Investigating the impact of excess zeros on hurdle‐generalized Poisson regression model with right censored count data

Typically, a Poisson model is assumed for count data. In many cases, there are many zeros in the dependent variable, thus the mean is not equal to the variance value of the dependent variable. Therefore, Poisson model is not suitable anymore for this kind of data because of too many zeros. Thus, we suggest using a hurdle‐generalized Poisson regression model. Furthermore, the response variable in such cases is censored for some values because of some big values. A censored hurdle‐generalized Poisson regression model is introduced on count data with many zeros in this paper. The estimation of regression parameters using the maximum likelihood method is discussed and the goodness‐of‐fit for the regression model is examined. An example and a simulation will be used to illustrate the effects of right censoring on the parameter estimation and their standard errors.     

Factors Influencing Markov Chains Predictability Characteristics, Utilizing SPI, RDI, EDI and SPEI Drought Indices in Different Climatic Zones

Comparability analyses were carried out to investigate behavioural aspects of effective drought index (EDI), standardized precipitation index (SPI), reconnaissance drought index (RDI) and standardized precipitation evapotranspiration index (SPEI), considering 3-month, 6-month and annual time periods. Investigations included parametric/non-parametric correlation analysis among indices, climatic zone influence, record length impacts and evapotranspiration role (RDI and SPEI) on Markov chains predictability characteristics. Except for the EDI, all indices/cases (all climatic zones) showed significant correlation. In arid/semi-arid climates, the 3-month and 6-month maximum drought severities were detected by the RDI and annual maximum drought severities were detected by the SPEI, emphasizing the evapotranspiration influence. In all climatic zones, the EDI values for wet (dry) periods were higher (lower), compared to other indices. First order dependency was detected for the EDI (all cases) and the SPI (most cases), over entire period (1951–2011) and sub-periods [(1951–1981), (1982–2011)]. The largest number of second order dependency was detected by the SPEI, followed by a relatively large number of such cases by the RDI (3-month time period), for the 61-year data period. This research showed that several factors influence Markov chains predictability characteristics in drought studies, particularly the impact of record length and evapotranspiration (RDI and SPEI) were confirmed.     

Development of a Simulation Model for Estimation of Potential Recharge in a Semi-arid Foothill Region

The SMPR (Soil Moisture and Potential Recharge) model is developed to simulate soil moisture content and potential recharge under semi-arid conditions. In SMPR model, infiltration and soil moisture redistribution follow two successive stages. In stage (I), precipitation infiltrates and is distributed into the soil profile utilizing the soil moisture accounting fashion and in stage (II), moisture is redistributed using simplified Richards’ equation (neglecting matric-potential gradient). Liquid and vapor evaporation from bare soil are estimated based on Dual-Crop methodology [Ke and optimized Kcb (0.17)]. Two commonly applied unsaturated hydraulic conductivity functions [K(θ)] of B-C (Brooks and Corey) and V-G (van-Genuchten); and an Empirical Exponential (E-E) equation are locally calibrated and used for potential recharge estimation (as main simulation objective). Model performance (calibration/validation) is based on reasonable estimation of potential recharge and acceptable simulation of soil moisture, considering local lysimeter data. According to results, B-C, V-G an E-E equations produced acceptable simulation of soil moisture content (NRMSE?<?30%), however, potential recharge was underestimated/overestimated, using K(θ) by B-C/V-G. The best estimation of potential recharge (based on absolute annual recharge error, ?Q?<?10%) was achieved by the SMPR model with K(θ) of E-E. Results of the relative simple SMPR model [K(θ) by E-E equation] compared favorably with HYDRUS-1D sophisticated model [using locally calibrated V-G equation of K(θ)].The proposed SMPR model requiring minimal data, can be used in regions with limited data.     

Prediction of Water-Level in the Urmia Lake Using the Extreme Learning Machine Approach

Predicting the dynamics of water-level in lakes plays a vital role in navigation, water resources planning and catchment management. In this paper, the Extreme Learning Machine (ELM) approach was used to predict the daily water-level in the Urmia Lake. Daily water-level data from the Urmia Lake in northwest of Iran were used to train, test and validate the employed models. Results showed that the ELM approach can accurately forecast the water-level in the Urmia Lake. Outcomes from the ELM model were also compared with those of genetic programming (GP) and artificial neural networks (ANNs). It was found that the ELM technique outperforms GP and ANN in predicting water-level in the Urmia Lake. It also can learn the relation between the water-level and its influential variables much faster than the GP and ANN. Overall, the results show that the ELM approach can be used to predict dynamics of water-level in lakes.     

SIMETAW# - a Model for Agricultural Water Demand Planning

A successful water management scheme for irrigated crops requires an integrated approach, which accounts for water, soil, and crop management. SIMETAW# is a user friendly soil water balance model that assesses crop water use, irrigation requirements, and generates hypothetical irrigation schedules for a wide range of crops experiencing full or deficit irrigation. SIMETAW# calculates reference evapotranspiration (ET_o), and it computes potential crop evapotranspiration (ET_c), and the evapotranspiration of applied water (ET_aw), which is the amount of irrigation water needed to match losses from the effective soil root zone due to ET_c that are not replaced by precipitation and other sources. Using input information on crop and soil characteristics and the distribution uniformity of infiltrated irrigation applications in full or deficit conditions, the model estimates the mean depth of infiltrated water (IW) into each quarter of the field. The impact of deficit irrigation on the actual crop evapotranspiration (ET_a) is computed separately for each of the four quarters of the cropped field. SIMETAW# simulation adjusts ET_o estimates for projected future CO₂ concentration, and hence the model can assess climate change impacts on future irrigation demand allowing the user to propose adaptation strategies that potentially lead to a more sustainable water use. This paper discusses the SIMETAW# model and evaluates its performance on estimating ET_c, ET_a, and ET_aw for three case studies.     

Precipitation Estimation Using Support Vector Machine with Discrete Wavelet Transform

Precipitation prediction is of dispensable importance in many hydrological applications. In this study, monthly precipitation data sets from Serbia for the period 1946–2012 were used to estimate precipitation. To fulfil this objective, three mathematical techniques named artificial neural network (ANN), genetic programming (GP) and support vector machine with wavelet transform algorithm (WT-SVM) were applied. The mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), Pearson correlation coefficient (r) and coefficient of determination (R²) were used to evaluate the performance of the WT-SVM, GP and ANN models. The achieved results demonstrate that the WT-SVM outperforms the GP and ANN models for estimating monthly precipitation.     

Improving Consumer Satisfaction in Water Distribution Networks Through Optimal Use of Auxiliary Tanks (A Case Study of Kashan City,Iran)

Temporal and spatial variations in pressure may lead to consumer dissatisfaction and distrust of water distribution networks when it comes to reliable performance. Pressure management is a set of programs and operations conducted in water distribution networks to adjust the pressure. Constructing new auxiliary tanks in proper locations at the best height for the area they serve minimizes the pressure fluctuations. Additionally, chlorine is often injected in the reservoirs and tanks to improve the water quality. The goal of this research was to improve the condition of the network by adding auxiliary tanks with appropriate locations, heights and chlorine concentration. An optimization model is prepared to optimize consumer satisfaction, water quality and the relevant costs as objective functions. The performance of the models are evaluated by a selected case study; and the objectives are optimized in three scenarios. Using the proposed model in a water distribution network, a trade-off diagram of reliability and costs is obtained, that lets the decision makers select the proper options considering the available fund. A new indicator, the consumer satisfaction index, is also proposed as a way to evaluate the performance of water distribution networks.     

A nonparametric approach to measurement of efficiency and technological change: The case of large U.S. commercial banks

The purpose of this paper is to derive the efficiency measures and the rate of technological change for a sample of large U.S. commercial banks by employing a nonparametric technique. This technique is used to construct a multiproduct production frontier relative to which the efficiency measures of the banks in the sample are calculated and the displacement of which over time provides a measure of the rate of technological change. The empirical results indicate that the relevant frontier shifted inward between 1980 and 1985 reflecting a high pace of technological advancement achieved by the banks in the sample. The pace varied significantly across the banks with some banks even regressing over time.     

The Effect of Audit Committee Characteristics on Financial Restatements in Malaysia

This study investigates the influence of audit committee characteristics on the likelihood of financial restatements by firms in Malaysia. Annual reports of 350 firms that have restated their financial statements in the year 2008 and 2009 are analyzed. An additional 350 firms that did not restate their financial statements are considered, resulting in a total of 700 observations. Regression analysis identifies audit committee characteristics such as its independence, size, expertise and activity as statistically significant in explaining the likelihood of financial restatements. This article highlights the important role of the audit committee in mitigating financial restatements by firms in Malaysia.