Using Steepness Coefficient to Improve Artificial Neural Network Performance for Environmental Modeling

Demi̇r, Selami; Karadeniz, Aykut; Demır, Neslihan Manav

doi:10.15244/pjoes/61958

Cited by 9 publications

(11 citation statements)

References 5 publications

(6 reference statements)

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“…MLP was also selected as an artificial neural network for EC, and it featured the exponential activation function of the hidden neurons and linear activation function of the output neurons (Table 3). In modeling engineering problems for environmental purposes the optimization of activation function should be applied to improve ANN with satisfactory results [47]. The value of EC was mainly affected by soil moisture content, soil texture, soil temperature, and bulk density (MPE, mean percentage error was 2.35%).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Applying an Artificial Neural Network (ANN) to Assess Soil Salinity and Temperature Variability in Agricultural Areas of a Mountain Catchment

Halecki¹,

Młyński²,

Ryczek³

et al. 2017

Pol. J. Environ. Stud.

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Spatial analysis is currently a popular research tool, particularly in studies that focus on soil properties, and it is important for a comprehensive presentation of results by means of spatial statistics techniques. Spatial autocorrelation determines a degree of relationship between variables for two specific spatial units (locations). This relationship is reflected by spatial dependence of investigated soil properties. Moran's I was used as a measure of spatial autocorrelation. Positive spatial autocorrelation was determined for soil salinity (electrical conductivity) and temperature. Thus, the aim of the study was to identify the factors affecting spatial correlation of electrical conductivity (EC) and temperature in farmland and forest-covered areas. A model of artificial neural network was based on salinity, as salinity reduces the amount of nutrients and soil temperature, thus inhibiting plant root growth. Our study revealed that the most effective parameters determining soil temperature were EC and moisture content. The best results in the EC model were achieved for soil moisture content, temperature, and soil texture. Both soil parameters were impacted by catchment land use. Spatial analysis of soil properties and identification of factors affecting their diversity may be helpful in determining proper land use -particularly of sustainable agricultural practices in mountain areas.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Applying an Artificial Neural Network (ANN) to Assess Soil Salinity and Temperature Variability in Agricultural Areas of a Mountain Catchment

Halecki¹,

Młyński²,

Ryczek³

et al. 2017

Pol. J. Environ. Stud.

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“…Geri beslemeli yapay sinir ağı (BPANN) için bir Excel Visual Basic for Applications (Excel VBA) programı kullanılmıştır [19,20]. BPANN'de girdi katmanı olarak çamur yaşı (θ s ), aerobik tankta hidrolik bekletme süresi (θ h ), geri devir oranı (R s ), iç geri devir oranı (R n ), aerobik tanktaki çözünmüş oksijen konsantrasyonu (C) ve giriş suyunda KOİ konsantrasyonu ve giriş suyunda TN konsantrasyonu olmak üzere 7 adet nöron kullanılmıştır.…”

Section: Yapay Sinir Ağıunclassified

“…Bu hususta ek bir çalışma yapılmamış olup, diklik katsayısının kullanımı ve optimum değerinin belirlenmesi ile ilgili reçete Demir ve ark. [19]'da açıklanmıştır. BPANN ile KOİ değerlerinin tahmininde performans açısından dikkat edilmesi gereken bir diğer husus ise öğrenme yeteneğinin ne kadar istikrarlı olduğudur.…”

Section: Koi̇ Giderim Verimiunclassified

“…Hava kirliliğinden su ve toprak kirliliğine kadar birçok alanda muhtelif prosesleri öğrenip modellemek için kullanılan yapay sinir ağları [14][15][16][17][18][19][20], biyolojik atıksu arıtma proseslerinin modellenmesi amacıyla da uygulanmıştır [21][22][23]. Ne var ki biyolojik atıksu arıtma proseslerinin yapay sinir ağları ile modellenmesi amacıyla gerçekleştirilen çalışmaların çoğu ağ topolojisinin kurulması üzerine odaklanmış; ağda kullanılan aktivasyon fonksiyonları geri planda kalmıştır.…”

Section: Introductionunclassified

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Bi̇yoloji̇k Atiksu Aritma Tesi̇sleri̇nde Karbon Ve Azot Gi̇deri̇m Veri̇mleri̇ni̇n Tahmi̇ni̇ Amaciyla Yapay Si̇ni̇r Ağlarinin Kullanimi

Demır¹

2017

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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ÖZBiyolojik azot giderimi gerçekleştirilen atıksu arıtma proseslerinin (AO prosesi) modellenmesi amacıyla Aktif Çamur Modeli No. 1 (ASM1) kullanılagelmişse de bu modelde ihtiyaç duyulan girdi parametrelerinin tahmin edilmesi çok zaman almaktadır. Bu çalışma kapsamında, ASM1 kadar detaylı girdi verisi gerektirmeyen geri beslemeli yapay sinir ağlarının ( USE OF ARTIFICIAL NEURAL NETWORKS AS A TOOL TO PREDICT CARBON AND NITROGEN REMOVAL EFFICIENCIES IN BIOLOGICAL WASTEWATER TREATMENT PLANTS ABSTRACTAlthough Activated Sludge Model No. 1 (ASM1) was used for modelling biological nitrogen removal processes, estimation of input parameters required to run this model necessitates complicated laboratory analyses. In this study, the performance of Backpropagation Artificial Neural Networks (BPANN), which requires considerably less numbers of input parameters, in predicting chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), and total nitrogen (TN) removal efficiencies was tested. For this purpose, four activation functions were employed in BPANN. Results suggested that COD, TKN, and TN removal efficiencies in AO processes can be accurately estimated using BPANN, with the highest learning and prediction capacity when Sinc function is employed. The mean square errors (MSEs) with Sinc-BPANN were calculated as 2.5010 -4 for COD removal efficiency, 4.1510 -4 for TKN removal efficiency, and 2.6510 -4 for TN removal efficiency. Therefore, the Sinc-BPANN is concluded to be an efficient tool for estimating nonlinear nature of COD, TKN, and TN removal efficiencies in AO processes using considerably less numbers of input parameters.

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“…Likewise, a remarkable interest in the application of artificial neural networks to various prognoses and evaluations has been observed over the past few years. ANNs have been employed to enhance environmental engineering through anticipating soil contamination, emission of nitrous oxide and concentration of polycyclic aromatic hydrocarbons [7,8], predicting solar activity to facilitate the management of solar power plants [9] and evaluating the performance of cyclone separators [10]. ANNs have also been used for prediction and classification in various problems on the border between environmental engineering and meteorology.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks to the Technical Condition Assessment of Water Supply Systems

Kamiński

Mizerski³

2017

Ecological Chemistry and Engineering S

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Abstract:The paper explains a method for discerning the parts of a water supply system in need of renovation. The results are based on technical data collected over the last twenty one years, concerning more than two hundred sections of both renovated and nonrenovated pipelines. In the study, an appropriately prepared data set was used for training an artificial neural network (ANN) in the form of a multilayer perceptron (MLP). Further comparison between the responses of the trained MLP and the decisions made by human experts showed satisfactory consistency, although 15% of the database records produced certain discrepancies. The presented method can help create an expert system capable of supporting failure-free operation of a water distribution system.

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Using Steepness Coefficient to Improve Artificial Neural Network Performance for Environmental Modeling

Cited by 9 publications

References 5 publications

Applying an Artificial Neural Network (ANN) to Assess Soil Salinity and Temperature Variability in Agricultural Areas of a Mountain Catchment

Applying an Artificial Neural Network (ANN) to Assess Soil Salinity and Temperature Variability in Agricultural Areas of a Mountain Catchment

Bi̇yoloji̇k Atiksu Aritma Tesi̇sleri̇nde Karbon Ve Azot Gi̇deri̇m Veri̇mleri̇ni̇n Tahmi̇ni̇ Amaciyla Yapay Si̇ni̇r Ağlarinin Kullanimi

Application of Artificial Neural Networks to the Technical Condition Assessment of Water Supply Systems

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