基于神经网络的东北地区秸秆焚烧预测

选取农作物秸秆露天燃烧严重的东北地区,采用人工神经网络的方法,结合卫星火点和气象数据,开展秸秆露天燃烧预测研究.结果表明：人工神经网络预测模型成功验证了松嫩平原地区2015年10月25日~11月15日的秸秆露天燃烧情况,其准确度为67.1%,经过多次试验,在神经网络建模与验证数据配比为80：20时,预测准确度最高,可达69.7%,同时该模型的稳定性较好.而对不同区域,不同时间段的预测研究表明,人工神经网络较适用于长时间序列的预测.就影响因素而言,相对湿度是影响秸秆露天燃烧的最重要因素.本研究结果可为空气质量模式提供火点预测数据,提高其预报预警能力,为区域联防联控政策的制定提供科技支持.     

小麦产量的遥感动态预估研究──以山东禹城县为例

本文利用现代遥感信息、数理统计和回归分析方法，研究了禹城县试验区的小麦单产与卫星遥感信息（绿度）间的关系，并建立了小麦单产与绿度关系的预估模型；阐述了直接利用遥感宏观信息动态预估小麦产量的可行性和这一方法的应用前景。     

探讨影响赤潮的物理因子及其预报

通过多年对广东沿海港湾赤潮发生机理的探讨 ,归纳出引发赤潮的主要物理因子 ,并深入研究潮汐变化与赤潮形成的关系。作者认为在沿海一些半封闭的海湾 ,在月赤纬偏小期间发生大潮易引发赤潮。大亚湾澳头水域 4～ 5月份大潮后多呈现不正规全日潮 ,易出现赤潮生物高峰期 ,较明显的赤潮从发生至消退的全过程往往跨越一个潮水期。根据实践经验总结 ,作者提出利用潮汐变化规律对大亚湾澳头水域 2 0 0 1年 5月份可能出现的赤潮初始阶段作尝试性预测 ,并在赤潮初始阶段通过对水域的浮游生物优势种和当地天气的变化趋势加强观测、研究 ,希望能对赤潮暴发作出较准确的预报。     

灰色生长曲线与万元产值废水量预测

基于ＧＩＭ（１）灰色线性幂函数曲线，研究开发出ＧＰＭ（１）这一新的灰色生长曲线，并建立了ＧＰＭ（１）简捷的参数辨别方法。研究表明，ＧＰＭ（１）概念明确，结论可信，精度令人满意，比传统的皮尔生长曲线在应用上有着更大的优越性。为具有生长规律的非线性灰色环境系统的分析、预测和决策提供了新途径。     

Famine forecasting; Prices and peasant behaviour in Northern Ethiopia

This paper, completed in January 1984, begins by briefly outlining the contrasting views of price behaviour during the last Ethiopian famine (1972–1974) put forward by Seaman and Holt (1980) and Sen (1981). A hypothesis is developed to account for peasant behaviour and price responses under developing famine conditions. This is then tested with data recently made available. The paper concludes by summarizing the findings and their implications for famine forecasting, and argues that famine conditions in Northern Ethiopia are likely to worsen during 1984.     

提高洪水智能预报中洪峰预报精度方法的研究

针对防洪减灾的实际需要,对如何提高智能网络对洪峰的预报精度问题进行了深入系统的研究,提出了峰值放大修正系数和遗传算法优化网络初始权重相结合的改进算法,历史资料的检验结果表明了这些改进策略的有效性和可靠性.     

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

Probabilistic Flood Inundation Forecasting Using Rating Curve Libraries

One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre‐running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre‐computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting (RCAFF) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods.     

基于数值模式的环境空气质量预报影响因素和改进方法

数值预报是各地开展环境空气质量业务预报的主流预报方法之一。模式预报产品、基础输入资料、外部技术支持、主客观预报偏差和预报命中概率等诸多因素均会不同程度地影响数值模式预报效果,研究探讨了上述因素对数值模式预报效果的影响及其针对性的改进方法,以期降低预报偏差,改善业务预报整体效果,为各地提高业务预报质量提供方法思路和技术参考。     

嫩江水体溶解氧变化规律的混沌研究

针对目前溶解氧研究主要是从生物、化学等方面开展,而对溶解氧自身变化规律的研究很少的特点,以嫩江干流浏园水文站测得的溶解氧质量浓度序列为例,首先进行相空间重构,应用自相关函数法确定出延迟时间、伪邻近点法确定出嵌入维数;然后应用主分量分析(PCA)方法对序列进行分析,表明该序列不是噪声序列而可能是混沌序列,通过计算Kolmogorov熵得出溶解氧质量浓度序列具有混沌特性的结论;最后应用基于关联度的局域加权线性回归预测法对该序列进行了混沌预测研究,得到了比较理想的预测结果,这为应用混沌理论进行溶解氧变化规律的研究提供了依据.