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A comparative study of artificial neural networks and multivariate regression for predicting groundwater depths in the Arak aquifer

Hanifian Soadat, Khaleghi Mohammad Reza, Najarchi Mohsen, Jafarnia Reza, Varvani Javad

Acta Geophysica

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2024

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Vol. 72, no. 1

419--432

EN

In recent years, the groundwater resources of Arak plain have been under severe stress, so in some areas, due to the drying up of wells, the depth of wells has increased to access water. In some areas, the groundwater depth is high, which will lead to the salinization of those lands in the future. Regional modeling was used to organize and measure the response of the groundwater resources of Arak plain against the implementation of different management and implementation scenarios. This study aims to investigate the effective factors in the groundwater depth to provide a regional model with multiple linear regression (MLR) methods for Arak plain aquifer. For this purpose, the average groundwater potential maps (GPMs) in the Arak plain, as a dependent variable, and the transmissivity of the aquifer formations, groundwater exploitation values, altitude, average precipitation of the region, the amount of evaporation, and the distance from water resources are considered independent variables and regression analysis is done in SPSS software media. It was done to present a linear model. In the next stage, the presented model was evaluated by applying it to places where its statistics and information were not used to present the model, and finally, by applying this model in the GIS environment, the GPMs for the region were created. The study was prepared. Also, an artificial neural network (ANN) was used to simulate the depth of underground water. The performance of the ANN was measured through parameters such as root-mean-square error (RMSE) and correlation coefficient between real and desired outputs (R). The results of both methods indicate that factors such as the transmissivity of aquifer formations, GPMs drawdown, topography (the height of the well site on the level of the watershed), the groundwater exploitation values at the maximum operating radius of the well, and the distance from water resources are the main factors of GPMs drawdown. But the effectiveness of ANN in estimating GPMs drawdown is higher than the MLR method. The implemented methodology could be generalized to other watersheds with water scarcity problems for groundwater management.

2

An investigation of the effects of moderator variables on the lower heating value estimation of lignite deposits in Turkey

Aksoy Mehmet

Gospodarka Surowcami Mineralnymi

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2023

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T. 39, z. 3

199--216

EN

Turkey has 19.3 billion tons of lignite reserves and the vast majority of these Neogene lignite deposits are preferred for use in thermal power plants due to their low calorific value. The calorific value of lignite used in thermal power plants for electricity generation must be kept under constant control. In the control of calorific value, the estimation of the lower and higher heating values (LHV and HHV) of lignite is of great importance. In the literature, there are many studies that establish a relationship between the heating values of coal and proximate and ultimate analysis variables. In the studies dealing with proximate analysis data, it is observed that although the coefficients of the obtained multiple linear regression models (MRM) are statistically insignificant, these models are used to predict heating values because of the meaningful correlation coefficient. In this study, it is investigated whether moderator variables are effective on LHV estimation with proximate analysis data collected from forty-one lignite basins in different regions of Turkey, and a moderator variable analysis (MVA) model is developed to be used for the prediction of LHV. As a result of the study, it is found that the proposed MVA model is in accordance with observation values (coefficient of determination R2 = 0.951), and absolute and standard errors are also small. Therefore, it is concluded that the use of MVA to estimate the LHV of Turkey’s lignite is found to be more statistically meaningful.

PL

Turcja posiada 19,3 mld ton zasobów węgla brunatnego, a zdecydowana większość tych neogeńskich złóż węgla brunatnego jest preferowana do wykorzystania w elektrowniach cieplnych ze względu na ich niską wartość opałową. Wartość opałowa węgla brunatnego wykorzystywanego w elektrowniach ciepłowniczych do produkcji energii elektrycznej musi być stale kontrolowana. W procesie kontroli wartości opałowej bardzo ważne jest oszacowanie wartości opałowej i ciepła spalania węgla brunatnego. W literaturze istnieje wiele badań, które ustalają związek między wartościami opałowymi węgla a zmiennymi analizy przybliżonej (technicznej) i końcowej. W badaniach dotyczących danych analizy technicznej zaobserwowano, że chociaż współczynniki uzyskanych modeli wielokrotnej regresji liniowej (MRM) są statystycznie nieistotne, modele te są wykorzystywane do przewidywania wartości opałowych ze względu na znaczący współczynnik korelacji. W niniejszym artykule zbadano, czy zmienne moderatora są skuteczne w szacowaniu wartości opałowej (LHV) na podstawie danych z analizy technicznej zebranych z czterdziestu jeden zagłębi węgla brunatnego w różnych regionach Turcji, a także opracowano model analizy zmiennych moderatora (MVA), który ma być wykorzystywany do przewidywania LHV. W wyniku badań stwierdzono, że proponowany model MVA jest zgodny z wartościami obserwacji (współczynnik determinacji R2 = 0,951), a błędy bezwzględne i standardowe są również niewielkie. W związku z tym stwierdzono, że wykorzystanie MVA do oszacowania LHV tureckiego węgla brunatnego jest statystycznie uzasadnione.

3

Prediction of electrical conductivity using ANN and MLR: a case study from Turkey

Ekemen Keskin Tülay, Özler Emre, Şander Emrah, Düğenci Muharrem, Mohammed Yadgar Ahmed

Acta Geophysica

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2020

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Vol. 68, no. 3

811--820

EN

The study areas are located in Turkey (Kastamonu, Bartın, Karabük, Sivas) and contain very diferent rock types, various mining and agricultural activity opportunities. So, the areas have groundwaters that have diferent chemical compositions and electrical conductivity (EC) values. The EC can be measured using EC meter, and it must be measured in situ. But, the measurement of EC in situ is laborious, time-consuming, expensive, and difcult in arduous terrain environments. In recent years, machine learning models have been a primary focus of interest for a lot of study by providing often highly accurate forecast for solutions of such problems. The aim of the study is to forecast EC of groundwater using artifcial neural networks (ANN) and multiple linear regressions (MLR). Twelve diferent hydrochemical parameters, which afect the EC, such as major/minor ions and trace elements, were used in the analysis. Multilayer feed-forward ANN trained with backpropagation in Python machine learning libraries was used in this study. In order to obtain the most appropriate ANN architecture, trialand-error procedure was used and diferent numbers of hidden layers, neurons, activation functions, optimizers, and test sizes were constructed. This study also tests the usability of input parameters in EC prediction studies. As a result, comparisons between the measured and predicted values indicated that the machine learning models could be successfully applied and provide high accuracy and reliability for EC and similar parameters forecasting.

4

Prediction of quality parameters of a dry air separation product using machine learning methods

Żogała Alina, Rzychoń Maciej

Gospodarka Surowcami Mineralnymi

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2019

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T. 35, z. 2

119--138

EN

The purpose of the work was to predict the selected product parameters of the dry separation process using a pneumatic sorter. From the perspective of application of coal for energy purposes, determination of process parameters of the output as: ash content, moisture content, sulfur content, calorific value is essential. Prediction was carried out using chosen machine learning algorithms that proved to be effective in forecasting output of various technological processes in which the relationships between process parameters are non-linear. The source of data used in the work were experiments of dry separation of coal samples. Multiple linear regression was used as the baseline predictive technique. The results showed that in the case of predicting moisture and sulfur content this technique was sufficient. The more complex machine learning algorithms like support vector machine (SVM) and multilayer perceptron neural network (MPL) were used and analyzed in the case of ash content and calorific value. In addition, k-means clustering technique was applied. The role of cluster analysis was to obtain additional information about coal samples used as feed material. The combination of techniques such as multilayer perceptron neural network (MPL) or support vector machine (SVM) with k-means allowed for the development of a hybrid algorithm. This approach has significantly increased the effectiveness of the predictive models and proved to be a useful tool in the modeling of the coal enrichment process.

PL

Celem pracy było prognozowanie wybranych parametrów produktu procesu suchej separacji za pomocą sortera pneumatycznego. Z punktu widzenia zastosowania węgla do celów energetycznych niezbędne jest określenie parametrów procesowych wydobycia, takich jak: zawartość popiołu, zawartość wilgoci, zawartość siarki czy wartość kaloryczna. Prognozowanie przeprowadzono przy użyciu wybranych algorytmów uczenia maszynowego, które okazały się skuteczne w prognozowaniu wyjścia różnych procesów technologicznych, w których zależności między parametrami procesu są nieliniowe. Źródłem danych wykorzystanych w pracy były eksperymenty procesu suchej separacji węgla. Zastosowano wieloraką regresję liniową jako bazową metodę predykcyjną. Wyniki pokazały, że w przypadku przewidywania zawartości wilgoci i siarki technika ta była wystarczająca. Bardziej złożone algorytmy uczenia maszynowego, takie jak maszyna wektorów nośnych (SVM) i perceptron wielowarstwowy (MLP) zostały wykorzystane i przeanalizowane w przypadku zawartości popiołu i wartości opałowej. Ponadto wdrożono technikę k-średnich. Rolą analizy skupień było uzyskanie dodatkowych informacji na temat próbek węgla będących wejściem procesu. Połączenie technik, takich jak perceptron wielowarstwowy (MLP) lub maszyna wektorów nośnych (SVM) z metodą k-średnich pozwoliło na opracowanie hybrydowego algorytmu. Takie podejście znacznie zwiększyło efektywność modeli predykcyjnych i okazało się użytecznym narzędziem w modelowaniu procesu wzbogacania węgla.

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Planowanie terenów mieszkaniowych z perspektywy zrównoważonego rozwoju na przykładzie gminy Murowana Goślina

Zydroń A., Kayzer D., Antkowiak M., Matecka B.

Rocznik Ochrona Środowiska

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2016

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Tom 18, cz. 2

247--258

EN

The main aim of the study was to create the map of attractiveness of the housing area earmarked for construction purposes in Murowana Goślina commune in the aspect of sustainable development. The basis for creating the map was the analysis of factors affecting the value of undeveloped property. The study included both natural and anthropogenic factors. The study analyzes the material from the years 2010-2013, which were obtained from the District Centre of Geodesy and Cartography Documentation in Poznan. In order to achieve this aim, two types of analyses were performed: spatial with the QGIS 2.12.0 Lyon software and statistical, using multiple regression with STATISTICA 10 software. The research helped to isolate the factors significantly affecting the value of the property and determine the degree of their impact. The thesis also presents the map of investment attractiveness for Murowana Goślina. In these terms, the most attractive areas were Murowana Goślina and Mściszewo.