A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble

Dissolved oxygen(DO)is an important indicator of aquaculture,and its accurate forecasting can effectively improve the quality of aquatic products.In this paper,a new DO hybrid forecasting model is proposed that includes three stages:multi-factor analysis,adaptive decomposition,and an optimizationbased ensemble.First,considering the complex factors affecting DO,the grey relational(GR)degree method is used to screen out the environmental factors most closely related to DO.The consideration of multiple factors makes model fusion more effective.Second,the series of DO,water temperature,salinity,and oxygen saturation are decomposed adaptively into sub-series by means of the empirical wavelet transform(EWT)method.Then,five benchmark models are utilized to forecast the sub-series of EWT decomposition.The ensemble weights of these five sub-forecasting models are calculated by particle swarm optimization and gravitational search algorithm(PSOGSA).Finally,a multi-factor ensemble model for DO is obtained by weighted allocation.The performance of the proposed model is verified by timeseries data collected by the pacific islands ocean observing system(PacIOOS)from the WQB04 station at Hilo.The evaluation indicators involved in the experiment include the Nash–Sutcliffe efficiency(NSE),Kling–Gupta efficiency(KGE),mean absolute percent error(MAPE),standard deviation of error(SDE),and coefficient of determination(R^(2)).Example analysis demonstrates that:①The proposed model can obtain excellent DO forecasting results;②the proposed model is superior to other comparison models;and③the forecasting model can be used to analyze the trend of DO and enable managers to make better management decisions.

Multi-factor high-order intuitionistic fuzzy timeseries forecasting model

Fuzzy sets theory cannot describe the neutrality degreeof data, which has largely limited the objectivity of fuzzy time seriesin uncertain data forecasting. With this regard, a multi-factor highorderintuitionistic fuzzy time series forecasting model is built. Inthe new model, a fuzzy clustering algorithm is used to get unequalintervals, and a more objective technique for ascertaining membershipand non-membership functions of the intuitionistic fuzzy setis proposed. On these bases, forecast rules based on multidimensionalintuitionistic fuzzy modus ponens inference are established.Finally, contrast experiments on the daily mean temperature ofBeijing are carried out, which show that the novel model has aclear advantage of improving the forecast accuracy.

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.

Forecasting and Decision-Making of Systematic Theories for Engineering Geology in Environmental Geoscience

The paper discusses the problems of engineering geology in environmental geoscience from several aspects. For natural sciences and social sciences, it deduces essential theory from logistic cycle model, logic mapping and Verhulst model. It had been discovered that these aspects are equal. However, these were the studies of normal effects. We must establish mathematical model to check from contrary course for gray forecasting and decision-making and answer several questions satisfactorily.

A Hybrid Model for the Mid-Long Term Runoff Forecasting by Evolutionary Computation Techniques

The mid-long term hydrology forecasting is one of most challenging problems in hydrological studies. This paper proposes an efficient dynamical system prediction model using evolutionary computation techniques. The new model overcomes some disadvantages of conventional hydrology forecasting ones. The observed data is divided into two parts; the slow 'smooth and steady' data, and the fast 'coarse and fluctuation' data. Under the divide and conquer strategy, the behavior of smooth data is modeled by ordinary differential equations based on evolutionary modeling, and that of the coarse data is modeled using gray correlative forecasting method. Our model is verified on the test data of the mid-long term hydrology forecast in the northeast region of China. The experimental results show that the model is superior to gray system prediction model (GSPM).

Determination on Microclimatic Conditions at Vines upon Development on Gray Mold (<i>Botrytis cinerea</i>)

One of the most important plant diseases in viticulture is gray mold caused by Botrytis cinerea Pers. Fr., the anamorph of an ascomycete fungus (Botryotinia fuckeliana Whetzel). Locality Smilica, Kavadarci, Republic of North Macedonia, was the place where experimental fields with white varieties Smedervka and Zilavka were continuously observed. Working hypothesis was to follow development of the disease after increasing glucose over 11% until the time of the grape harvest, and microclimate was monitored at the same time. In both white varieties Smederevka and Zilavka on the control variants weren’t used botricide treatments to distinguish between the variants that were conventionally treated against B. cinerea. The aim of the research was to determine how microclimatic conditions affect the development of B. cinerea and consequently to create forecasting model for gray mold. The forecasting model for B. cinerea is based on relationship between temperature and humidity in the vines’ canopies. The aim of the research is to prevent development of B. cinerea and consequently reduce the number of chemical treatments.

抽油机井躺井的区块预测方法(英文)

An exact forecast of the failures of a sucker rod-pumped well in a production area means much for an oilfield’s operation budget, operational arrangement and production plan. In this paper, according to the characteristics of failed sucker rod-pumped well randomness and strong outburst, with the gray GM (1,1) forecast model and the Markov forecast model combined, gray GM (1,1) forecast model is utilized to handle the primary data of an oilfield, and Markov forecast model is utilized to calculate the state transfer probability of forecast value. Then, the gray Markov forecast model considering the influence of randomness factors is formed. Field results prove that the calculation precision of this method is higher and the practicability is greater.

1991—1998年世界竞走运动发展的状况分析及21世纪展望

运用文献资料、常规数理统计、灰色建模等研究方法 ,对 1991— 1998年世界竞走运动的发展状况进行了分析表明 :1995年底竞走定义的修改并未对世界竞走运动的发展产生不良影响 .展望今后世界竞走运动发展的新思路 :充分利用竞走运动的特殊性和人体生物力学界限标准及比赛中裁判员的判罚界限标准进行训练和比赛 ;改善和提高训练、比赛的方法、手段 ,特别是高原训练的方法、手段 ;完善赛前运动员运动能力的控制和比赛中走动节奏的控制 ;更加重视科学选材及心理训练和恢复训练等都将是今后世界竞走运动发展的趋势 .

Short-term Load Forecasting of Regional Distribution Network Based on Generalized Regression Neural Network Optimized by Grey Wolf Optimization Algorithm

Short-term load forecasting of regional distribution network is the key to the economic operation of smart distribution systems,which not only requires high accuracy and fast calculation speed,but also has a diversity of influential factors and strong randomness.This paper proposes a short-term load forecasting model for regional distribution network combining the maximum information coefficient,factor analysis,gray wolf optimization,and generalized regression neural network(MIC-FA-GWO-GRNN).To screen and decrease the dimension of the multiple-input features of the short-term load forecasting model,MIC is first used to quantify the non-linear correlation between the load and input features,and to eliminate the ineffective features,and then FA is used to reduce the dimension of the screened input features on the premise of preserving the main information of input features.After that the high-precision short-term丨oad forecasting based on GWO-GRNN model is realized.GRNN is used to regressively analyze the input features after screening and dimension reduction,and the parameter of GRNN is optimized by using the GWO,which has strong global searching ability and fast convergence.Finally a case study of a regional distribution network in Tianjin,China verifies the accuracy and applicability of the proposed forecasting model.

战略实施研究的新成果——评《技术转移学》

在日益重视软科学研究的今天，有关战略研究、决策研究的论著不少，有的已形成独立的学科，但是对战略决策如何实施的研究，尚未引起关注，更未形成独立学科。由福建科技出版社出版的《技术转移学》（曾德聪、仲长荣著），作为一部探讨科教兴国战略实施的专著，对战略实施...