scholarly journals Groundwater Modeling with Machine Learning Techniques: Ljubljana polje Aquifer

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 697 ◽  
Author(s):  
Klemen Kenda ◽  
Matej Čerin ◽  
Mark Bogataj ◽  
Matej Senožetnik ◽  
Kristina Klemen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Groundwater Level ◽  
Groundwater Modeling ◽  
Machine Learning Techniques ◽  
Physical Parameters ◽  
Strongly Correlated ◽  
Groundwater Levels ◽  
Groundwater Level Fluctuations ◽  
Decision Making Processes ◽  
Input Variables

In this study a thorough analysis is conducted concerning the prediction of groundwater levels of Ljubljana polje aquifer. Machine learning methodologies are implemented using strongly correlated physical parameters as input variables. The results show that data-driven modelling approaches can perform sufficiently well in predicting groundwater level changes. Different evaluation metrics confirm and highlight the capability of these models to catch the trend of groundwater level fluctuations. Despite the overall adequate performance, further investigation is needed towards improving their accuracy in order to be comprised in decision making processes.

scholarly journals Intelligent Machine Learning: Tailor-Making Macromolecules

Polymers ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 579 ◽  
Author(s):  
Yousef Mohammadi ◽  
Mohammad Saeb ◽  
Alexander Penlidis ◽  
Esmaiel Jabbari ◽  
Florian J. Stadler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Kinetic Monte Carlo ◽  
Engineering Problem ◽  
Optimization Techniques ◽  
Machine Learning Techniques ◽  
Coordination Polymerization ◽  
Intelligent Modeling ◽  
Learning Techniques ◽  
Input Variables ◽  
Architectural Characteristics

Nowadays, polymer reaction engineers seek robust and effective tools to synthesize complex macromolecules with well-defined and desirable microstructural and architectural characteristics. Over the past few decades, several promising approaches, such as controlled living (co)polymerization systems and chain-shuttling reactions have been proposed and widely applied to synthesize rather complex macromolecules with controlled monomer sequences. Despite the unique potential of the newly developed techniques, tailor-making the microstructure of macromolecules by suggesting the most appropriate polymerization recipe still remains a very challenging task. In the current work, two versatile and powerful tools capable of effectively addressing the aforementioned questions have been proposed and successfully put into practice. The two tools are established through the amalgamation of the Kinetic Monte Carlo simulation approach and machine learning techniques. The former, an intelligent modeling tool, is able to model and visualize the intricate inter-relationships of polymerization recipes/conditions (as input variables) and microstructural features of the produced macromolecules (as responses). The latter is capable of precisely predicting optimal copolymerization conditions to simultaneously satisfy all predefined microstructural features. The effectiveness of the proposed intelligent modeling and optimization techniques for solving this extremely important ‘inverse’ engineering problem was successfully examined by investigating the possibility of tailor-making the microstructure of Olefin Block Copolymers via chain-shuttling coordination polymerization.

scholarly journals The Role of Environmental Background Processes in Determining Groundwater Level Variability—An Investigation of a Record Flood Event Using Dynamic Factor Analysis

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2336
Author(s):  
Balázs Trásy ◽  
Norbert Magyar ◽  
Tímea Havril ◽  
József Kovács ◽  
Tamás Garamhegyi
Keyword(s):  
Factor Analysis ◽  
Groundwater Level ◽  
Coefficient Of Determination ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Climate Change Scenarios ◽  
Background Factors ◽  
Groundwater Levels ◽  
Groundwater Level Fluctuations ◽  
Environmental Background

Since groundwater is a major source of water for drinking and for industrial and irrigation uses, the identification of the environmental processes determining groundwater level fluctuation is potentially a matter of great consequence, especially in light of the fact that the frequency of extreme climate events may be expected to increase, causing changes in groundwater recharge systems. In the recent study, data measured at a frequency of one hour were collected from the Szigetköz, an inland delta of the Danube. These were then used to determine the presence, or not, and magnitude of any hidden environmental background factors that may be causing groundwater level fluctuations. Through the application of dynamic factor analysis, it was revealed that changes in groundwater level are mainly determined by (i) the water level of neighboring rivers and (ii) evapotranspiration. The intensity of these factors may also be estimated spatially. If the background factors determined by dynamic factor analysis do indeed figure in the linear model as variables, then the time series of groundwater levels can be said to have been accurately estimated with the use of linear regression. The accuracy of the estimate is indicated by the fact that adjusted coefficient of determination exceeds 0.9 in 80% of the wells. The results, via an enhanced understanding of the reasons for changes in the fluctuation of groundwater, could assist in the development of sustainable water management and irrigation strategies and the preparation for varying potential climate change scenarios.

scholarly journals Modelling groundwater level fluctuation in an Indian coastal aquifer

Water SA ◽  
2020 ◽  
Vol 46 (4 October) ◽  
Author(s):  
Safieh Javadinejad ◽  
Rebwar Dara ◽  
Forough Jafary
Keyword(s):  
Groundwater Level ◽  
Coastal Aquifer ◽  
M5 Model Tree ◽  
Groundwater Levels ◽  
Proposed Model ◽  
Soft Computing Techniques ◽  
Input Variables ◽  
Lag Times ◽  
Observation Wells

Estimating groundwater level (GWL) fluctuations is a vital requirement in hydrology and hydraulic engineering, and is commonly addressed through artificial intelligence (AI) models. The purpose of this research was to estimate groundwater levels using new modelling methods. The implementation of two separate soft computing techniques, a multilayer perceptron neural network (MLPNN) and an M5 model tree (M5-MT), was examined. The models are used in the estimation of monthly GWLs observed in a shallow unconfined coastal aquifer. Data for the water level were collected from observation wells located near Ganjimatta, India, and used to estimate GWL fluctuation. To do this, two scenarios were provided to achieve optimal input variables for modelling the GWL at the present time. The input parameters applied for developing the proposed models were a monthly time-series of summed rainfall, the mean temperature (within its lag times that have an effect on groundwater), and historical GWL observations throughout the period 1996–2006. The efficiency of each proposed model for Ganjimatt was investigated in stages of trial and error. A performance evaluation showed that the M5-MT outperformed the MLPNN model in estimating the GWL in the aquifer case study. Based on the M5-MT approach, the development of this model gives acceptable results for the Indian coastal aquifers. It is recommended that water managers and decision makers apply these new methods to monitor groundwater conditions and inform future planning.

scholarly journals Food Sales Forecasting Using Machine Learning Techniques: A Survey

2021 ◽  
Vol 9 (9) ◽  
pp. 869-872
Author(s):  
Aaron Rodrigues
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Grocery Stores ◽  
Data Analyst ◽  
Sales Prediction ◽  
Input Variables ◽  
Food Sales

Abstract: Food sales forecasting is concerned with predicting future sales of food-related businesses such as supermarkets, grocery stores, restaurants, bakeries, and patisseries. Companies can reduce stocked and expired products within stores while also avoiding missing revenues by using accurate short-term sales forecasting. This research examines current machine learning algorithms for predicting food purchases. It goes over key design considerations for a data analyst working on food sales forecasting’s, such as the temporal granularity of sales data, the input variables to employ for forecasting sales, and the representation of the sales output variable. It also examines machine learning algorithms that have been used to anticipate food sales and the proper metrics for assessing their performance. Finally, it goes over the major problems and prospects for applied machine learning in the field of food sales forecasting. Keywords: Food, Demand forecasting, Machine learning, Regression, Timeseries forecasting, Sales prediction

scholarly journals Evaluation of Machine Learning Techniques for Daily Reference Evapotranspiration Estimation

2019 ◽  
Author(s):  
Ali Rashid Niaghi ◽  
Oveis Hassanijalilian ◽  
Jalal Shiri
Keyword(s):  
Machine Learning ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Gene Expression Programming ◽  
Machine Learning Techniques ◽  
Limiting Factors ◽  
Support Vector ◽  
Daily Maximum ◽  
Climate Data ◽  
Input Variables

The ASCE-EWRI reference evapotranspiration (ETo) equation is recommended as a standardized method for reference crop ETo estimation. However, various climate data as input variables to the standardized ETo method are considered limiting factors in most cases and restrict the ETo estimation. This paper assessed the potential of different machine learning (ML) models for ETo estimation using limited meteorological data. The ML models used to estimate daily ETo included Gene Expression Programming (GEP), Support Vector Machine (SVM), Multiple Linear Regression (LR), and Random Forest (RF). Three input combinations of daily maximum and minimum temperature (Tmax and Tmin), wind speed (W) with Tmax and Tmin, and solar radiation (Rs) with Tmax and Tmin were considered using meteorological data during 2003–2016 from six weather stations in the Red River Valley. To understand the performance of the applied models with the various combinations, station, and yearly based tests were assessed with local and spatial approaches. Considering the local and spatial approaches analysis, the LR and RF models illustrated the lowest rate of improvement compared to GEP and SVM. The spatial RF and SVM approaches showed the lowest and highest values of the scatter index as 0.333 and 0.457, respectively. As a result, the radiation-based combination and the RF model showed the best performance with higher accuracy for all stations either locally or spatially, and the spatial SVM and GEP illustrated the lowest performance among models and approaches.

scholarly journals Wildfire Impacts on Groundwater Aquifers: A Case Study of the 1996 Honey Boy Fire in Beaver County, Utah, USA

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2279
Author(s):  
Benjamin T. Johnk ◽  
David C. Mays
Keyword(s):  
Groundwater Level ◽  
Temporary Reduction ◽  
Groundwater Levels ◽  
Groundwater Level Fluctuations ◽  
Monitoring Wells ◽  
Groundwater Aquifers ◽  
Before And After ◽  
Beaver County ◽  
Level Reduction

It is well known that wildfires destroy vegetation and form soil crusts, both of which increase stormwater runoff that accelerates erosion, but less attention has been given to wildfire impacts on groundwater aquifers. Here, we present a systematic study across the contiguous United States to test the hypothesis that wildfires reduce infiltration, indicated by temporary reductions in groundwater levels. Geographic information systems (GIS) analysis performed using structured queried language (SQL) categorized wildfires by their proximity to wells with publicly available monitoring data. Although numerous wildfires were identified with nearby monitoring wells, most of these data were confounded by unknown processes, preventing a clear acceptance or rejection of the hypothesis. However, this analysis did identify a particular case study, the 1996 Honey Boy Fire in Beaver County, Utah, USA that supports the hypothesis. At this site, daily groundwater data from a well located 790 m from the centroid of the wildfire were used to assess the groundwater level before and after the wildfire. A sinusoidal time series adjusted for annual precipitation matches groundwater level fluctuations before the wildfire but cannot explain the approximately two-year groundwater level reduction after the wildfire. Thus, for this case study, there is a correlation, which may be causal, between the wildfire and temporary reduction in groundwater levels. Generalizing this result will require further research.

scholarly journals Modelling of the Complex Groundwater Level Dynamics during Episodic Rainfall Events of a Surficial Aquifer in Southern Italy

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2916
Author(s):  
Nicola Pastore ◽  
Claudia Cherubini ◽  
Angelo Doglioni ◽  
Concetta Immacolata Giasi ◽  
Vincenzo Simeone
Keyword(s):  
Groundwater Level ◽  
Southern Italy ◽  
Infiltration Rate ◽  
Water Volume ◽  
Rainfall Events ◽  
Groundwater Levels ◽  
Groundwater Level Fluctuations ◽  
Groundwater Supply ◽  
Non Linear ◽  
Surficial Aquifer

We analyzed the complex dynamics that are involved the groundwater level variations due to the episodic rainfall supply in the Ionian coastal plain surficial aquifer located in Southern Italy. In this aquifer, as a consequence of the particular hydrogeological framework, both direct and lateral recharge mechanisms coexist. Hence, the dynamics of groundwater level variations are quite complex and strongly non-linear. Our focus was essentially on the short-term behavior of groundwater levels, with a specific analysis on episodic rainfall events. To model these dynamics, due to the presence of the preferential pathways in the infiltration processes, a kinematic dispersion wave model was used. Specifically, a one-dimensional and non-linear particle-based numerical model was developed. It uses ideal particles with constant water volume travel, according to celerity and hydraulic dispersion, to simulate the infiltration rate wave through the vadose zone. The infiltration rate that reaches the water table represents the input function to evaluate the aquifer groundwater level fluctuations. As a consequence of the special lithological and storage capacity characteristics of the surficial layers, groundwater flow conditions change from unconfined to confined. The developed model analyzes the direct groundwater supply under natural conditions, including episodic rainfall, and it has been validated using a high-resolution time series of rainfall data and groundwater level obtained from the monitoring station Terra Montonata.

Modeling of the Mechanical Properties of Carbon-Black Reinforced Rubber Blends by Machine Learning Techniques

2014 ◽  
Vol 627 ◽  
pp. 97-100 ◽  
Author(s):  
R. Fernandez-Martinez ◽  
R. Hernandez ◽  
J. Ibarretxe ◽  
Pello Jimbert ◽  
M. Iturrondobeitia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Carbon Black ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Efficient Design ◽  
Rubber Blends ◽  
Taguchi Design Of Experiments ◽  
Physical Attributes ◽  
Input Variables

Mastering the relationship between the final mechanical properties of carbon black reinforced rubber blends and their composition is a key advantage for an efficient design of the composition of the blend. In this work, some models to predict three relevant physical attributes of rubber blends — modulus at 100% deformation, Shore A hardness, and tensile strength — are built by machine learning methods and subsequently evaluated. Linear regression, artificial neural networks, support vector machine, and regression trees are used to generate the models. The number of used samples and the values for the input variables is determined by a Taguchi design of experiments, and prior to the modeling the uncertainty of the experimental data was analyzed.

scholarly journals The dynamics of cultivation and floods in arable lands of Central Argentina

2009 ◽  
Vol 13 (4) ◽  
pp. 491-502 ◽  
Author(s):  
E. F. Viglizzo ◽  
E. G. Jobbágy ◽  
L. Carreño ◽  
F. C. Frank ◽  
R. Aragón ◽  
...  
Keyword(s):  
Land Use ◽  
Groundwater Level ◽  
Time Lag ◽  
Groundwater Levels ◽  
Groundwater Level Fluctuations ◽  
Central Argentina ◽  
Cyclic Mechanism ◽  
Subsurface Lateral Flow ◽  
Flooded Area ◽  
One Year

Abstract. Although floods in watersheds have been associated with land-use change since ancient times, the dynamics of flooding is still incompletely understood. In this paper we explored the relations between rainfall, groundwater level, and cultivation to explain the dynamics of floods in the extremely flat and valuable arable lands of the Quinto river watershed, in central Argentina. The analysis involved an area of 12.4 million hectare during a 26-year period (1978–2003), which comprised two extensive flooding episodes in 1983–1988 and 1996–2003. Supported by information from surveys as well as field and remote sensing measurements, we explored the correlation among precipitation, groundwater levels, flooded area and land use. Flood extension was associated to the dynamics of groundwater level. While no correlation with rainfall was recorded in lowlands, a significant correlation (P<0.01) between groundwater and rainfall in highlands was found when estimations comprise a time lag of one year. Correlations between groundwater level and flood extension were positive in all cases, but while highly significant relations (P<0.01) were found in highlands, non significant relations (P>0.05) predominate in lowlands. Our analysis supports the existence of a cyclic mechanism driven by the reciprocal influence between cultivation and groundwater in highlands. This cycle would involve the following stages: (a) cultivation boosts the elevation of groundwater levels through decreased evapotranspiration; (b) as groundwater level rises, floods spread causing a decline of land cultivation; (c) flooding propitiates higher evapotranspiration favouring its own retraction; (d) cultivation expands again following the retreat of floods. Thus, cultivation would trigger a destabilizing feedback self affecting future cultivation in the highlands. It is unlikely that such sequence can work in lowlands. The results suggest that rather than responding directly and solely to the same mechanism, floods in lowlands may be the combined result of various factors like local rainfall, groundwater level fluctuations, surface and subsurface lateral flow, and water-body interlinking. Although the hypothetical mechanisms proposed here require additional understanding efforts, they suggest a promising avenue of environmental management in which cultivation could be steered in the region to smooth the undesirable impacts of floods.

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