scholarly journals Correlation approach in predictor selection for groundwater level forecasting in areas threatened by water deficits

2021 ◽  
Author(s):  
Joanna Kajewska-Szkudlarek ◽  
Justyna Kubicz ◽  
Ireneusz Kajewski
Keyword(s):  
Groundwater Level ◽  
Mean Squared Error ◽  
Support Vector ◽  
Modern Approach ◽  
Water Deficits ◽  
Input Selection ◽  
Preprocessing Technique ◽  
General Regression

Abstract Reliable long-term groundwater level (GWL) prediction is essential to assess the availability of resources and the risk to drinking water supply in changing climatic and socio-economic conditions, especially in areas with water deficits. The modern approach in this area involves the use of machine learning methods. However, the greatest challenge in these methods lies in the optimization of input selection. The presented research concerns the selection of the best combination of predictors using the Hellwig method. It served as a preprocessing technique before GWL prediction using support vector regression (SVR) and multilayer perceptron (MLP) for three wells in the Greater Poland Province, where the largest water deficits occur, in the period 1975–2014. The results of this method were compared with those of the regression method, general regression model. For the case study under investigation, the Hellwig method found GWL at lags of −1 and −2 months, all precipitation from the current month, and delayed by −1 to −6 months, and past temperature at months −1, −3, −4 and −6 as the most informative input set. Such input led to a model accuracy of 0.003–0.022 for a mean squared error and r2 of >0.8. The results obtained with SVR were slightly better than those with MLP. Moreover, every well required an individual set of predictors, and additional meteorological inputs improved the models’ performance.

scholarly journals A New Integrated Approach for Municipal Landfill Siting Based on Urban Physical Growth Prediction: A Case Study Mashhad Metropolis in Iran

2021 ◽  
Vol 13 (5) ◽  
pp. 949
Author(s):  
Salman Qureshi ◽  
Saman Nadizadeh Shorabeh ◽  
Najmeh Neysani Samany ◽  
Foad Minaei ◽  
Mehdi Homaee ◽  
...  
Keyword(s):  
Urban Growth ◽  
Integrated Approach ◽  
Physical Growth ◽  
Support Vector ◽  
Markov Chain Method ◽  
Physical Expansion ◽  
Hierarchy Process ◽  
The City

Due to irregular and uncontrolled expansion of cities in developing countries, currently operational landfill sites cannot be used in the long-term, as people will be living in proximity to these sites and be exposed to unhygienic circumstances. Hence, this study aims at proposing an integrated approach for determining suitable locations for landfills while considering their physical expansion. The proposed approach utilizes the fuzzy analytical hierarchy process (FAHP) to weigh the sets of identified landfill location criteria. Furthermore, the weighted linear combination (WLC) approach was applied for the elicitation of the proper primary locations. Finally, the support vector machine (SVM) and cellular automation-based Markov chain method were used to predict urban growth. To demonstrate the applicability of the developed approach, it was applied to a case study, namely the city of Mashhad in Iran, where suitable sites for landfills were identified considering the urban growth in different geographical directions for this city by 2048. The proposed approach could be of use for policymakers, urban planners, and other decision-makers to minimize uncertainty arising from long-term resource allocation.

scholarly journals Assessment of Hellwig Method for Predictors’ Selection in Groundwater Level Time Series Forecasting

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 778
Author(s):  
Joanna Kajewska-Szkudlarek ◽  
Wojciech Łyczko
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Mean Squared Error ◽  
Measurement Data ◽  
Treatment Plant ◽  
Water Volume ◽  
Support Vector ◽  
Complex Nature ◽  
Good Prediction ◽  
Prediction Ability

Effective groundwater planning and management should be based on the prediction of available water volume. The complex nature of groundwater systems makes this complicated and requires the use of complex methods. Data-driven models using computational intelligence are becoming increasingly popular in that field. The key issue in predictive modelling is the selection of input variables. Wrocław-Osobowice irrigation fields were a wastewater treatment plant until 2013. The monitoring of groundwater levels is being continued to assess the water relations in that area after the end of their exploitation. The aim of the study was to assess the Hellwig method for predictors’ selection in groundwater level forecasting with support vector regression models. Data covered the daily time series of groundwater level in the period 2015–2019. Obtained models with a root mean squared error (RMSE) of 0.024–0.292 m and r2 of 0.7–0.9 were considered as high quality. Moreover, they showed good prediction ability for high as well as low groundwater values. Additionally, the proposed method is simple, and its implementation only requires access to groundwater level measurement data. It may be useful in groundwater management and planning in terms of actual climate change and threat of water deficits.

scholarly journals Data-driven runoff forecasting for Minjiang River: a case study

2020 ◽  
Vol 20 (6) ◽  
pp. 2284-2295
Author(s):  
Yuqiang Wu ◽  
Qinhui Wang ◽  
Ge Li ◽  
Jidong Li
Keyword(s):  
Optimal Allocation ◽  
Data Driven ◽  
Function Parameter ◽  
Support Vector ◽  
Runoff Forecasting ◽  
Minjiang River ◽  
Svm Model ◽  
Runoff Prediction

Abstract Long-term runoff forecasting has the characteristics of a long forecast period, which can be widely applied in environmental protection, hydropower operation, flood prevention and waterlogging management, water transport management, and optimal allocation of water resources. Many models and methods are currently used for runoff prediction, and data-driven models for runoff prediction are now mainstream methods, but their prediction accuracy cannot meet the needs of production departments. To this end, the present research starts with this method and, based on a support vector machine (SVM), it introduces ant colony optimization (ACO) to optimize its penalty coefficient C, Kernel function parameter g, and insensitivity coefficient p, to construct a data-driven ACO-SVM model. The validity of the method is confirmed by taking the Minjiang River Basin as an example. The results show that the runoff predicted by use of ACO-SVM is more accurate than that of the default parameter SVM and the Bayesian method.

scholarly journals Influence of peat soils on runoff process: case study of Vydra River headwaters, Czechia

Geografie ◽  
2016 ◽  
Vol 121 (2) ◽  
pp. 235-253 ◽  
Author(s):  
Lukáš Vlček ◽  
Jan Kocum ◽  
Bohumír Janský ◽  
Luděk Šefrna ◽  
Šárka Blažková
Keyword(s):  
Groundwater Level ◽  
Weather Conditions ◽  
Peat Bog ◽  
Flood Wave ◽  
Peat Soils ◽  
Rainfall Events ◽  
Forest Coverage

This paper summarizes findings from the hydrological research in the Vydra River headwaters, the Šumava Mts., s-w Czechia, dealing with the hydrological function of local peat soils and their effect on the outflow from the basin. This study represents a part of a long-term research carried out at the Faculty of Science, Charles University in Prague. The paper shows how important it is to study the groundwater level in peat soils and its area in a catchment as well as to predict the outflow in distinct weather conditions. There were chosen four small experimental catchments with different peat and waterlogged forest coverage. Rainfall events were selected in various periods within a year with a varying groundwater level (maximum and minimum) in the peat bog. Within these situations flood wave volumes were calculated and all of them were compared regarding the peat bog extension. The presented research also compares various sources of data about peat soils areas and areas of waterlogged forest.

Caregivers' Perceptions of Speech-Language Pathologist Talk About Child Language and Literacy Disorders

2020 ◽  
Vol 29 (4) ◽  
pp. 2049-2067
Author(s):  
Karmen L. Porter ◽  
Janna B. Oetting ◽  
Loretta Pecchioni
Keyword(s):  
Language And Literacy ◽  
Collective Case Study ◽  
Study Approach ◽  
School Aged Children ◽  
Speech Language Pathologist ◽  
Therapy Goals ◽  
Semistructured Interviews ◽  
Types Of Information

Purpose This study examined caregiver perceptions of their child's language and literacy disorder as influenced by communications with their speech-language pathologist. Method The participants were 12 caregivers of 10 school-aged children with language and literacy disorders. Employing qualitative methods, a collective case study approach was utilized in which the caregiver(s) of each child represented one case. The data came from semistructured interviews, codes emerged directly from the caregivers' responses during the interviews, and multiple coding passes using ATLAS.ti software were made until themes were evident. These themes were then further validated by conducting clinical file reviews and follow-up interviews with the caregivers. Results Caregivers' comments focused on the types of information received or not received, as well as the clarity of the information. This included information regarding their child's diagnosis, the long-term consequences of their child's disorder, and the connection between language and reading. Although caregivers were adept at describing their child's difficulties and therapy goals/objectives, their comments indicated that they struggled to understand their child's disorder in a way that was meaningful to them and their child. Conclusions The findings showed the value caregivers place on receiving clear and timely diagnostic information, as well as the complexity associated with caregivers' understanding of language and literacy disorders. The findings are discussed in terms of changes that could be made in clinical practice to better support children with language and literacy disorders and their families.

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