, with the risk of compromising their own health and wellbeing as well as public health. A proper age determination is necessary to expose potential malpractices. Combining multiple independent methods is essential to compensate for possible variations in one single estimation method. Assessment of the dental age and bone age, more specifically by evaluating the eruption status of the dentition and the radiographic appearance and formation of ossification centers are both eligible. However, current available data are not detailed enough. In order to improve the accuracy and reduce the error in age estimation, more breed specific data are required, more intermediate development stages must be described and possible developmental disorders and pathologies must be considered.

Ensemble effort estimation using selection and genetic algorithms

Software effort estimation plays a vital role in software project management. Several single estimation methods have been introduced vying to be the best estimation method. However, it is difficult to decide which method offers the best estimation result. This study proposes an ensemble effort estimation from several single estimation methods which yield different estimations but comparably high accuracy. The proposed technique employs a correlation-based feature selection algorithm to choose methods out of twelve methods derived from combinations of four transformation and three learning techniques. The ensemble then uses a genetic algorithm to build a mathematical function to compute one combined estimation from those selected method estimations. Experiments are set up based on six benchmark data sets to evaluate estimation performance. The resulting measure of error metrics show that the proposed ensemble technique deploying only necessary estimation methods can yield more accurate estimation than the best method from the twelve methods.

Investigating the Dynamic Interaction between Military Spending and Economic Growth

AbstractThe relationship and interaction of military spending and economic growth have been theoretically and empirically investigated since the 1970s but it still cannot provide conclusive evidence towards the direction and the quantification of the impact between the two magnitudes. The use of different data sets in terms of time periods, and number and geographic location of countries, different theoretical background leading to different econometric specifications, and single type of econometric methodology, make any comparison impossible. This paper looks into the dynamic interaction between military spending and economic growth during the period 1988–2013 that includes the recent years of economic crisis covering 138 countries without making any prior assumptions about the theoretical channels of influence, while not limited to a single estimation method but employing a wide range of methodologies in order to form a complete picture of the long‐ and short‐run interaction. Furthermore, as such interaction might not be linear, we create three groups of countries based on the countries' income developmental stage. Overall we find no evidence of long‐ and short‐run causality from the military spending to economic growth except for the developing countries (positive in the long run). However, from economic growth to military spending we find a positive impact for all groups except the least developed countries. We also notice the interaction was more prominent prior to the start of the economic crisis.

Iterative channel estimation method for long‐term evolution downlink transmission

Long-term evolution (LTE) standard employs orthogonal frequency division multiplexing (OFDM) for downlink (DL) data transmission. As pilot subcarriers are non-uniformly and sparsely spaced in the OFDM DL transmission, more complicated channel estimation methods may have to be adopted in the mobile devices to ensure adequate performance. An iterative approach is proposed for the channel estimation task, in which an inverse fast Fourier transform and fast Fourier transform pair is iteratively used to smooth channel estimates provided by decision feedback. The performance of this approach is compared with two two-dimensional interpolation methods and one discrete Fourier transform-based estimation method that have been proposed for the LTE DL transmission. Simulation results show that the performance of the proposed method surpasses all the other methods except at high signal-to-noise ratio, and moreover, not a single estimation method consistently performs better than the proposed method for all the channel models considered. Thus, the proposed method is the best among the considered methods for LTE DL transmission.

Power laws in top wealth distributions: evidence from Canada

This article investigates Pareto power law (PPL) behavior at the top of the Canadian wealth distribution. To this end, Canadian Business data on the wealthiest 100 Canadians for the years 1999–2008 are used. The resulting estimates of the PPL exponent ranged from approximately 1.0 to 1.3 depending on the year of analysis and the estimation method used. These estimates are roughly comparable to those based on Forbes’ list of the wealthiest 400 Americans. Furthermore, whereas modified OLS and maximum likelihood estimates of the power law exponents conform to Zipf’s law, the OLS estimates do not. These results raise some concerns about deducing the magnitudes of and trends in the power law exponents based on a single estimation method and highlight the importance of extensive hypothesis testing for model adequacy. The battery of diagnostic tests pertaining to PPL behavior at the top of the Canadian wealth distribution yields some conflicting results.

Petroleum Reserves and the Credibility Gap

This paper focuses on studying the available definitions of petroleum reserves and the resulting credibility gap. The credibility gap refers to the apparent disparity amongst the reported petroleum reserves values. There are two main reasons for this gap; the first reason is that there is no consistent definition of petroleum reserves. In fact there are many definitions available today in the oil industry. In addition, the wording of each definition is sufficiently vague allow- ing evaluators a great deal of latitude when classifying reserves. Furthermore, oil companies tend to use definitions that fulfill their specific goals. This misuse, along with the companies' pressure to look good in the financial market, has led to a bending of the rules. Consequently, it is necessary to adopt a consistent definition with clear wording in order to enhance communication and to avoid a situation where the definition of petroleum reserves loses all meanings. The second reason for the gap is that petroleum reserves are not measurable quantities; they are only estimates. As a matter of fact there is no single estimation method and there is always uncertainty in each method. This reason coupled with the countries' underlying poli- tics and prevailing economics govern the credibility of the reported reserves values. Therefore, it is necessary to provide a methodology for estimating reserves on a common basis in order to improve the support for financial reporting and to attract the public financing.