Mechanistic Drifting Forecast Model for A Small Semi-Submersible Drifter Under Tide–Wind–Wave Conditions

Understanding the drifting motion of a small semi-submersible drifter is of vital importance regarding monitoring surface currents and the floating pollutants in coastal regions. This work addresses this issue by establishing a mechanistic drifting forecast model based on kinetic analysis. Taking tide–wind–wave into consideration, the forecast model is validated against in situ drifting experiment in the Radial Sand Ridges. Model results show good performance with respect to the measured drifting features, characterized by migrating back and forth twice a day with daily downwind displacements. Trajectory models are used to evaluate the influence of the individual hydrodynamic forcing. The tidal current is the fundamental dynamic condition in the Radial Sand Ridges and has the greatest impact on the drifting distance. However, it loses its leading position in the field of the daily displacement of the used drifter. The simulations reveal that different hydrodynamic forces dominate the daily displacement of the used drifter at different wind scales. The wave-induced mass transport has the greatest influence on the daily displacement at Beaufort wind scale 5–6; while wind drag contributes mostly at wind scale 2–4.

Study on soil erosion dynamics in typical regionof southern China based on remote sensing, GISand gray forecast model

This paper StUdies soil erosion dynamics in the typical region of southem China based onremote sensing, GIS tecndques and gray forecast model. The resultS of survey on Xingguo countyshown the soil eroded area and annual soil erosion amount decreased by 19.09% and 43.05%reSPectively from 1958 to 1988. The results of gray forecast model presented that soil eroded areaincreased from 818.04 km2 in 1988 to 1276.69 km2 in 1995. in the meanthne the total soil erosiollamount decreased from 607.21×104 ba in 1988 to 472. 12 ×104 t/a in 1995. By comparing differentlanduse types, the soil loss modulus of the forest was the lowest with 177. 16~187.75t/km2. a, on thecontraly the bare land was the highest with 10626.76~11265.48 t/km2. a. so the high vegetationcoverage can decrease soil and water loss effectively.

Comparison of aircraft observations with ensemble forecast model results in terms of the microphysical characteristics of stratiform precipitation

The prediction of the particle number concentration and liquid/ice water content of cloud is significant for many aspects of atmospheric science.However,given the uncertainties in the initial and boundary conditions and imperfections of microphysical schemes,the accurate prediction of these microphysical properties of cloud is still a big challenge.The ensemble approach may be a viable way to reduce forecast uncertainties.In this paper,a large-scale stratiform cloud precipitation process is studied by comparing results of a 10-member ensemble forecast model with aircraft observation data.By means of the ensemble average,the prediction of bulk parameters such as liquid water content and ice water content can be improved in comparison with the control member,but the particle number concentrations are still one to two orders of magnitude less than those from observations.Intercomparison of raindrop size spectra reveals a big distinction between observations and predictions for particles with a diameter less than 1000μm.

Remote Sensing and GIS Based Spectro-Agrometeorological Maize Yield Forecast Model for South Tigray Zone, Ethiopia

Remote-sensing data acquired by satellite imageries have a wide scope in agricultural applications owing to their synoptic and repetitive coverage. This study reports the development of an operational spectro-agrometereological yield model for maize crop derived from time series data of SPOT VEGETATION, actual and potential evapotranspiration and rainfall estimate satellite data for the years 2003-2012. Indices of these input data were utilized to validate their strength in explaining grain yield recorded by the Central Statistical Agency through correlation analyses. Crop masking at crop land area was applied and refined using agro-ecological zones suitable for maize. Rainfall estimates and average Normalized Difference Vegetation Index were found highly correlated to maize yield with the former accounting for 85% variation and the latter 80%, respectively. The developed spectro-agrometeorological yield model was successfully validated against the predicted Zone level yields estimated by Central Statistical Agency (r2 = 0.88, RMSE = 1.405 q·ha-1 and 21% coefficient of variation). Thus, remote sensing and geographical information system based maize yield forecast improved quality and timelines of the data besides distinguishing yield production levels/areas and making intervention very easy for the decision makers thereby proving the clear potential of spectro-agrometeorological factors for maize yield forecasting, particularly for Ethiopia.

Study on Population Forecast Model in Planning of Land Use

On the basis of describing characteristics and condition of application of natural growth model of population,weighted average growth model,regression forecast model and GM(1,1) forecast model,taking Gushi County in Henan Province as an example,according to the statistics of population in Gushi County Statistical Yearbook from 1991 to 2007,we establish four models to conduct fitting on population change respectively,and meanwhile,we predict population size from 2008 to 2009 and conduct preciseness test on the population size.The test results show that the preciseness of forecast results of natural growth model is not high,and the preciseness of forecast results of weighted average growth model is not scientific when the total size of population is unstable.The results of GM(1,1) forecast model and regression forecast model largely conform to the actual data,so we can take the mean of the two as the final forecast result.

The Modified GM( 1 , 1) Grey Forecast Model

Because the impacts of the factors such as some disturbances are graduallyadded into the system, the grey forecast results will deviate from the systemtrue value. To improve the forecast precision, Pro-Dens Julons provided twomethfor-But they had not consider the impact of artificial disturbance. LiZhihua et al. of Qinghua Univ. presented another method. This paper revisesthe method and make it be a spocial case.

Impact of festival factor on electric quantity multiplication forecast model

This research aims to improve the forecasting precision of electric quantity. It is discovered that the total electricity consumption considerably increased during the Spring Festival by the analysis of the electric quantity time series from 2002 to 2007 in Shandong province. The festival factor is ascertained to be one of the important seasonal factors affecting the electric quantity fluctuations, and the multiplication model for forecasting is improved by introducing corresponding variables and parameters. The computational results indicate that the average relative error of the new model decreases from 4.31% to 1.93% and the maximum relative error from 14.05% to 6.52% compared with those of the model when the festival factor is not considered. It shows that introducing the festival factor into the multiplication model for electric quantity forecasting evidently improves the precision.

Evaluation of building energy demand forecast models using multi-attribute decision making approach

With the existence of several conventional and advanced building thermal energy demand forecast models to improve the energy efficiency of buildings,it is hard to find an appropriate,convenient,and efficient model.Evaluations based on statistical indexes(MAE,RMSE,MAPE,etc.)that characterize the accuracy of the forecasts do not help in the identification of the efficient building thermal energy demand forecast tool since they do not reflect the efforts entailed in implementation of the forecast model,i.e.,data collection to production/use phase.Hence,this work presents a Gini Index based Measurement of Alternatives and Ranking according to COmpromise Solution(GI-MARCOS),a hybrid Multi Attribute Decision Making(MADM)approach for the identification of the most efficient building energy demand forecast tool.GI-MARCOS employs(i)GI based objective weight method:assigns meaningful objective weights to the attributes in four phases(1:pre-processing,2:implementation,3:post-processing,and 4:use phase)thereby avoiding unnecessary biases in the expert’s opinion on weights and applicable to domains where there is a lack of domain expertise,and(ii)MARCOS:provides a robust and reliable ranking of alternatives in a dynamic environment.A case study with three alternatives evaluated over three to six attributes in four phases of implementation(pre-processing,implementation,post-processing and use)reveals that the use of GI-MARCOS improved the accuracy of alternatives MLR and BM by 6%and 13%,respectively.Moreover,additional validations state that(i)MLR performs best in Phase 1 and 2,while ANN performs best in Phase 3 and 4 with BM providing a mediocre performance in all four phases,(ii)sensitivity analysis:provides robust ranking with interchange of weights across phases and attributes,and(iii)rank correlation:ranks produce by GI-MARCOS has a high correlation with GRA(0.999),COPRAS(0.9786),and ARAS(0.9775).

Comparison among the UECM Model, and the Composite Model in Forecasting Malaysian Imports

For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding a good statistical model for time series predicting imports in Malaysia is the main target of this study. The decision made during this study mostly addresses the unrestricted error correction model (UECM), and composite model (Combined regression—ARIMA). The imports of Malaysia from the first quarter of 1991 to the third quarter of 2022 are employed in this study’s quarterly time series data. The forecasting outcomes of the current study demonstrated that the composite model offered more probabilistic data, which improved forecasting the volume of Malaysia’s imports. The composite model, and the UECM model in this study are linear models based on responses to Malaysia’s imports. Future studies might compare the performance of linear and nonlinear models in forecasting.

A Hybrid Model Evaluation Based on PCA Regression Schemes Applied to Seasonal Precipitation Forecast

Possible changes in the structure and seasonal variability of the subtropical ridge may lead to changes in the rainfall’s variability modes over Caribbean region. This generates additional difficulties around water resource planning, therefore, obtaining seasonal prediction models that allow these variations to be characterized in detail, it’s a concern, specially for island states. This research proposes the construction of statistical-dynamic models based on PCA regression methods. It is used as predictand the monthly precipitation accumulated, while the predictors (6) are extracted from the ECMWF-SEAS5 ensemble mean forecasts with a lag of one month with respect to the target month. In the construction of the models, two sequential training schemes are evaluated, obtaining that only the shorter preserves the seasonal characteristics of the predictand. The evaluation metrics used, where cell-point and dichotomous methodologies are combined, suggest that the predictors related to sea surface temperatures do not adequately represent the seasonal variability of the predictand, however, others such as the temperature at 850 hPa and the Outgoing Longwave Radiation are represented with a good approximation regardless of the model chosen. In this sense, the models built with the nearest neighbor methodology were the most efficient. Using the individual models with the best results, an ensemble is built that allows improving the individual skill of the models selected as members by correcting the underestimation of precipitation in the dynamic model during the wet season, although problems of overestimation persist for thresholds lower than 50 mm.

Correlation Analysis for the Attack of Bacillary Dysentery and Meteorological Factors Based on the Chinese Medicine Theory of Yunqi and the Medical-Meteorological Forecast Model

Objective： To explore the impact of meteorological factors on the outbreak of bacillary dysentery, so as to provide suggestions for disease prevention. Methods： Based on the Chinese medicine theory of Yunqi, the descriptive statistics, single-factor correlation analysis and back-propagation artificial neural net-work were conducted using data on five basic meteorological factors and data on incidence of bacillary dysentery in Beijing, China, for the period 1970-2004. Results： The incidence of bacillary dysentery showed significant positive correlation relationship with the precipitation, relative humidity, vapor pressure, and temperature, respectively. The incidence of bacillary dysentery showed a negatively correlated relationship with the wind speed and the change trend of average wind speed. The results of medical-meteorological forecast model showed a relatively high accuracy rate. Conclusions： There is a close relationship between the meteorological factors and the incidence of bacillary dysentery, but the contributions of which to the onset of bacillary dysentery are different to each other.

Forecast correct model of overpressure attenuation during gas explosion in excavation roadway

Through analyzing experimental data of gas explosions in excavation roadwaysand the forecast models of the literature, Found that there is no direct proportional linearcorrelation between overpressure and the square root of the accumulated volume of gas,the square root of the propagation distance multiplicative inverse.Also, attenuation speedof the forecast model calculation is faster than that of experimental data.Based on theoriginal forecast models and experimental data, deduced the relation of factors by introducinga correlation coefficient with concrete volume and distance, which had been verifiedby the roadway experiment data.The results show that it is closer to the roadway experimentaldata and the overpressure amount increases first then decreases with thepropagation distance.

Development of yield forecast model using multiple regression analysis and impact of climatic parameters on spring wheat

Understanding the impacts of climate change in agriculture is important to ensure optimal and continuous crop production.The agricultural sector plays a significant role in the economy of Upper Midwestern states in the USA,especially that of North Dakota(ND).Spring wheat contributes most of the wheat production in ND,which is a major producer of wheat in the USA.This study focuses on assessing possible impacts of three climate variables on spring wheat yield in ND by building a regression model.Eighty-five years of field data were collected and the trend of average minimum temperature along with average maximum temperature,average precipitation,and spring wheat yield was analyzed using Mann-Kendall test.The study area was divided into 9 divisions based on physical locations.The minimum temperature plays an important role in the region as it impacts the physiological development of the crops.Increasing trend was noticed for 6 divisions for average minimum temperature and average precipitation during growing season.Northeast and Southeast division showed the strongest increasing trend for average minimum temperature and average precipitation,respectively.East-central division had the most decreasing trend for average maximum temperature.A significant relationship was established between spring wheat yield and climatic parameters as the p-value is lower than 0.05 level which rejects the null hypothesis.The regression model was tested for forecasting accuracy.The percentage deviation of error for the model is approximately±30%in most of the years.

A hybrid model for short-term rainstorm forecasting based on a back-propagation neural network and synoptic diagnosis

Rainstorms are one of the most important types of natural disaster in China.In order to enhance the ability to forecast rainstorms in the short term,this paper explores how to combine a back-propagation neural network(BPNN)with synoptic diagnosis for predicting rainstorms,and analyzes the hit rates of rainstorms for the above two methods using the county of Tianquan as a case study.Results showed that the traditional synoptic diagnosis method still has an important referential meaning for most rainstorm types through synoptic typing and statistics of physical quantities based on historical cases,and the threat score(TS)of rainstorms was more than 0.75.However,the accuracy for two rainstorm types influenced by low-level easterly inverted troughs was less than 40%.The BPNN method efficiently forecasted these two rainstorm types;the TS and equitable threat score(ETS)of rainstorms were 0.80 and 0.79,respectively.The TS and ETS of the hybrid model that combined the BPNN and synoptic diagnosis methods exceeded the forecast score of multi-numerical simulations over the Sichuan Basin without exception.This kind of hybrid model enhanced the forecasting accuracy of rainstorms.The findings of this study provide certain reference value for the future development of refined forecast models with local features.

Synoptic Verification of Precipitation Forecast of Three NWP Models from May to August of 2008 in Liaoning Province

In order to evaluate the precipitation forecast performance of mesoscale numerical model in Northeast China,mesoscale model in Liaoning Province and T213 model,and improve the ability to use their forecast products for forecasters,the synoptic verifications of their 12 h accumulated precipitation forecasts of 3 numerical modes from May to August in 2008 were made on the basis of different systems impacting weather in Liaoning Province.The time limitations were 24,36,48 and 60 h.The verified contents included 6 aspects such as intensity and position of precipitation center,intensity,location,scope and moving velocity of precipitation main body.The results showed that the three models had good forecasting capability for precipitation in Liaoning Province,but the cupacity of each model was obviously different.

Forecasting Emergency Paediatric Asthma Hospital Admissions in Trinidad and Tobago: Development of a Local Model Incorporating the Interactions of Airborne Dust and Pollen Concentrations with Meteorological Parameters and a Time-Lag Factor

Respiratory diseases such as asthma and rhinitis are multifaceted disorders which are exacerbated by various factors including: gender, age, diet, genetic background, biological materials, allergens (pollen and spores), pollutants, meteorological conditions and dust particles. It is hypothesized that, the number of valid physician diagnosed cases of paediatric asthma, which has resulted in emergency room visits in Trinidad can be expressed as a function of the magnitude of pollen counts, particulate matter (PM10), and selected meteorological parameters. These parameters were used to develop a 7-day predictive model for paediatric asthma admittance. The data showed no obvious, strong correlations between paediatric asthma admissions and dust concentrations, and paediatric asthma admissions and pollen concentrations, when considered in isolation or in a linear fashion. However, using polynomial regression analysis, which looked at combinations of interactions, a strong 7-day predictive model for paediatric asthma admissions, was developed. The model was tested against actual data collated during the study period and showed a strong correlation (R2 = 0.85) between the regression model and the actual admissions data.

Interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model

This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on the traditional nonhomogenous discrete grey forecasting model（NDGM）, the interval grey number and its algebra operations are redefined and combined with the NDGM model to construct a new interval grey number sequence prediction approach. The solving principle of the model is analyzed, the new accuracy evaluation indices, i.e. mean absolute percentage error of mean value sequence（MAPEM） and mean percent of interval sequence simulating value set covered（MPSVSC）, are defined and, the procedure of the interval grey number sequence based the NDGM（IG-NDGM） is given out. Finally, a numerical case is used to test the modelling accuracy of the proposed model. Results show that the proposed approach could solve the interval grey number sequence prediction problem and it is much better than the traditional DGM（1,1） model and GM（1,1） model.

The Water-Bearing Numerical Model and Its Operational Forecasting Experiments PartII: The Operational Forecasting Experiments

おhe water-bearing numerical model is undergone all round examinations during the operational forecasting experiments from 1994 to 1996. A lot of difficult problems arising from the model′s water-bearing are successfully resolved in these experiments through developing and using a series of technical measures. The operational forecasting running of the water-bearing numerical model is realized stably and reliably, and satisfactory forecasts are obtained.

A STUDY OF THE INFLUENCE OF MICROPHYSICAL PROCESSES ON TYPHOON NIDA(2016) USING A NEW DOUBLE-MOMENT MICROPHYSICS SCHEME IN THE WEATHER RESEARCH AND FORECASTING MODEL

The basic structure and cloud features of Typhoon Nida(2016) are simulated using a new microphysics scheme(Liuma) within the Weather Research and Forecasting(WRF) model. Typhoon characteristics simulated with the Liuma microphysics scheme are compared with observations and those simulated with a commonly-used microphysics scheme(WSM6). Results show that using different microphysics schemes does not significantly alter the track of the typhoon but does significantly affect the intensity and the cloud structure of the typhoon. Results also show that the vertical distribution of cloud hydrometeors and the horizontal distribution of peripheral rainband are affected by the microphysics scheme. The mixing ratios of rain water and graupel correlate highly with the vertical velocity component and equivalent potential temperature at the typhoon eye-wall region. According to the simulation with WSM 6 scheme,it is likely that the very low typhoon central pressure results from the positive feedback between hydrometeors and typhoon intensity. As the ice-phase hydrometeors are mostly graupel in the Liuma microphysics scheme, further improvement in this aspect is required.

A new grey forecasting model based on BP neural network and Markov chain

A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model（GM（1,1））. Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system＇s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model （GNNM（1,1）） was built. In order to reduce stochastic phenomenon in GNNM（1,1）, the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model（MNNGM（1,1）） was proposed by combining Markov chain with GNNM（1,1）. Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM（1, 1） and GNNM（1,1） with the proposed model, the results indicate that the absolute mean error of MNNGM（1,1） is about 0.4 times of GNNM（1,1） and 0.2 times of GM（I, 1）, and the mean square error of MNNGM（1,1） is about 0.25 times of GNNM（1,1） and 0.1 times of GM（1,1）.