Prediction of composite suitability index for physical habitat simulations using the ANFIS method

A physical habitat simulation is a useful tool for assessing the impact of river development or restoration on river ecosystem. Conventional methods of physical habitat simulation use the habitat suitability index models and their success depends largely on how well the model reflects monitoring data. One of preferred habitat suitability index models is habitat suitability curves, which are normally constructed based on monitoring data. However, these curves can easily be affected by the subjective opinion of the expert. This study introduces the ANFIS method for predicting the composite suitability index for use in physical habitat simulations. The ANFIS method is a hybrid type of artificial intelligence technique that combines the artificial neural network and fuzzy logic. The method is known to be a powerful approach especially for developing nonlinear relationships between input and output datasets.In this study, the ANFIS method was used to predict the composite suitability index for the physical habitat simulation of a 2.5 km long reach of the Dal River in Korea. Zacco platypus was chosen as the target fish of the study area. A 2D hydraulic simulation was performed, and the hydraulic model was validated by comparing the measured and predicted water surface elevations. The distribution of the composite suitability index predicted by the ANFIS model was compared with that using the habitat suitability curves. The comparisons reveal that the two distributions are similar for various flows. In addition, the distribution of the composite suitability index of the Dal River is computed by the ANFIS method using monitoring data for the other watersheds, namely the Hongcheon River, the Geum River, and the Chogang Stream. The monitoring data for the Chogang Stream, correlation pattern of which was the most similar to that of the Dal River, yielded the distribution of the composite suitability index, which was very close to that obtained using data for the Dal River. This is also supported by the mean absolute percentage error for the difference in the weighted usable areas.     

Automated coronal hole segmentation from Solar EUV Images using the watershed transform

Region of interest segmentation in solar images is the subject of frequent research in solar physics. This study outlines watershed by immersion segmentation to identify coronal hole areas in solar images acquired using the Extreme UV Imaging Telescope (EIT). Solutions presented here produce highly accurate segmentation results of coronal holes of irregular shape, and what is more, they do so for images representing varied solar activity, recorded in different years and months. In addition, the solutions presented here make all the methods used operate very quickly. These methods include: the preprocessing step before the watershed segmentation, the watershed segmentation itself, and also the postprocessing of solar images after the watershed segmentation. The mean duration of the entire segmentation process of solar images amounts to 342 ms for a single coronal hole, without the parallel implementation of the methods used. The experiments were carried out on a computer with an Intel Core i7 CPU @ 2 GHz and 4 GB RAM. After the seed point is identified inside the coronal hole, the segmentation runs automatically.     

Prediction of sediment,particulate nutrient and dissolved nutrient concentrations in a dry tropical river to provide input to a mechanistic coastal water quality model

A Generalised Additive Modelling (GAM) approach is applied to prediction of both particulate and dissolved nutrient concentrations in a wet-tropical river (the Fitzroy River, Queensland, Australia). In addition to covariant terms considered in previous work (i.e. flow, discounted flow and a rising-falling limb term), we considered several new potential covariates: meteorological and hydrological variables that are routinely monitored, available in near-real time, and were considered to have potential predictive power. Of the additional terms considered, only flows from three tributaries of the Fitzroy River (namely, the Nogoa, Comet and Isaac Rivers) were found to significantly improve the model. Inclusion of one or more of these additional flow terms greatly improved results for dissolved nitrogen and dissolved phosphorus concentrations, which were not otherwise amenable to prediction. In particular, the Nogoa sub-catchment, dominated by pasture for cattle, was found to be important in determining dissolved inorganic nitrogen and phosphorus concentrations reaching the river mouth. This insight may direct further research, including future refinement of processed-based catchment models. The GAMs described here are used to provide near real-time river boundary conditions for a complex coupled hydrodynamic and biogeochemical model of the Great Barrier Reef Lagoon, and can be coupled with a forecasting hydrological model to allow integrated forecasting simulations of the catchment to coast system.     

A web-based software tool for participatory optimization of conservation practices in watersheds

WRESTORE (Watershed Restoration Using Spatio-Temporal Optimization of Resources) is a web-based, participatory planning tool that can be used to engage with watershed stakeholder communities, and involve them in using science-based, human-guided, interactive simulation–optimization methods for designing potential conservation practices on their landscape. The underlying optimization algorithms, process simulation models, and interfaces allow users to not only spatially optimize the locations and types of new conservation practices based on quantifiable goals estimated by the dynamic simulation models, but also to include their personal subjective and/or unquantifiable criteria in the location and design of these practices. In this paper, we describe the software, interfaces, and architecture of WRESTORE, provide scenarios for implementing the WRESTORE tool in a watershed community's planning process, and discuss considerations for future developments.     

The new era of the livestock production in Mongolia: Consequences on streams of the Great Lakes Depression

Mongolia, a landlocked country of the Central Asian plateau, is experiencing a significant modification of herding practices coupled with an increase in livestock numbers. These modifications lead to increasing impacts of grazing on the Mongolian steppes with major consequences on the waterbodies. We researched the impacts of grazing intensity on the streams of the Great Lakes Depression in northwestern Mongolia. We assessed the level of watershed and stream bank erosion and the type of vegetation structure. We calculated the livestock densities per watershed and linked them to the stream water discharge through a new metric (I_CU). I_CU was created as a function of cattle unit density and water discharge, having water discharge at a stream section reflecting its location in the drainage and therefore accounting for the surface area drained upstream. We measured also the major nutrients in the stream water and researched the causalities between the grazing and the impairment of watersheds and streams. Our results suggest that the increase of livestock numbers is reaching beyond the grassland and affecting the stream ecosystem. Two major impacts were highlighted by this study, 1) the extensive watershed and stream bank erosion and 2) the increase in concentration of suspended particles and orthophosphate in stream systems. When compared with past values from literature, our results show recent eutrophication of the streams compared to the pre-liberalization of the herding activity in Mongolia (before 1991). Consequently the continued uncontrolled increase of livestock numbers could threaten the conservation of the Mongolian waterbodies, with notable consequences on the life of the nomadic population of the Central Asian Plateau.     

Reconstructing the microstructure of polyimide–silicalite mixed‐matrix membranes and their particle connectivity using FIB‐SEM tomography

Properties of a composite material made of a continuous matrix and particles often depend on microscopic details, such as contacts between particles. Focusing on processing raw focused‐ion beam scanning electron microscope (FIB‐SEM) tomography data, we reconstructed three mixed‐matrix membrane samples made of 6FDA‐ODA polyimide and silicalite‐1 particles. In the first step of image processing, backscattered electron (BSE) and secondary electron (SE) signals were mixed in a ratio that was expected to obtain a segmented 3D image with a realistic volume fraction of silicalite‐1. Second, after spatial alignment of the stacked FIB‐SEM data, the 3D image was smoothed using adaptive median and anisotropic nonlinear diffusion filters. Third, the image was segmented using the power watershed method coupled with a seeding algorithm based on geodesic reconstruction from the markers. If the resulting volume fraction did not match the target value quantified by chemical analysis of the sample, the BSE and SE signals were mixed in another ratio and the procedure was repeated until the target volume fraction was achieved. Otherwise, the segmented 3D image (replica) was accepted and its microstructure was thoroughly characterized with special attention paid to connectivity of the silicalite phase. In terms of the phase connectivity, Monte Carlo simulations based on the pure‐phase permeability values enabled us to calculate the effective permeability tensor, the main diagonal elements of which were compared with the experimental permeability. In line with the hypothesis proposed in our recent paper (?apek, P. et al. (2014) Comput. Mater. Sci. 89 , 142–156), the results confirmed that the existence of particle clusters was a key microstructural feature determining effective permeability.     

降雨输入对青龙河流域HSPF模型模拟结果的影响

为评价降雨输入对青龙河流域BASINs/HSPF模型模拟结果的影响,改善HSPF模型模拟精度,应用趋势成分建模、周期成分建模、相依随机建模和独立随机建模（白噪声）等随机建模程序和蒙特卡洛计算机模拟方法,获得200组降雨随机模拟序列,分别作为青龙河流域BASINs/HSPF模型的输入,以Nash-Sutcliffe效率系数（E_NS）作为模型模拟效果的评价标准,获得如下结论:采用趋势成分、周期成分与ARMA建模以及正态随机模拟获得降雨随机模拟序列是量化降雨输入随机性的可行方法;在模型参数优化的条件下,降雨随机模拟序列HSPF模拟E_NS值的变化区间为[71.09%,74.96%],波动幅度达3.87%,表明降雨输入随机性对于HSPF模拟结果具有显著影响;当考虑每年的日降雨量极大值时,E_NS值变化区间为[75.35%,78.81%],波动幅度达3.46%,且结果均优于没有考虑降雨极大值点的降雨随机模拟序列,表明降雨时间序列的极大值点对于HSPF模拟效果具有显著影响;随着降雨时间序列中所考虑极大值点数量的逐渐增多,HSPF模拟效果出现下降趋势,表明HSPF模拟应特别关注若干最大极值点的影响;降雨输入的不确定性是HSPF模型模拟结果不确定性的重要来源,改善HSPF模拟效果需要考虑降雨时间序列随机性和极值点因素的影响.本研究可为量化降雨输入对HSPF模型模拟的影响以及HSPF模拟的降雨情景优选提供借鉴.     

数据规则化技术在焉耆盆地老资料处理中的应用

焉耆盆地种马场构造带三维地震资料受地震采集面元、地震采集方位等不同因素的影响,部分地区地震资料存在空道或缺道,偏移距不均衡。整个工区覆盖次数差异较大,叠前道集振幅不均衡,造成偏移划弧、偏移噪声大,不利于叠前偏移成像。应用GEOVATION公司的数据规则化技术,有效地解决了偏移距分布不均匀的问题,提高了资料的信噪比,在焉耆盆地马1井地区三维地震老资料重新处理中取得了较好的效果。     

Improved 3D image segmentation for X-ray tomographic analysis of packed particle beds

Although High Resolution X-ray Micro Tomography (HRXMT) has been developed in the past years for the 3D analysis of multiphase mineral particles in packed particle beds, image analysis of fine and/or high-density/high atomic number particles has been limited by existing segmentation algorithms. In this regard, a feature-based segmentation algorithm has been developed and demonstrated to provide a more accurate image processing method for the analysis of such multiphase particle populations. Based on this improved segmentation algorithm, image analyses of packed particle bed samples were compared to segmentation by traditional 3D watershed segmentation. Also, calculation of particle number using optical microscopy, together with a digital camera, was accomplished to validate feature-based segmentation. Detailed procedures and results for sample preparation, image analysis and validation are presented and discussed.     

Development of a knowledge library for automated watershed modeling

In this work, we develop a library of components for building semi-distributed watershed models. The library incorporates basic modeling knowledge that allows us to adequately model different water fluxes and nutrient loadings on a watershed scale. It is written in a formalism compliant with the equation discovery tool ProBMoT, which can automatically construct watershed models from the components in the library, given a conceptual model specification and measured data. We apply the proposed modeling methodology to the Ribeira da Foupana catchment to extract a set of viable hydrological models. By specifying the conceptual model and using the knowledge library, two different hydrological models are generated. Both models are automatically calibrated against measurements and the model with the lower root mean squared error (RMSE) value is selected as an appropriate hydrological model for the selected study area.