scholarly journals Dam Site Suitability Mapping and Analysis Using an Integrated GIS and Machine Learning Approach

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1880 ◽  
Author(s):  
Al-Ruzouq ◽  
Shanableh ◽  
Yilmaz ◽  
Idris ◽  
Mukherjee ◽  
...  
Keyword(s):  
Machine Learning ◽  
United Arab Emirates ◽  
Curve Number ◽  
Influential Factors ◽  
Site Suitability ◽  
Factors Affecting ◽  
Thematic Layers ◽  
Dam Site ◽  
Stream Density ◽  
Total Dissolved Solid

: Meeting water demands is a critical pillar for sustaining normal human living standards, industry evolution and agricultural growth. The main obstacles for developing countries in arid regions include unplanned urbanisation and limited water resources. Locating and constructing dams is a strategic priority of countries to preserve and store water. Recent advances in remote sensing, geographic information system (GIS), and machine learning (ML) techniques provide valuable tools for producing a dam site suitability map (DSSM). In this research, a hybrid GIS decision-making technique supported by an ML algorithm was developed to identify the most appropriate location to construct a new dam for Sharjah, one of the major cities in the United Arab Emirates. Nine thematic layers have been considered to prepare the DSSM, including precipitation, drainage stream density, geomorphology, geology, curve number, total dissolved solid elevation, slope and major fracture. The weights of the thematic layers were determined through the analytical hierarchy process supported by several ML techniques, where the best attempted ML technique was the random forest method, with an accuracy of 76%. Precipitation and drainage stream density were the most influential factors affecting the DSSM. The developed DSSM was validated using existing dams across the study area, where the DSSM provides an accuracy of 83% for dams located in the high and moderate zones. Three major sites were identified as suitable locations for constructing new dams in Sharjah. The approach adopted in this study can be applied for any other location globally to identify potential dam construction sites.

scholarly journals Factors and Predictors of Length of stay in Offenders diagnosed with schizophrenia- A Machine-learning-based approach

2020 ◽  
Author(s):  
Johannes Kirchebner ◽  
Moritz Günther ◽  
Martina Sonnweber ◽  
Alice King ◽  
Steffen Lau
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Learning Algorithms ◽  
Evaluation Criteria ◽  
Selection Procedure ◽  
Legal Aspects ◽  
Influential Factors ◽  
Machine Learning Algorithms ◽  
Forward Selection ◽  
Factors Affecting

Abstract Background: Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clinical concerns. Due to the confounded nature of factors affecting length of stay of psychiatric offender patients, prior research has called for the application of a new statistical methodology better accommodating this data structure. The present study attempts to investigate factors contributing to long-term hospitalization of schizophrenic offenders referred to a Swiss forensic institution, using machine learning algorithms that are better suited than conventional methods to detect nonlinear dependencies between variables. Methods: In this retrospective file and registry study, multidisciplinary notes of 143 schizophrenic offenders were reviewed using a structured protocol on patients’ characteristics, criminal and medical history and course of treatment. Via a forward selection procedure, the most influential factors for length of stay were preselected. Machine learning algorithms then identified the most efficient model for predicting length-of-stay. Results: Two factors have been identified as being particularly influential for a prolonged forensic hospital stay, both of which are related to aspects of the index offense, namely (attempted) homicide and the extent of the victim's injury. The results are discussed in light of previous research on this topic. Conclusions: In this study, length of stay was determined by legal considerations, but not by factors that can be influenced therapeutically. Results emphasize that forensic risk assessments should be based on different evaluation criteria and not merely on legal aspects.

scholarly journals Factors and predictors of length of stay in offenders diagnosed with schizophrenia- A machine-learning-based approach

2020 ◽  
Author(s):  
Johannes Kirchebner ◽  
Moritz Günther ◽  
Martina Sonnweber ◽  
Alice King ◽  
Steffen Lau
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Learning Algorithms ◽  
Evaluation Criteria ◽  
Selection Procedure ◽  
Legal Aspects ◽  
Influential Factors ◽  
Machine Learning Algorithms ◽  
Forward Selection ◽  
Factors Affecting

Abstract Background: Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clinical concerns. Due to the confounded nature of factors affecting length of stay of psychiatric offender patients, prior research has called for the application of a new statistical methodology better accommodating this data structure. The present study attempts to investigate factors contributing to long-term hospitalization of schizophrenic offenders referred to a Swiss forensic institution, using machine learning algorithms that are better suited than conventional methods to detect nonlinear dependencies between variables. Methods: In this retrospective file and registry study, multidisciplinary notes of 143 schizophrenic offenders were reviewed using a structured protocol on patients’ characteristics, criminal and medical history and course of treatment. Via a forward selection procedure, the most influential factors for length of stay were preselected. Machine learning algorithms then identified the most efficient model for predicting length-of-stay. Results: Two factors have been identified as being particularly influential for a prolonged forensic hospital stay, both of which are related to aspects of the index offense, namely (attempted) homicide and the extent of the victim's injury. The results are discussed in light of previous research on this topic. Conclusions: In this study, length of stay was determined by legal considerations, but not by factors that can be influenced therapeutically. Results emphasize that forensic risk assessments should be based on different evaluation criteria and not merely on legal aspects.

INFORMATION SUPPORT FOR MAKING MANAGEMENT DECISIONS IN THE FIELD OF PREVENTION OF EYE DISEASES AT STUDENTS BASED ON MULTI-VARIANT SIMULATION AND FORECASTING

2020 ◽  
pp. 147-153
Author(s):  
Юлия Владимировна Татаркова ◽  
Татьяна Николаевна Петрова ◽  
Олег Валериевич Судаков ◽  
Александр Юрьевич Гончаров ◽  
Ольга Николаевна Крюкова
Keyword(s):  
Preventive Measures ◽  
Influential Factors ◽  
Eye Diseases ◽  
Information Support ◽  
Factor Space ◽  
Factors Affecting ◽  
Eye Fatigue ◽  
Modeling And Forecasting ◽  
Medical Examinations

В настоящей статье представлен обзор основных решений, доступных сегодня для формирования как краткосрочных, так и долгосрочных проекций заболеваемости болезней глаза и его придаточного аппарата в студенческой среде. С другой стороны, существует ряд проблем, связанных с многообразием факторов, влияющих на заболеваемость, статистической необоснованностью и противоречивостью имеющихся результатов анализа данных. Представлены результаты математического моделирования зависимости показателя заболеваемости от наиболее влиятельных факторов образовательной и социальной среды. Перечислены важнейшие направления разработки математических моделей распространения заболеваемости. С помощью разработанного программного комплекса проведена серия вычислительных экспериментов по оценке и прогнозированию заболеваемости обучающихся в вузах разного профиля. Показана эффективность применения методики многовариантного моделирования и прогнозирования, указаны их ограничения и возможности практического применения. По расположению обобщенной области благоприятного прогноза в факторном пространстве можно определить время воздействия неблагоприятных для зрения факторов, которое должно составлять не более 10 ... 11 часов в сутки, количество профилактических мероприятий должно составлять не менее 3 ... 4. При этом риск развития миопии составит не более 0,4, вероятность усталости глаз за компьютером составит не более 0,4, вероятность дискомфорта глаз на занятиях составит не более 0,15. Исходя из характера прогноза, определяется длительность диспансерного наблюдения, а также потребность профилактических мероприятий по устранению или ослаблению действия неблагоприятно влияющих социально-гигиенических и медико-биологических факторов конкретного больного. Использование прогностической матрицы в практическом здравоохранении позволяет существенно улучшить работу по профилактике офтальмологической заболеваемости и является одним из эффективных мероприятий диспансеризации студенческой молодежи, так как дает возможность выделить из числа обучающихся группу с высоким риском неблагоприятного исхода заболевания This article provides an overview of the main solutions available today for the formation of both short-term and long-term projections of the incidence of eye diseases and its adnexa in the student environment. On the other hand, there are a number of problems associated with a variety of factors affecting the incidence, statistical unreasonability and inconsistency of the available data analysis results. The results of mathematical modeling of the dependence of the incidence rate on the most influential factors of the educational and social environment are presented. The most important areas of developing mathematical models for the spread of morbidity are listed. With the help of the developed software package, a series of computational experiments was carried out to assess and predict the incidence of students in universities of various profiles. The effectiveness of the application of multivariate modeling and forecasting methods is shown, their limitations and practical application possibilities are indicated. By the location of the generalized region of favorable prognosis in the factor space, it is possible to determine the exposure time of factors unfavorable for vision, which should be no more than 10 ... 11 hours a day, the number of preventive measures should be at least 3 ... 4. At the same time, the risk of development myopia will be no more than 0.4, the probability of eye fatigue at the computer will be no more than 0.4, the likelihood of eye discomfort in the classroom will be no more than 0.15. Based on the nature of the forecast, the duration of the follow-up observation is determined, as well as the need for preventive measures to eliminate or weaken the action of adverse social, hygienic and biomedical factors of a particular patient. The use of the prognostic matrix in practical health care can significantly improve the work on the prevention of ophthalmic morbidity and is one of the effective medical examinations for students, since it makes it possible to distinguish among the students a group with a high risk of an unfavorable outcome of the disease

scholarly journals Integrating GIS-Based MCDA Techniques and the SCS-CN Method for Identifying Potential Zones for Rainwater Harvesting in a Semi-Arid Area

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 704
Author(s):  
Hussein Al-Ghobari ◽  
Ahmed Z. Dewidar
Keyword(s):  
Water Conservation ◽  
Surface Runoff ◽  
Rainwater Harvesting ◽  
Global Climate ◽  
Remote Sensing Data ◽  
Curve Number ◽  
Drainage Density ◽  
Promising Alternative ◽  
Thematic Layers ◽  
Scs Cn

An increasing scarcity of water, as well as rapid global climate change, requires more effective water conservation alternatives. One promising alternative is rainwater harvesting (RWH). Nevertheless, the evaluation of RWH potential together with the selection of appropriate sites for RWH structures is significantly difficult for the water managers. This study deals with this difficulty by identifying RWH potential areas and sites for RWH structures utilizing geospatial and multi-criteria decision analysis (MCDA) techniques. The conventional data and remote sensing data were employed to set up needed thematic layers using ArcGIS software. The soil conservation service curve number (SCS-CN) method was used to determine surface runoff, centered on which yearly runoff potential map was produced in the ArcGIS environment. Thematic layers such as drainage density, slope, land use/cover, and runoff were allotted appropriate weights to produced RWH potential areas and zones appropriate for RWH structures maps of the study location. Results analysis revealed that the outcomes of the spatial allocation of yearly surface runoff depth ranging from 83 to 295 mm. Moreover, RWH potential areas results showed that the study areas can be categorized into three RWH potential areas: (a) low suitability, (b) medium suitability, and (c) high suitability. Nearly 40% of the watershed zone falls within medium and high suitability RWH potential areas. It is deduced that the integrated MCDA and geospatial techniques provide a valuable and formidable resource for the strategizing of RWH within the study zones.

scholarly journals On factors affecting surface free energy of carbon black for reinforcing rubber

2020 ◽  
Vol 9 (1) ◽  
pp. 170-181 ◽  
Author(s):  
Shangyong Zhang ◽  
Ruipeng Zhong ◽  
Ruoyu Hong ◽  
David Hui
Keyword(s):  
Free Energy ◽  
Carbon Black ◽  
Surface Free Energy ◽  
Surface Activity ◽  
Gas Flow ◽  
Influential Factors ◽  
Chromatographic Column ◽  
Factors Affecting ◽  
Carrier Gas Flow

AbstractThe surface activity of carbon black (CB) is an important factor affecting the reinforcement of rubber. The quantitative determination of the surface activity (surface free energy) of CB is of great significance. A simplified formula is obtained to determine the free energy of CB surface through theoretical analysis and mathematical derivation. The surface free energy for four kinds of industrial CBs were measured by inverse gas chromatography, and the influential factors were studied. The results showed that the aging time of the chromatographic column plays an important role in accurate measurement of the surface free energy of CB, in comparison with the influences from the inlet pressure and carrier gas flow rate of the chromatographic column filled with CB. Several kinds of industrial CB were treated at high temperature, and the surface free energy of CB had a significant increase. With the increase of surface free energy, the maximum torque was decreased significantly, the elongation at break tended to increase, the heat generation of vulcanizates was increased, and the wear resistance was decreased.

scholarly journals Health-Related Quality of Life among Cancer Survivors Depending on the Occupational Status

2021 ◽  
Vol 18 (7) ◽  
pp. 3803
Author(s):  
Kisook Kim ◽  
Hyohyeon Yoon
Keyword(s):  
Quality Of Life ◽  
Depressive Symptoms ◽  
Cancer Survivors ◽  
Occupational Status ◽  
Influential Factors ◽  
Health Related Quality ◽  
Factors Affecting ◽  
Related Quality ◽  
Health Related

The study aimed to identify and compare the factors affecting health-related quality of life (HRQoL) depending on the occupational status of cancer survivors. This study was a secondary data analysis from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2014 to 2018. Hierarchical multivariate linear regression was used to investigate the factors affecting the HRQoL of each group. Non-working cancer survivors had significantly lower HRQoL than working cancer survivors (p < 0.001). A hierarchical multiple regression model showed that demographic, health-related, and psychological characteristics explained 62.0% of non-working cancer survivors’ HRQoL (F = 4.29, p < 0.001). Among the input variables, health-related characteristics were the most influential factors (ΔR2 = 0.274, F = 9.84, p < 0.001). For working cancer survivors, health-related characteristics were the only variable that was statistically associated with HRQoL (F = 5.556, p < 0.001). It is important to enhance physical activities and manage the chronic disease to improve the HRQoL of working cancer survivors. Further, managing health-related characteristics, including depressive symptoms and suicidal ideation, is necessary for non-working cancer survivors. Regarding working survivors, psychological factors such as depressive symptoms and suicidal tendencies did not affect HRQoL. Therefore, an early and effective return to work program should be developed for the improvement of their HRQoL.

scholarly journals Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 601
Author(s):  
Nelson K. Dumakor-Dupey ◽  
Sampurna Arya ◽  
Ankit Jha
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Academic Research ◽  
Empirical Models ◽  
Rock Breakage ◽  
Environmental Implications ◽  
Learning Methods ◽  
Factors Affecting ◽  
Impact Prediction ◽  
Machine Learning Methods

Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and heave, blasting operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, and noise, that have significant operational and environmental implications on mining activities. Consequently, blast impact studies are conducted to determine an optimum blast design that can maximize the desirable impacts and minimize the undesirable ones. To achieve this objective, several blast impact estimation empirical models have been developed. However, despite being the industry benchmark, empirical model results are based on a limited number of factors affecting the outcomes of a blast. As a result, modern-day researchers are employing machine learning (ML) techniques for blast impact prediction. The ML approach can incorporate several factors affecting the outcomes of a blast, and therefore, it is preferred over empirical and other statistical methods. This paper reviews the various blast impacts and their prediction models with a focus on empirical and machine learning methods. The details of the prediction methods for various blast impacts—including their applications, advantages, and limitations—are discussed. The literature reveals that the machine learning methods are better predictors compared to the empirical models. However, we observed that presently these ML models are mainly applied in academic research.

An Experimental Study of Spammer Detection on Chinese Microblogs

2020 ◽  
Vol 30 (11n12) ◽  
pp. 1759-1777
Author(s):  
Jialing Liang ◽  
Peiquan Jin ◽  
Lin Mu ◽  
Jie Zhao
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
User Behavior ◽  
Real Data ◽  
User Profile ◽  
Data Set ◽  
Sina Weibo ◽  
Factors Affecting ◽  
The Government ◽  
Hot Event

With the development of Web 2.0, social media such as Twitter and Sina Weibo have become an essential platform for disseminating hot events. Simultaneously, due to the free policy of microblogging services, users can post user-generated content freely on microblogging platforms. Accordingly, more and more hot events on microblogging platforms have been labeled as spammers. Spammers will not only hurt the healthy development of social media but also introduce many economic and social problems. Therefore, the government and enterprises must distinguish whether a hot event on microblogging platforms is a spammer or is a naturally-developing event. In this paper, we focus on the hot event list on Sina Weibo and collect the relevant microblogs of each hot event to study the detecting methods of spammers. Notably, we develop an integral feature set consisting of user profile, user behavior, and user relationships to reflect various factors affecting the detection of spammers. Then, we employ typical machine learning methods to conduct extensive experiments on detecting spammers. We use a real data set crawled from the most prominent Chinese microblogging platform, Sina Weibo, and evaluate the performance of 10 machine learning models with five sampling methods. The results in terms of various metrics show that the Random Forest model and the over-sampling method achieve the best accuracy in detecting spammers and non-spammers.

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