Forecasting of Drought: A Case Study of Water-Stressed Region of Pakistan

Demand for water resources has increased dramatically due to the global increase in consumption of water, which has resulted in water depletion. Additionally, global climate change has further resulted as an impediment to human survival. Moreover, Pakistan is among the countries that have already crossed the water scarcity line, experiencing drought in the water-stressed Thar desert. Drought mitigation actions can be effectively achieved by forecasting techniques. This research describes the application of a linear stochastic model, i.e., Autoregressive Integrated Moving Average (ARIMA), to predict the drought pattern. The Standardized Precipitation Evapotranspiration Index (SPEI) is calculated to develop ARIMA models to forecast drought in a hyper-arid environment. In this study, drought forecast is demonstrated by results achieved from ARIMA models for various time periods. Result shows that the values of p, d, and q (non-seasonal model parameter) and P, D, and Q (seasonal model parameter) for the same SPEI period in the proposed models are analogous where “p” is the order of autoregressive lags, q is the order of moving average lags and d is the order of integration. Additionally, these parameters show the strong likeness for Moving Average (M.A) and Autoregressive (A.R) parameter values. From the various developed models for the Thar region, it has been concluded that the model (0,1,0)(1,0,2) is the best ARIMA model at 24 SPEI and could be considered as a generalized model. In the (0,1,0) model, the A.R term is 0, the difference/order of integration is 1 and the moving average is 0, and in the model (1,0,2) whose A.R has the 1st lag, the difference/order of integration is 0 and the moving average has 2 lags. Larger values for R2 greater than 0.9 and smaller values of Mean Error (ME), Mean Absolute Error (MAE), Mean Percentile Error (MPE), Mean Absolute Percentile Error (MAPE), and Mean Absolute Square Error (MASE) provide the acceptance of the generalized model. Consequently, this research suggests that drought forecasting can be effectively fulfilled by using ARIMA models, which can be assist policy planners of water resources to place safeguards keeping in view the future severity of the drought.

Simulating Rainfall Interception by Caatinga Vegetation Using the Gash Model Parametrized on Daily and Seasonal Bases

Rainfall partitioning by trees is an important hydrological process in the contexts of water resource management and climate change. It becomes even more complex where vegetation is sparse and in vulnerable natural systems, such as the Caatinga domain. Rainfall interception modelling allows extrapolating experimental results both in time and space, helping to better understand this hydrological process and contributing as a prediction tool for forest managers. In this work, the Gash model was applied in two ways of parameterization. One was the parameterization on a daily basis and another on a seasonal basis. They were validated, improving the description of rainfall partitioning by tree species of Caatinga dry tropical forest already reported in the scientific literature and allowing a detailed evaluation of the influence of rainfall depth and event intensity on rainfall partitioning associated with these species. Very small (0.0–5.0 mm) and low-intensity (0–2.5 mm h−1) events were significantly more frequent during the dry season. Both model approaches resulted in good predictions, with absence of constant and systematic errors during simulations. The sparse Gash model parametrized on a daily basis performed slightly better, reaching maximum cumulative mean error of 9.8%, while, for the seasonal parametrization, this value was 11.5%. Seasonal model predictions were also the most sensitive to canopy and climatic parameters.

Prediction of China’s Housing Price Based on a Novel Grey Seasonal Model

The fluctuation of real estate prices has an important impact on China's economic development. Accurate prediction of real estate market price changes has become the focus of scholars. The existing prediction methods not only have great limitations on the input variables but also have many deficiencies in the nonlinear prediction. In the process of real estate market price forecasting, the priority of data and the seasonal fluctuation of housing price are important influencing factors, which are not taken into account in the traditional model. In order to overcome these problems, a novel grey seasonal model is proposed to predict housing prices in China. The main method is to introduce seasonal factor decomposition into the new information priority grey prediction model. Two practical examples are used to test the performance of the new information priority grey seasonal model. The results show that compared with the existing prediction models, this method has better applicability and provides more accurate prediction results. Therefore, the proposed model can be a simple and effective tool for housing price prediction. At the same time, according to the prediction results, this paper analyzes the causes of housing price changes and puts forward targeted suggestions.

Natural hazards in the mountainous region of Rio de Janeiro state, Brazil: rainfall erosivity as an early warning index

Rainfall erosivity is defined as the potential of rain to cause erosion. It has great potential for application in studies related to landslides and floods, in addition to water erosion. The objectives of this study were: i) to model the Rday using a seasonal model for the Mountainous Region of the State of Rio de Janeiro (MRRJ); ii) to adjust thresholds of the Rday index based on catastrophic events which occurred in the last two decades; and iii) to map the maximum daily rainfall erosivity (Rmaxday) to assess the region’s susceptibility to rainfall hazards according to the established Rday limits. The fitted Rday model presented a satisfactory result, thereby enabling its application as an estimator of the daily rainfall erosivity in MRRJ. Events that resulted in Rday > 1,500 MJ.ha-1.mm.h-1.day-1 were those with the highest number of fatalities. The spatial distribution of Rmaxday showed that the entire MRRJ has presented values that can cause major rainfall. The Rday index proved to be a promising indicator of rainfall hazards, which is more effective than those normally used that are only based on quantity (mm) and/or intensity (mm.h-1) of the rain.

How BFAST Trend and Seasonal Model Components Affect Disturbance Detection in Tropical Dry Forest and Temperate Forest

Time series analysis has gained popularity in forest disturbance monitoring thanks to the availability of satellite and airborne remote sensing images and the development of different time series methods for change detection. Previous research has focused on time series data noise reduction, the magnitude of breakpoints, and accuracy assessment; however, few have looked in detail at how the trend and seasonal model components contribute to disturbance detection in different forest types. Here, we use Landsat time series images spanning 1994–2018 to map forest disturbance in a western Pacific area of Mexico, where both temperate and tropical dry forests have been subject to severe deforestation and forest degradation processes. Since these two forest types have distinct seasonal characteristics, we investigate how trend and seasonal model components, such as the goodness-of-fit (R2), magnitude of change, amplitude, and model length in a stable historical period, affect forest disturbance detection. We applied the Breaks For Additive Season and Trend Monitor (BFAST) algorithm and after accuracy assessment by stratified random sample points, and we obtained 68% and 86% of user accuracy and 75.6% and 86% of producer’s accuracy in disturbance detection, in tropical dry forests and temperate forests, respectively. We extracted the noncorrelated trend and seasonal model components R2, magnitude, amplitude, length of the stable historical period, and percentage of pixels with NA and tested their effects on disturbance detection employing forest-type specific logistic regression. Our results showed that, for all forests combined, the amplitude and stable historical period length contributed to disturbance detection. While for tropical dry forest alone, amplitude was the main predictor, and for the temperate forest alone, the stable historical period length contributed most to the prediction, although it was not statistically significant. These findings provide insights for improving the results of forest disturbance detection in different forest types.

Inventory Management Evaluation and Inventory Forecast Using EOQ

The fashion industry that is gamis in Indonesia is growing rapidly because the majority of the population is Moslem. Elzatta is a company that does business on Moslem clothing, one of its products is the gamis. The company is experiencing problems with stockpiling in warehouses, because of models that were not sold, as well as outdated models. With the accumulation of products in warehouses in 2017 and 2018, many products will be damaged. For this reason, the company runs a buy one get one business strategy and sells products at low prices. As a result, the company suffered a substantial loss. For this reason, it is necessary to evaluate the inventory management that has been carried out using the EOQ model. For 2019, it is necessary to plan the number of products to be sold and apply the EOQ model. The results of evaluations in 2017 and 2018, by using EOQ the company could save 64.78% for 2017 and 63.40% for 2018. Whereas for 2019, after forecasting the number of sales using the seasonal model, sales projections are similar to the number of sales in the previous years, so that the number of products needed for a single order is 1364 pcs.

Predicting virologically-confirmed influenza using school absences in Allegheny County, Pennsylvania, USA during the 2007-2015 influenza seasons

Background Children are important in community-level influenza transmission. School-based monitoring may inform influenza surveillance. Methods We used reported weekly confirmed influenza in Allegheny County during the 2007, and 2010-2015 influenza seasons using Pennsylvania’s Allegheny County Health Department all-age influenza cases from health facilities, and all-cause and influenza-like illness (ILI)-specific absences from nine county school districts. Negative binomial regression predicted influenza cases using all-cause and illness-specific absence rates, calendar week, average weekly temperature and relative humidity, using four cross-validations. Results School districts reported 2,184,220 all-cause absences (2010-2015). Three one-season studies reported 19,577 all-cause and 3,012 ILI-related absences (2007, 2012, 2015). Over seven seasons, 11,946 confirmed influenza cases were reported. Absences improved seasonal model fits and predictions. Multivariate models using elementary school absences outperformed middle and high school models (relative mean absolute error (relMAE)=0.94, 0.98, 0.99). K-5 grade-specific absence models had lowest mean absolute errors (MAE) in cross-validations. ILI-specific absences performed marginally better than all-cause absences in two years, adjusting for other covariates, but markedly worse one year. Conclusions Our findings suggest seasonal models including K-5th grade absences predict all-age confirmed influenza and may serve as a useful surveillance tool.