Evaluation of an Agricultural Meteorological Disaster Based on Multiple Criterion Decision Making and Evolutionary Algorithm

The evaluation of a meteorological disaster can be regarded as a multiple-criteria decision making problem because it involves many indexes. Firstly, a comprehensive indexing system for an agricultural meteorological disaster is proposed, which includes the disaster rate, the inundated rate, and the complete loss rate. Following this, the relative weights of the three criteria are acquired using a novel proposed evolutionary algorithm. The proposed algorithm consists of a differential evolution algorithm and an evolution strategy. Finally, a novel evaluation model, based on the proposed algorithm and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), is presented to estimate the agricultural meteorological disaster of 2008 in China. The geographic information system (GIS) technique is employed to depict the disaster. The experimental results demonstrated that the agricultural meteorological disaster of 2008 was very serious, especially in Hunan and Hubei provinces. Some useful suggestions are provided to relieve agriculture meteorological disasters.

[1]  Georgios Aim. Skianis,et al.  Assessment of rural community and agricultural development using geomorphological–geological factors and GIS in the Trikala prefecture (Central Greece) , 2013, Stochastic Environmental Research and Risk Assessment.

[2]  Zaiwu Gong,et al.  The comprehensive risk evaluation on rainstorm and flood disaster losses in China mainland from 2004 to 2009: based on the triangular gray correlation theory , 2014, Natural Hazards.

[3]  H. Kan,et al.  Ambient Air Pollution, Meteorological Factors and Outpatient Visits for Eczema in Shanghai, China: A Time-Series Analysis , 2016, International journal of environmental research and public health.

[4]  F. Rembold,et al.  Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery , 2011 .

[5]  Yongqiang Liu,et al.  Effects of meteorological droughts on agricultural water resources in southern China , 2017 .

[6]  Biswajeet Pradhan,et al.  Suitability estimation for urban development using multi-hazard assessment map. , 2017, The Science of the total environment.

[7]  T. L. Saaty.  A Scaling Method for Priorities in Hierarchical Structures , 1977 .

[8]  Hongxia Liu,et al.  Evolution Characteristics of Agricultural Drought Disasters in China , 2015 .

[9]  Gary G. Yen,et al.  A generic framework for constrained optimization using genetic algorithms , 2005, IEEE Transactions on Evolutionary Computation.

[10]  Zhongyi Sun,et al.  Integrated risk zoning of drought and waterlogging disasters based on fuzzy comprehensive evaluation in Anhui Province, China , 2014, Natural Hazards.

[11]  Chengdong Xu,et al.  Spatiotemporal Risk of Bacillary Dysentery and Sensitivity to Meteorological Factors in Hunan Province, China , 2017, International journal of environmental research and public health.

[12]  Zoe Nivolianitou,et al.  Important meteorological data for use in risk assessment , 2004 .

[13]  D. Yan,et al.  The impact of irrigation water supply rate on agricultural drought disaster risk: a case about maize based on EPIC in Baicheng City, China , 2015, Natural Hazards.

[14]  Mehmet Fatih Tasgetiren,et al.  Differential evolution algorithm with ensemble of parameters and mutation strategies , 2011, Appl. Soft Comput..

[15]  Morteza Yazdani,et al.  A state-of the-art survey of TOPSIS applications , 2012, Expert Syst. Appl..

[17]  Wei Sun,et al.  Extreme meteorological disaster effects on grain production in Jilin Province, China , 2017 .

[18]  Chih-Hao Lin,et al.  A rough penalty genetic algorithm for constrained optimization , 2013, Inf. Sci..

[19]  Jidong Wu,et al.  Exploring the characteristics of major natural disasters in China and their impacts during the past decades , 2013, Natural Hazards.

[20]  Xiaobing Yu,et al.  An effective improved differential evolution algorithm to solve constrained optimization problems , 2017, Soft Computing.

[21]  Dequn Zhou,et al.  How Have Political Incentives for Local Officials Reduced Environmental Pollution in Resource-Depleted Cities? , 2017 .

[22]  Kalliopi Gaki-Papanastassiou,et al.  Potential suitability for urban planning and industry development using natural hazard maps and geological–geomorphological parameters , 2012, Environmental Earth Sciences.

[23]  Jiquan Zhang,et al.  Risk assessment of drought disaster in the maize-growing region of Songliao Plain, China , 2003 .

[24]  Ruhul A. Sarker,et al.  Analyzing the Simple Ranking and Selection Process for Constrained Evolutionary Optimization , 2008, Journal of Computer Science and Technology.

[25]  F. Zheng,et al.  Spatial and temporal changes of meteorological disasters in China during 1950–2013 , 2015, Natural Hazards.

[26]  Jian-Bo Yang,et al.  Multiple Attribute Decision Making , 1998 .

[27]  C. Genthon,et al.  Evaluation of Antarctic snowfall in global meteorological reanalyses , 2017 .

[28]  Lihong Wang,et al.  Priority of a Hesitant Fuzzy Linguistic Preference Relation with a Normal Distribution in Meteorological Disaster Risk Assessment , 2017, International journal of environmental research and public health.

[29]  Yuren Zhou,et al.  An Adaptive Tradeoff Model for Constrained Evolutionary Optimization , 2008, IEEE Transactions on Evolutionary Computation.

[30]  Zaiwu Gong,et al.  China’s regional rainstorm floods disaster evaluation based on grey incidence multiple-attribute decision model , 2014, Natural Hazards.

[31]  Pei Yee Ho,et al.  Evolutionary constrained optimization using an addition of ranking method and a percentage-based tolerance value adjustment scheme , 2007, Inf. Sci..

[32]  Yong Wang,et al.  An improved (μ + λ)-constrained differential evolution for constrained optimization , 2013, Inf. Sci..

[33]  Xin Yao,et al.  Search biases in constrained evolutionary optimization , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[34]  Jun Liu,et al.  Does industrial agglomeration promote the increase of energy efficiency in China , 2017 .

[35]  M. Webber,et al.  Incentive-compatible payments for watershed services along the Eastern Route of China’s South-North Water Transfer Project , 2017 .

[36]  Dequn Zhou,et al.  How Have Political Incentives for Local Officials Reduced the Environmental Pollution of Resource-depleted Cities? , 2017 .

[37]  T. Saaty.  Decision making — the Analytic Hierarchy and Network Processes (AHP/ANP) , 2004 .

[38]  Naiming Xie,et al.  China’s regional meteorological disaster loss analysis and evaluation based on grey cluster model , 2014, Natural Hazards.

[39]  Jing J. Liang,et al.  Problem Deflnitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization , 2006 .

[40]  E. A. Baltas,et al.  Urban flood hazard assessment in the basin of Athens Metropolitan city, Greece , 2016, Environmental Earth Sciences.

[41]  L. Hao,et al.  Risk assessment to China’s agricultural drought disaster in county unit , 2012, Natural Hazards.

[42]  Qiang Zhang,et al.  Risk evaluation of agricultural disaster impacts on food production in southern China by probability density method , 2016, Natural Hazards.

[43]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.