Optimized forest degradation model (OFDM): an environmental decision support system for environmental impact assessment using an artificial neural network

被引:29
作者
Jahani, Ali [1 ]
Feghhi, Jahangir [2 ]
Makhdoum, Majid F. [2 ]
Omid, Mahmoud [3 ]
机构
[1] Univ Environm, Environm & Nat Resources Sci Dept, Karaj, Iran
[2] Univ Tehran, Univ Coll Agr & Nat Resources, Fac Nat Resources, Dept Forestry & Forest Econ, Karaj, Iran
[3] Univ Tehran, Fac Agr Engn & Technol, Dept Agr Machinery Engn, Karaj, Iran
关键词
EIA; EDSS; ANN; OFDM; MCDA; MANAGEMENT; PREDICTION; TOOLS; DSS; GIS; INTEGRATION; VARIABLES; PROPOSAL;
D O I
10.1080/09640568.2015.1005732
中图分类号
F0 [经济学]; F1 [世界各国经济概况、经济史、经济地理]; C [社会科学总论];
学科分类号
0201 ; 020105 ; 03 ; 0303 ;
摘要
The purpose of this article is Artificial Neural Network (ANN) modeling using ecological and associated factors with forest degradation to predict the degradation of ecosystem, thereby enabling us to assess the environmental impacts of forest projects as an Environmental Decision Support System (EDSS). Results of the Multi-Layer Feed-Forward Network (MLFN), trained for Optimized Forest Degradation Model (OFDM), indicate that the performance of OFDM is more than other degradation models. Changes in forest management activities with higher value in sensitivity analysis help forest managers to decrease OFDM entity and environment impacts. The system is an intelligent EDSS, which allows the decision-maker to model criteria in forest degradation in order to reach and employ the optimal allocation plan. Considering results, multi criteria decision analysis (MCDA) approaches based on ANN, is an encouraging and robust method for solving MCDA problems.
引用
收藏
页码:222 / 244
页数:23
相关论文
共 86 条
[1]  
[Anonymous], ENV IMPACT ASSESSMEN
[2]   A new approach to water quality modelling and environmental decision support systems [J].
Argent, R. M. ;
Perraud, J-M. ;
Rahman, J. M. ;
Grayson, R. B. ;
Podger, G. M. .
ENVIRONMENTAL MODELLING & SOFTWARE, 2009, 24 (07) :809-818
[3]   Decision support system for water distribution systems based on neural networks and graphs theory for leakage detection [J].
Arsene, Corneliu T. C. ;
Gabrys, Bogdan ;
Al-Dabass, David .
EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (18) :13214-13224
[4]   Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application [J].
Asadi, Ehsan ;
da Silva, Manuel Gameiro ;
Antunes, Carlos Henggeler ;
Dias, Luis ;
Glicksman, Leon .
ENERGY AND BUILDINGS, 2014, 81 :444-456
[5]  
Azari Dehkordi F., 2003, HIKOBIA, V14, P9
[6]   Thermal properties estimation during thawing via real-time neural network learning [J].
Boillereaux, L ;
Cadet, C ;
Le Bail, A .
JOURNAL OF FOOD ENGINEERING, 2003, 57 (01) :17-23
[7]   Assessing environmental costs and impacts of forestry activities: A multi-method approach to environmental accounting [J].
Buonocore, Elvira ;
Haeyhae, Tiina ;
Paletto, Alessandro ;
Franzese, Pier Paolo .
ECOLOGICAL MODELLING, 2014, 271 :10-20
[8]  
Burris R.K., 1997, ENVIRON IMPACT ASSES, V17, P5, DOI 10.1016/S0195-9255(96)00082-0
[9]  
Callan R., 1999, ESSENCE NEURAL NETWO
[10]   Artificial intelligence and environmental decision support systems [J].
Cortés, U ;
Sànchez-Marrè, M ;
Ceccaroni, L ;
Roda, IR ;
Poch, M .
APPLIED INTELLIGENCE, 2000, 13 (01) :77-91