Environmental emergency decision support system based on Artificial Neural Network

Liao, Zhenliang; Wang, Bo; Xia, Xiaowei; Hannam, Phillip M.

doi:10.1016/j.ssci.2011.07.014

Cited by 36 publications

(20 citation statements)

References 21 publications

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“…Sedangkan SIG digunakan untuk mengolah, mensimulasikan skenario dan memvisualisasikan hasil pemodelan. Beberapa penelitian dilakukan khususnya dibidang terapan lingkungan termasuk 128 untuk kajian erosi dan longsor membuktikan bahwa integrasi SIG dan PJ lebih akurat dan efektif (Asis dan Omasa, 2007; Pradhnan dan Lee., 2007;Pradhan, Lee, dan Buchroitner., 2010;Liao et al, 2012 (Ypsilantis, 2011).…”

Section: Pendahuluanunclassified

Pemodelan Spasial Erosi Kualitatif Berbasis Raster Studi Kasus di DAS Serang, Kabupaten Kulonprogo

2018

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ABSTRAKErosi merupakan salah satu fenomena alam yang banyak dikaji karena melibatkan banyak faktor yaitu vegetasi, tanah, iklim, topografi dan manusia. Kompleksitas faktor-faktor yang mempengaruhi erosi disederhanakan melalui pemodelan untuk memprediksi tingkat erosi pada suatu wilayah dengan memanfaatkan data penginderaan jauh dan sistem informasi geografis. Faktor yang digunakan dalam menyusun model hanya melibatkan tiga faktor yaitu vegetasi, tanah dan lereng. Penelitian ini dilakukan di DAS Serang karena termasuk salah satu DAS yang berada dalam kondisi kritis yang dapat memicu terjadinya degradasi lahan, erosi dan longsor. Tujuan penelitian ini adalah mengetahui distribusi spasial tingkat erosi kualitatif di DAS Serang. Pendekatan yang digunakan adalah integrasi peginderaan jauh dan sistem informasi geografis berbasis raster. Validasi model dilakukan dengan melihat faktor topografi dan indikator erosi kualitatif di lapangan yaitu armour layer, singkapan akar, pedestal, erosi alur dan gully. Hasil penelitian menunjukan model yang dihasilkan sangat efektif sebagai solusi cepat prediksi erosi. Berdasarkan hasil analisis tingkat erosi sangat berat mendominasi di wilayah kajian yaitu sebagian besar di kecamatan Kokap, Girimulyo dan sebagian Pengasih. ABSTRACTErosion is one of the natural phenomena that's studied by many because it involves many factors, namely vegetation, soil, climate, topography and humans. The complexity of the factors affecting erosion is simplified through modeling to predict of erosion rates in a region by utilizing remote sensing data and geographic information systems. The erosion control factor used in this research fewer parameters, namely vegetation, soil and topography only. This research was conducted in Serang watershed because it is one of the watersheds which are in critical conditions which can trigger land degradation, erosion and landslides. The purpose of this research was to know the spatial distribution of erosion susceptibility levels in Serang watershed. The approach used was the integration of remote sensing and raster-based geographic information system. Model validation was undertaken based on topograhy factor and observation of qualitative erosion indicators in the field. The indicators used were pedestals, armor layers, root exposure, or other erosion featuress such as rill and gullies. The results show that the resulting model is more effective as a quick solution to the prediction of erosion. Based on the results of the analysis, the spatial distribution of erosion rates is very dominant in the study area, mostly in Kokap, Girimulyo and some of the subdistricts.

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Section: Pendahuluanunclassified

Pemodelan Spasial Erosi Kualitatif Berbasis Raster Studi Kasus di DAS Serang, Kabupaten Kulonprogo

2018

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“…ANN has been found to be the domain for many successful applications of prediction tasks, in modelling and prediction of energy-engineering systems [22], prediction of the energy consumption of passive solar buildings [23], developing energy system and forecast of energy consumption [24], and analysis of reduction of emissions [25]. There are also some relevant reports of ANN's use based on decision support systems in various subjects such as solving the buffer allocation problem in reliable production [26], developing environmental emergency decision support systems [27], risk assessment on prediction of terrorism insurgency [28] and metamodeling of simulation metamodel [29]. ANN has been used to predict specific fuel consumption and exhaust temperature of a Diesel engine for various injection timings [30].…”

Section: The Use Of Artificial Neural Network and Related Literaturementioning

confidence: 99%

An artificial neural network based decision support system for energy efficient ship operations

Beşikçi

Arslan

Turan

et al. 2016

Computers & Operations Research

217

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Reducing fuel consumption of ships against volatile fuel prices and greenhouse gas emissions resulted from international shipping are the challenges that the industry faces today. The potential for fuel savings is possible for new builds, as well as for existing ships through increased energy efficiency measures; technical and operational respectively. The limitations of implementing technical measures increase the potential of operational measures for energy efficient ship operations. Ship owners and operators need to rationalise their energy use and produce energy efficient solutions. Reducing the speed of the ship is the most efficient method in terms of fuel economy and environmental impact. The aim of this paper is twofold: (i) predict ship fuel consumption for various operational conditions through an inexact method, Artificial Neural Network ANN; (ii) develop a decision support system (DSS) employing ANN based fuel prediction model to be used on-board ships on a real time basis for energy efficient ship operations. The fuel prediction model uses operating data -'Noon Data' -which provides information on a ship's daily fuel consumption. The parameters considered for fuel prediction are ship speed, revolutions per minute (RPM), mean draft, trim, cargo quantity on board, wind and sea effects, in which output data of ANN is fuel consumption. The performance of the ANN is compared with multiple regression analysis (MR), a widely used surface fitting method, and its superiority is confirmed. The developed DSS is exemplified with two scenarios, and it can be concluded that it has a promising potential to provide strategic approach when ship operators have to make their decisions at an operational level considering both the economic and environmental aspects.

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“…Numerous researches have been conducted, especially in the field of applied environment, including the research in erosion and landslides. These researches integrated remote sensing with the geographic information system which managed to generate more accurate and effective predictions (Asis & Omasa, 2007;Liao et al, 2012;Pradhan, Lee, & Buchroithner, 2010;Pradhan & Saro, 2007).…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing and Gis Approaches to a Qualitative Assessment of Soil Erosion Risk in Serang Watershed, Kulonprogo, Indonesia

Arif

Danoedoro

Hartono

2017

Geoplanning: Journal of Geomatics and Planning

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Environmental emergency decision support system based on Artificial Neural Network

Cited by 36 publications

References 21 publications

Pemodelan Spasial Erosi Kualitatif Berbasis Raster Studi Kasus di DAS Serang, Kabupaten Kulonprogo

Pemodelan Spasial Erosi Kualitatif Berbasis Raster Studi Kasus di DAS Serang, Kabupaten Kulonprogo

An artificial neural network based decision support system for energy efficient ship operations

Remote Sensing and Gis Approaches to a Qualitative Assessment of Soil Erosion Risk in Serang Watershed, Kulonprogo, Indonesia

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