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Development of an open-source software package for watershed modeling with the Hydrological Simulation Program in Fortran

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ENVIRONMENTAL MODELLING & SOFTWARE 2015年 68卷 166-174页

作者： Lampert, D. J. Wu, M. Argonne Natl Lab Lemont IL 60439 USA

The Hydrological Simulation Program in Fortran (HSPF) is used extensively for the assessment of water quantity and water quality issues. Herein, the development of an open-source, cross-platform package for building input files, performing simulations, postprocessing and calibrating HSPF models using the python programming Language is presented. The flexible nature of python opens the door to automated preprocessing and calibration routines, visualization, multiprocessing, and larger-scale model development. The software is applied using python scripts, which provides a flexible mechanism for learning and applying HSPF. An example application of the software was used to build a calibrated HSPF model the Hunting Creek watershed within the Patuxent River Basin, Maryland, USA, which is the example application distributed with the HSPF calibration software package HSPExp. A script of a few hundred lines was used to build a calibrated model comparable to HSPExp in a simulation time of less than two hours. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Watershed modeling python programming Hydrology HSPF

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xINTERPDF: a graphical user interface for analyzing intermolecular pair distribution functions of organic compounds from X-ray total scattering data

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JOURNAL OF APPLIED CRYSTALLOGRAPHY 2018年第5期51卷 1498-1499页

作者： Shi, Chenyang AbbVie Inc Drug Prod Dev 1 North Waukegan Rd N Chicago IL 60064 USA

A new software program, xINTERPDF, that analyzes the intermolecular correlations in organic compounds via measured X-ray total scattering data is described.

关键词： X-ray total scattering pair distribution function python programming graphical user interfaces

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An object-oriented approach toward two-dimensional engineering mechanics

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COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2023年第5期31卷 1372-1397页

作者： Ghosh, Sunavo Kundu, Soumalya Neogy, Sumanta Nandi, Arghya Tata Metaliks Robot Div Kharagpur W Bengal India Jadavpur Univ Dept Mech Engn Kolkata W Bengal India Jadavpur Univ Dept Mech Engn Kolkata 700032 W Bengal India

The present work proposes an object-oriented approach for the solution of problems in two-dimensional engineering mechanics. New classes (in the terminology of object-oriented programming) are proposed and implemented in the popular programming language 'python.' These classes, along with the symbolic computation module of python 'sympy,' enable one to reduce the solution of a large class of problems in undergraduate engineering mechanics to a set of python statements. These statements are definitions of objects as instances of the proposed classes and calling of their relevant methods. If the thought process in the manual solution is represented by a flowchart, the present work can be viewed as a flowchart translator. As all the calculations are automatically performed, the present work relieves the student from the burden and fear of lengthy calculations. Different diagrammatic representations of velocity, acceleration, shear force, and bending moments are included for better physical understanding. It is believed that the proposed simple implementation would make problem-solving and the learning process more enjoyable. Apart from a standard PC, the proposed work runs on a single-board computer (tested in raspberry pi 4) and on an Android mobile device. A student feedback study ascertains the acceptability of the proposed method.

关键词： object-oriented programming python programming undergraduate engineering mechanics

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Development of PROMETHEE-Entropy data mining model for groundwater potentiality modeling: a case study of multifaceted geologic settings in south-western Nigeria

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ACTA GEOPHYSICA 2024年第3期72卷 1957-1984页

作者： Mogaji, Kehinde Anthony Atenidegbe, Olanrewaju Fred Fed Univ Technol Akure Dept Appl Geophys Akure Nigeria Univ Stirling Comp Sci & Math Stirling FK9 4LA Scotland

This work looks at developing an object-driven decision support system (DSS) model with the goal of improving the prediction accuracy of the present expert-driven DSS model in assessing groundwater potentiality. The database of remote sensing, geological, and geophysical information was constructed using the technological efficiency of GIS, data mining, and programming tools. Groundwater potential conditioning factors (GPCF) extracted from the datasets include lithology (Li), hydraulic conductivity (K), lineament density (Ld), transmissivity (T), and transverse resistance (TR) for groundwater potentiality mapping in a typical hard rock multifaceted geologic setting in south-western Nigeria. A python-based entropy approach was used to objectively weight these factors. The weightage findings determined that the greatest and lowest given values for Ld and K were 0.6 and 0.03, respectively. The produced python-based PROMETHEE-Entropy model algorithm was born through combining the weight findings with the python-based PROMETHEE-II method. The groundwater potentiality model (GPM) map of the area was created using the model algorithm's outputs on the gridded raster of GPCF themes. Based on the suggested approach, the validated results of the created GPM maps using the Receiver Operating Characteristic (ROC) curve technique yielded an accuracy of 86%. An object-driven DSS model was created using the approaches that were used. The created object-driven model is a viable alternative to existing approaches in groundwater hydrology and aids in the automation of groundwater resource management in the research region.

关键词： Groundwater potential Geophysics Remote sensing and GIS PROMETHEE-II Entropy python programming

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A New Toolkit for Energy Planning for Isolated Microgrids

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IEEE ACCESS 2023年 11卷 93815-93827页

作者： Polanco, Juan-Fernando Esparza, Alexis Singh, Jaspreet Gonzalez-Rodriguez, Mario Clairand, Jean-Michel Nutter, Robert Dauenhauer, Peter Univ Cent Ecuador Fac Ingn & Ciencias Aplicadas Quito 170122 Ecuador RPS Consulting London England Capgemini Engn F-31700 Blagnac France Community Housing Partners Christiansburg VA 24073 USA Snohomish PUD Everett WA USA

Rural areas in various locations are facing electricity shortages and are compelled to heavily rely on non-renewable and ecologically harmful fossil fuels as their primary source of energy. To address this issue, microgrids were proposed as a solution to provide energy to these areas. An IEEE working group, the SESDC Working Group, was established to investigate the feasibility of implementing isolated microgrids as solutions in these communities. However, it has been identified that a proper software tool for microgrid planning is needed to accurately analyze the optimal microgrid configuration. Thus, a user-friendly and secure web-based system for microgrid planning was proposed. This tool was developed using the Django web development framework and python programming language. It was then validated with users to ensure that the data entered is calculated correctly and produced the expected results.

关键词： Microgrids Optimization Renewable energy sources Planning Costs Software tools Investment Energy management python Django web development framework economic toolkit energy access microgrid planning model-view-controller prefeasibility analysis renewable energy python programming rural electrification software app

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An image-based deep learning model for water turbidity estimation in laboratory conditions

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2023年第1期20卷 149-160页

作者： Feizi, H. Sattari, M. T. Mosaferi, M. Apaydin, H. Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran Ankara Univ Fac Agr Dept Agr Engn Ankara Turkey Tabriz Univ Med Sci Hlth & Environm Res Ctr Tabriz Iran

One of the important parameters in water quality, especially surface water, is the amount of turbidity in the water which can have a harmful effect on irrigation water and drinking water. Existing methods for measuring water turbidity, although relatively simple, are costly due to the need for a turbidity meter. In addition, human intervention or non-calibration of the device can cause errors. Therefore, in this paper, a deep learning method including an image-based convolution neural network is used to estimate water turbidity. In this way, different samples of water turbidity were prepared and photographed in the laboratory and entered the network. The network was implemented using python programming on the Google Colab platform. The results of the proposed system were compared with the results of the Turbidity-Meter. The results showed that the proposed method's performance is good and at best classifies only one sample in the wrong class. This method can detect the water turbidity class with 97.5% accuracy and only 2.5% error due to lack of complete control of environmental factors in imaging. Consequently, having an extensive database of water quality images in different conditions, it is possible to detect water quality with sufficient accuracy only from images and there is no need to spend a lot of money. This method seems to be able to play an important role in areas where water turbidity is important but lacks laboratory facilities.

关键词： Classification Convolutional neural network python programming Water quality

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Weed detecting robot in sugarcane fields using fuzzy real time classifier

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2017年 134卷 160-171页

作者： Sujaritha, M. Annadurai, S. Satheeshkumar, J. Sharan, S. Kowshik Mahesh, L. Sri Krishna Coll Engn & Technol Coimbatore Tamil Nadu India Bharathiar Univ Dept Comp Applicat Coimbatore Tamil Nadu India

The objective of this research work is to propose a weed detecting robotic model for sugarcane fields that uses a fuzzy real time classifier on leaf textures. The differentiation between weed and crop and weed removal are the two challenging tasks for the farmers especially in the Indian sugarcane cultivation scenario. The automatic weed detection and removal becomes a vital task for improving the cost effectiveness and efficiency of the agricultural processes. The detection of weeds by the robotic model employs a Raspberry Pi based control system placed in a moving vehicle. An automated image classification system has been designed which extracts leaf textures and employs a fuzzy real-time classification technique. Morphological operators are applied to extract circular leaf patterns in different scales from the leaf images. An optimal set of features have been identified for the characterization of crops and weeds in sugarcane fields. A weed detecting robotic prototype is designed and developed using a Raspberry Pi micro controller and suitable input output subsystems such as cameras, small light sources and motors with power systems. The prototype's control incorporates the weed detection mechanism using a Raspbian operating system support and python programming. The designed robotic prototype correctly identifies the sugarcane crop among nine different weed species. The system detects weeds with 92.9% accuracy over a processing time of 0.02 s. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Image classification Morphological operators Leaf textures Machine learning Rotation invariance Fuzzy real time classifier Raspbian operating system python programming

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Development of image analysis using python: Relationship between matrix ratio of composite resin and curing temperature

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DENTAL MATERIALS JOURNAL 2020年第4期39卷 648-656页

作者： Hori, Miki Fujimoto, Kotaro Hori, Tadasuke Sekine, Hironao Ueno, Atsuko Kato, Akiko Kawai, Tatsushi Aichi Gakuin Univ Sch Dent Dept Dent Mat Sci Chikusa Ku 1-100 Kusumoto Cho Nagoya Aichi 4648650 Japan Aichi Gakuin Univ Ctr Adv Oral Sci Sch Dent Chikusa Ku 1-100 Kusumoto Cho Nagoya Aichi 4648650 Japan Aichi Gakuin Univ Sch Dent Dept Gerodontol Chikusa Ku 1-100 Kusumoto Cho Nagoya Aichi 4648650 Japan Aichi Gakuin Univ Sch Dent Dept Oral Anat Chikusa Ku 1-100 Kusumoto Cho Nagoya Aichi 4648650 Japan

The aim of this study was to establish a measurement method for filler and matrix in cured resin composite (RC) using python programming and to investigate the correlation between matrix ratio and curing temperature rise. Eight kinds of RCs were used. Backscattered electron images were taken for each cured specimen. Matrix and filler contents were calculated using python programming with the K-means or area segmentation method. Volume measurement methods were assessed for comparison. Heat released during the polymerization reaction was measured. The matrix ratio was calculated without human intervention. Three specimens contained only inorganic filler, and other specimens contained multiple types of fillers. Almost the same values of the matrix ratio were obtained by programming and the volume measurement methods for specimens containing a single type of inorganic filler. Moreover, a strong correlation was found between the matrix ratio obtained by the programming method and curing temperature rise (R=0.9826).

关键词： python programming Composite resin Segmentation analysis Temperature rise Correlation analysis

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Application of GIS-based PROMETHEE data mining technique to geoelectrical-derived parameters for aquifer potentiality assessment in a typical hardrock terrain Southwestern Nigeria

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SUSTAINABLE WATER RESOURCES MANAGEMENT 2022年第2期8卷 1-23页

作者： Mogaji, K. A. Atenidegbe, O. F. Adeyemo, I. A. Akinmulewo, K. P. Fed Univ Technol Akure Dept Appl Geophys Akure Nigeria

Groundwater potentiality assessment is an effective tool for groundwater management. Data mining modeling techniques have been efficacious in this regard. This study explored the potential of a GIS-based PROMETHEE method in the field of groundwater hydrology. The approach was applied to model aquifer potential conditioning factors derived from interpreted geoelectrical parameters. 68 depth sounding (VES) data locations were identified in Ipinsa, a typical multifaceted geologic hardrock terrain. The acquired VES data were quantitatively interpreted to determine the subsurface lithologic parameters in the form of resistivity and thickness. The interpreted results were used to derive the groundwater potential conditioning factors (GPCFs), namely: longitudinal conductance (Lc), transverse resistance (TR), transmissivity (T), reflection coefficient (Rc) and recharge rate (Re). Using the derived geoelectrical-based GPCFs values, the GPCFs themes were produced in the GIS platform. The produced GPCFs themes were multi-critically analyzed using the mechanism of python programming-based Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE-II) data mining model algorithm to produce groundwater potentiality indexing (GPI) map. Furthermore, to compare the performance of the (PROMETHEE-II) data mining model result, multi-criteria decision analysis-analytic hierarchy process (MCDA-AHP) model was applied. The efficiency of the PROMETHEE-II and MCDA-AHP-based GPI maps were evaluated using well data records. The results of the well data correlation with the predictive model maps show regression coefficients of 78% and 75% for PROMETHEE-II and MCDA-AHP data mining models, respectively. These results show that both models have good performance in prediction of groundwater potential zones, with the PROMETHEE-II as a better alternative. These maps and models could be used as future planning tool and part of decision support for decision making for locating appropr

关键词： Groundwater potential PROMETHEE-II Analytic hierarchy process (AHP) python programming

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Parallel cartographic modeling: a methodology for parallelizing spatial data processing

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2016年第12期30卷 2355-2376页

作者： Shook, Eric Hodgson, Michael E. Wang, Shaowen Behzad, Babak Soltani, Kiumars Hiscox, April Ajayakumar, Jayakrishnan Kent State Univ Dept Geog High Performance Comp & GIS HPCGIS Lab Kent OH 44242 USA Univ South Carolina Dept Geog Columbia SC USA Univ Illinois CyberGIS Ctr Adv Digital & Spatial Studies Urbana IL USA Univ Illinois Dept Geog & Geog Informat Sci Urbana IL USA Univ Illinois Natl Ctr Supercomp Applicat Urbana IL USA Univ Illinois Dept Comp Sci Urbana IL USA Univ Illinois Illinois Informat Inst Urbana IL USA

This article establishes a new methodological framework for parallelizing spatial data processing called parallel cartographic modeling, which extends the widely adopted cartographic modeling framework. Parallel cartographic modeling adds a novel component called a Subdomain, which serves as the elemental unit of parallel computation. Four operators are also added to express parallel spatial data processing, namely scheduler, decomposition, executor, and iteration. A parallel cartographic modeling language (PCML) is developed based on the parallel cartographic modeling framework, which is designed for usability, programmability, and scalability. PCML is a domain-specific language implemented in python for the domain of cyberGIS. A key feature of PCML is that it supports automatic parallelization of cartographic modeling scripts;thus, allowing the analyst to develop models in the familiar cartographic modeling language in a python syntax. PCML currently supports more than 70 operations and new operations can be easily implemented in as little as three lines of PCML code. Experimental results using the National Science Foundation-supported Resourcing Open Geospatial Education and Research computational resource demonstrate that PCML efficiently scales to 16 cores and can process gigabytes of spatial data in parallel. PCML is shown to support multiple decomposition strategies, decomposition granularities, and iteration strategies that be generically applied to any operation implemented in PCML.

关键词： Cartographic modeling language map algebra high-performance computing python programming spatial data processing

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