我國位處地震與颱風災害發生頻繁的區域，因此天然災害的發生率較其他國家為高，但近年來受到氣候變遷的影響，災害的規模與受災範圍均有增加的趨勢，由天然災害所引發的邊坡破壞與土壤液化之大地災害亟待分析與討論，因此，本研究嘗試建構可自我調適的學習模式，利用該模式來研究邊坡穩定與土壤液化兩大地災害。
首先本研究以具複數染色體的實數型基因演算法來改善類神經網路的學習能力，完成演化式類神經網路(AENN)的方法建置，再使AENN在學習時能同時對網路結構進行最佳化並達成內部參數的自我調適，開發一新式的演算法：可調適型演化式類神經網路(AAENN)。而後以類神經網路(ANN)、AENN與AAENN對邊坡穩定與土壤液化問題進行分析，根據分析結果顯示，AENN與AAENN較ANN具有較快的學習速度與較佳的學習結果，顯示AENN與AAENN是成功的模式建構方法。
為分析邊坡穩定問題，本研究以省道台18線公路邊坡1992到2001的邊坡資料庫為研究對象，以坡度、坡向、坡型、坡高、風化土層厚度、地層、基岩岩性、當日降雨量與前七天有效累積降雨量為分析因子，透過AENN與AAENN分別建立邊坡破壞分析模式，再進一步根據兩模式建立邊坡崩壞警戒準則，而分析結果顯示兩模式均得到相同警界準則：Re + 2.85Rd = 420 mm (Re：前七天有效累積雨量；Rd：當日降雨量)，顯示當研究區域的降雨量到達此界線後，邊坡的崩壞率會急遽上升，因此研究區域應發布邊坡崩壞的警戒訊息。最後並與其他類似之警界準則進行比較，並利用研究區域內之實際降雨狀況探討此模式之實用性。
另外，為研究土壤液化問題，本研究蒐集世界已發生之土壤液化案例共367筆，以土層深度、土壤單位重Wt、(N1)60、細粒料含量FC、地下水位深度Hw、地震規模Mw以及最大地表加速度amax作為影響因子，並透過AENN與AAENN來分別進行土壤液化模式的建立，再進一步根據兩模式建立評估地震時液化災害發生的可能性之方法，而分析結果顯示兩模式均得到相同的土壤液化率為1之邊界：Mw + 4amax = 7.0 (Mw：地震矩規模；amax：最大地表加速度)，透過此液化邊界以及推求土壤液化率之公式Lr=1/{1+exp[8-1.9(M_W+4.5a_max ) ]⁡} (Lr：土壤液化率)，本研究提出一個可評估土壤可能液化範圍的方法，並且以其他引發液化災害的地震事件驗證該分析模式。

Taiwan locates in the area in which the earthquakes and typhoons occur frequently; hence, the frequency of the natural disaster occurrence is higher than other countries. In addition, the climate changing results in more violent disasters in recent years. Therefore, there is a demand to study and evaluate the slope failures and soil liquefactions associated with typhoons and earthquakes seriously. The main focus of this study is to develop a new simulation method which can be adaptive to the problem by itself and utilize the developed model to evaluate and analyze the slope stability and soil liquefaction.
The first step of this study is to develop the artificial evolution neural network (AENN) by improving the learning ability of the artificial neural network (ANN) through the genetic algorithm (GA) with multi-chromosomes and real number genes. Then, this study adjusts the learning process of AENN to make the architecture of the neural network and the parameters of GA could be optimized and adaptive by itself individually during the learning process. This study calls the new learning method the adaptive artificial neural network (AAENN). The following step, this paper utilizes ANN, AENN and AAENN to evaluate the slope stability and soil liquefaction. According to the analyzing results, AENN and AAENN have faster learning speed and smaller mean squared error (MSE) than ANN which indicates AENN and AAENN are good and successful simulation method.
For the slope stability analysis, this paper collects 103 slope failure records from 1992 through 2001 which consists of slope grade, slope aspect, slope type, slope height, the thickness of weathered soil layer, stratum, bedrock lithology, daily rainfall and effective rainfall. Then, using AENN and AAENN to develop the slope failure evaluation models simultaneously; and creating the slope failure warning criteria based on the two slope failure evaluation models. The analyzing results exhibit the two model lead to the same warning criteria: Re + 2.85Rd = 420 mm (Re: effective rainfall; Rd: daily rainfall). This phenomenon shows that the slope failure rate would increase drastically and the warning message should be issued, after the rainfall in the study area exceeds the warning criteria. Finally, this study compares the developed warning criteria with the other criteria, and utilizing the real rainfall records of the study area to evaluate the practicability of the evaluation model.
In addition, for the soil liquefaction analysis, this study assembles 367 soil liquefaction records occurred in several countries. Each record consists of soil depth, soil unit weight, (N1)60, fine content, water table, magnitude of the earthquake and maximum ground acceleration. Then, using AENN and AAENN to develop the soil liquefaction evaluation models simultaneously; and creating a new soil liquefaction rate evaluation method based on the two soil liquefaction evaluation models. The analyzing results exhibit the two model lead to the same liquefaction boundary of Lr =1 (Lr: liquefaction rate): MW + 4amax = 7.0 (MW: moment magnitude scale; amax: maximum ground acceleration). Finally, according to the liquefaction boundary and the equation of the liquefaction rate: Lr = 1/{exp[8-1.9(MW+4.5amax)]}, this study creates a soil liquefaction range evaluation method, and utilizes other earthquake events with soil liquefaction records to examine the evaluation method.

1. 方士杰，「阿里山公路邊坡崩壞特性調查與崩壞潛能評估模式建構之研究」，國立成功大學土木工程研究所，博士論文，2009。
2. 王淑慧，「類神經網路應用於道路邊坡落石坍方預測之可行性研究-以阿里山公路為例」，國立台北科技大學材料及資源工程學系碩士論文，2000。
3. 王智仁，「以現場調查方式分析影響公路岩石邊坡穩定性之工程地質因子-以南橫公路梅山至啞口段為例」，國立成功大學資源工程學系，碩士論文，2001。
4. 王鑫，「地形學」，聯經出版事業公司，台北，1988。
5. 江晏佃，「山區道路落石危險度與危害度之評估與準則」，國立交通大學土木工程研究所，碩士論文，1999。
6. 吳俊彥，「以類神經網路模式評估砂質土壤液化」，國立台灣大學土木工程研究所，碩士論文，1996。
7. 吳哲一，「一種有效率之邊坡滑動災害預測模式與感知監控系統之研究與設計」，國立屏東科技大學坡地防災及水資源工程研究所，2009。
8. 吳傳威、范正成、鄭大偉、吳銘塘、陳嘉明、王玉瑞、翁祖炘、歐辰雄，「台灣區道路落石坍方危險度資訊系統建構(二)」，交通部科技顧問室，1999。
9. 呂政諭，「地震與颱風作用下阿里山地區公路邊坡崩壞特性之研究」，國立成功大學土木工程研究所，碩士論文，2001。
10. 李元宏，「螞蟻演算法最佳化類神經網路於土壤液化評估之研究」，國立台灣大學土木工程研究所，碩士論文，2009。
11. 李德河、田坤國、黃嵩傑、林國忠，「賀伯颱風引致阿里山公路低海拔路段邊坡變動之觀測調查」，地工技術雜誌，第57期，第81-91頁，1996。
12. 李德河、張舜孔、林宏明，「山區道路邊坡崩壞特性調查與整治-以阿里山公路為例」，地工技術雜誌， 第104期，第63-74頁，2005。
13. 林振平，「泥岩地區坡地破壞潛能分析」，國立成功大學土木工程研究所，碩士論文，1991。
14. 林豐澤，「演化式計算上篇：演化式演算法的三種理論模式」，第3卷，第1期，第1-28頁，2005。
15. 邱筠，「南台灣軟岩區公路邊坡崩塌潛感路段評估模式之建置與應用」，國立屏東科技大學土木工程學系，碩士論文，2006。
16. 唐瑋廷，「砂性土層液化潛能評估─模糊類神經網路」，國立台灣大學土木工程研究所，碩士論文，2001。
17. 涂書芳，「以遙感探測方法探討影響公路邊坡穩定的重要因子-以南橫公路甲仙至啞口段為例」，國立成功大學資源工程學系，碩士論文，2001。
18. 張朝盛，「土壤液化潛能之類神經網路分析」，國立交通大學土木工程研究所，碩士論文，2001。
19. 張舜孔，「類神經網路應用在阿里山公路邊坡破壞因子之分析研究」，國立成功大學土木工程研究所，碩士論文，2003。
20. 張義隆、陳永祥、劉玉雯、劉正川，「山區道路落石風險評估與防護對策」，嘉義農專學報，第42期，第85-97頁，1995。
21. 許瑞文，「地理資訊系統─類神經網路土石流潛勢判定方法」，國立交通大學土木工程學系，碩士論文，2003。
22. 陳沛靖，「液化敏感區土地開發適宜性之研究—以彰化縣員林地區為例」，長榮大學土地管理與開發研究所，碩士論文，2006。
23. 陳宗宏，「類神經網路評估砂性土壤液化潛能探討-基因演算法」，國立台灣大學土木工程研究所，碩士論文，2006。
24. 陳明澤，「用類神經網路建立以SPT、CPT 及Vs 為主之臨界液化曲線」，朝陽科技大學營建工程研究所，碩士論文，2002。
25. 陳榮河，林美聆，林銘郎，「台灣區道路落石坍方之危險度分級準則及防治改善工法研究」，交通部科技顧問室，1997。
26. 辜炳寰，「類神經網路於土壤液化評估之應用」，國立成功大學土木工程研究所，碩士論文，2002。
27. 黃金華，「機率分析與類神經網路方法在液化潛能評估之比較」，暨南國際大學土木工程學系，碩士論文，2003。
28. 黃春銘，「運用模糊類神經網路進行山崩潛感分析—以台灣中部國姓地區為例」，國立中央大學應用地質研究所，碩士論文，2005。
29. 楊智堯，「類神經網路於邊坡破壞潛能分析之應用研究」，國立成功大學土木工程研究所，碩士論文，1999。
30. 葉怡成，「應用類神經網路」，儒林圖書有限公司，台北市，2001。
31. 葉怡成，「類神經網路模式應用與實作」，儒林圖書，台北，2001。
32. 蔡瑞煌，「類神經網路概論」，三民書局，台北，1995。
33. 蔡瑞煌，「類神經網路概論」，三民書局，台北市，1995。
34. 鄭世楠、葉永田，「台灣西部地區地震斷層與地形變分佈的研究」，中國地質學會九十五年度學術研討會，苗栗，2006。
35. 盧炳志，「類神經網路在大地工程參數分析之研究」，國立成功大學土木工程研究所，博士論文，1999。
36. 謝獻仁，「類神經網路於落石坡危險度評估」，國立交通大學土木工程學系，碩士論文，1998。
37. 藍世欽，「工程地質因子對道路邊坡穩定性之影響-以南橫公路甲仙至梅山段為例」，國立成功大學資源工程研究所，碩士論文，2000。
38. 羅建民，「剪力波速評估土壤液化潛能-模糊類神經網路」，國立台灣大學土木工程研究所，碩士論文，2003。
39. 中央氣象局網站，http://www.cwb.gov.tw/，2010。
40. 台灣科技大學防災及資訊中心網頁，http://140.118.105.99/，2010。
41. Ahmad, I., Naggar, M.H.E., Khan, A.N., 2007. Soil Dynamics and Earthquake Engineering, 27, 892–905.
42. Arifovic, J and Gençay, R., 2001. Using genetic algorithms to select architecture of a feedforward artificial neural network. Physica A, 289, 574–594.
43. Baine, N., 2008. A simple multi-chromosome genetic algorithm optimization of a proportional-plus-derivative fuzzy logic controller. NAFIPS 2008, New York, New York, USA, 1–5.
44. Blake, T.F., 1996. Personal communication from Youd, T.L..
45. Blanco, A., Delgado, M. and Pegalajar, M.C., 2001. A real-coded genetic algorithm for training recurrent neural networks. Neural Networks, 14, 93–105.
46. Boulanger, R.W., Mejia, L.H., Idriss, I.M., 1997. Liquefaction at Moss Landing during Loma Prieta earthquake. Journal of Geotechnical and Geoenvironmental Engineering, 123(5), 453–467.
47. Bryson, A.E. and Ho, Y.C., 1969. Applied Optimal Control. Blaisdell, New York.
48. Caniani, D., Pascale, S., Sdao, F., Sole, A., 2008. Neural networks and landslide susceptibility: a case study of the urban area of Potenza. Natural Hazards, 45, 55–72.
49. Cavill, R., Smith, S., Tyrrell, A., 2005. Multi-chromosomal genetic programming. Proceedings of the 2005 conference on Genetic and evolutionary computation table of contents, Washington DC, USA, 1753–1759.
50. Cavill, R., Smith, S.L., Tyrrell, A.M., 2006. Variable length genetic algorithms with multiple chromosomes on a variant of the onemax problem. Proceedings of the 8th annual conference on Genetic and evolutionary computation, Seattle, Washington, USA, 1405–1406.
51. Cha, D., Blumenstein, M., Zhang, H., Jeng, D.S., 2008. A neural-genetic technique for coastal engineering: determining wave-induced seabed liquefaction depth. Studies in Computational Intelligence, 82, 337–351.
52. Chang, M.S., Chiu, Y.F., Lin, S.Y., Ke, T.C., 2005. Preliminary study on the 2003 slope failure in Woo-Wan-Chai area, Mt. Ali Road, Taiwan. Engineering Geology, 80, 93–114.
53. Chang, T.C., 2007a. Risk degree of debris flow applying neural networks. Natural Hazards, 42, 209–224.
54. Chang, T.C., Chao, R.J., 2006. Application of back-propagation networks in debris flow prediction. Engineering Geology, 85, 270–280.
55. Chang, W.D., 2006. An improved real-coded genetic algorithm for parameters estimation of nonlinear systems. Mechanical Systems and Signal Processing, 20, 236–246.
56. Chang, W.D., 2007b. Nonlinear system identification and control using a real-coded genetic algorithm. Applied Mathematical Modelling, 31, 541–550.
57. Chen, C., 1987. The development history of roads in Taiwan. Institute of Transportation, Ministry of Transportation and Communications, Taiwan.
58. Chen, Y.R., Hsieh, S.C., Chen, J.W., Shih, C.C., 2005. Energy-based probabilistic evaluation of soil liquefaction. Soil Dynamics and Earthquake Engineering, 25, 55–68.
59. Cho, S.E., 2009. Probabilistic stability analyses of slopes using the ANN-based response surface. Computers and Geotechnics, 36, 787–797.
60. Choi, J., Oh, H.J., Won, J.S., 2009. Validation of an artificial neural network model for landslide susceptibility mapping. Environmental Earth Sciences, Published online.
61. Chow, T.T., Zhang G.Q., Lin, Z., Song, C.L., 2002. Global optimization of absorption chiller system by genetic algorithm and neural network. Energy and Buildings, 34, 103–109.
62. Das, S.K. and Basudhar, P.K., 2008. Prediction of residual friction angle of clays using artificial neural network, Engineering Geology, 100, 142–145
63. Davis, L., 1991. Handbook of genetic algorithms, New York: Van Nostrand Reinhold.
64. Ermini, L., Catani, F., Casagli, N., 2005. Artificial neural network applied to landslide susceptibility assessment. Geomorphology, 66, 327–343.
65. Godo, M., Takeda, K., Nakazawa, T., and Tanaka, H., 2001. Updating expressway traffic regulations in rainfall and results of application. Tsuchi-to-kiso, Vol. 49, No. 7, 19–21 (in Japanese).
66. Goh, A.T.C., 1994. Seismic liquefaction potential assessed by neural networks. Journal of Geotechnical Engineering, 120(9), 1467–1480.
67. Goh, A.T.C., 1996. Neural-network modeling of CPT seismic liquefaction data. Journal of Geotechnical Engineering, 122(1), 70–73.
68. Goh, A.T.C., 2002. Probabilistic neural network for evaluating seismic liquefaction potential. Canadian Geotechnical Journal, 39(1), 219–232.
69. Goldberg, D.E., 1989. Genetic algorithms in search, optimization, and machine learning. USA: Addison-Wesley.
70. Goldberg, D.E., 1991. Real-coded genetic algorithms. Virtual alphabets, and blocking. Complex Systems, 5, 139-167.
71. Gomes, H.M. and Silva, N.R.S., 2008. Some comparisons for damage detection on structures using genetic algorithms and modal sensitivity method. Applied Mathematical Modelling, 32, 2216–2232.
72. Gómez, H., Kavzoglu, T., 2005. Assessment of shallow landslide susceptibility using artificial neural network in Jabonosa River Basin, Venezuela. Engineering Geology, 78, 11–27.
73. Gupta, J.N.D. and Sexton, R.S., 1999. Comparing backpropagation with a genetic algorithm for neural network training. Omega, 27, 679–684.
74. Hanna, A.M., Ural, D., Saygili, G., 2007. Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data. Soil Dynamics and Earthquake Engineering, 27, 521–540.
75. He, R.S. and Hwang, S.F., 2007. Damage detection by a hybrid real-parameter genetic algorithm under the assistance of grey relation analysis. Engineering Applications of Artificial Intelligence, 20, 980–992.
76. He, S. and Li, J., 2009. Modeling nonlinear elastic behavior of reinforced soilusing artificial neural networks. Applied Soft Computing, 9, 954–961.
77. He, S. and Li, J., 2009. Modeling nonlinear elastic behavior of reinforced soil using artificial neural networks. Applied Soft Computing, 9, 954–961.
78. Hecht-Nielsen, R., 1987. Kolmogorov’s mapping neural network existence theorem. Proceedings of the first IEEE international conference on neural networks, San Diego CA, USA , 11–14.
79. Holland, J., 1975. Adaptation in natural and artificial systems. University of Michigan Press.
80. Hopfield, J.J., 1982. Neural network and physical systems with emergent collective computational abilities. Proceeding of the National Academy of Sciences of the USA, 79, 2554–2558.
81. Huang, C.Y., Chen, L.H., Chen, Y.L., Chang F.M., 2009. Evaluating the process of a genetic algorithm to improve the back-propagation network: A Monte Carlo study. Expert Systems with Application, 36, 1459–1465.
82. Hush, D.R., 1989. Classification with neural networks: a performance analysis. Proceedings of the IEEE international conference on systems Engineering Dayton Ohia, USA, 277–280.
83. Idriss, I.M., Boulanger, R.W., 2006. Semi-empirical procedures for evaluating liquefaction potential during earthquakes. Soil Dynamics and Earthquake Engineering, 26, 115–130.
84. Inagaki, K., Sadohara, S., 2006. Slope management planning for the mitigation of landslide disaster in urban areas. Journal of Asian Architecture and Building Engineering, 5 (1), 183–190.
85. Iovine, G., D’Ambrosio, D., Gregorio, S.D., 2005. Applying genetic algorithms for calibrating a hexagonal cellular automata model for the simulation of debris flows characterized by strong inertial effects. Geomorphology, 66, 287–303.
86. Javadi, A.A., Farmani, R., Tan, T.P., 2005. A hybrid intelligent genetic algorithm. Advanced Engineering informatics, 19, 255–262.
87. Jha, S.K., Suzuki, K, 2009. Reliability analysis of soil liquefaction based on standard penetration test. Computers and Geotechnics, 36, 589–596.
88. Juang, C.H., Chen, C.J., Tang, W.H., Rosowsky, D.V., 2000. CPT-based liquefaction analysis, Part 1: Determination of limit state function. Geotechnique, 50 (5), 583–592.
89. Juang, C.H., Chen, C.J., Tien, Y.M., 1999a. Appraising CPT-based liquefaction resistance evaluation methods – Artificial Neural Network Approach. Canadian Geotechnical Journal, 36 (3), 443–454.
90. Juang, C.H., Jiang, T., Andrus, R.D., 2002. Assessing Probability-based Methods for Liquefaction Evaluation. Journal of Geotechnical and Geoenvironmental Engineering, 128 (7). 580–589.
91. Juang, C.H., Lee, D.H., Sheu, C., 1992. Mapping Slope Failure Potential Using Fuzzy Sets. Journal of Geotechnical Engineering, 118 (3), 475–494.
92. Juang, C.H., Rosowsky, D.V., Tang, W.H., 1999b. Reliability-based method for assessing liquefaction potential of sandy soils. Journal of Geotechnical and Geoenvironmental Engineering, 125 (8), 684–689.
93. Kaastra, I. and Boyd, M., 1996. Designing a neural network for forecasting financial and economic time series. Neurocomputing, vol. 10, 215–236.
94. Kanellopoulas, I., Wilkinson, G.G., 1997. Strategies and best practice for neural network image classification. International Journal of Remote Sensing, 18, 711–725.
95. Kanungo, D.P., Arora, M.K., Sarkar, S., Gupta, R.P., 2006. A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology, 85, 347–366.
96. Kawabata, D. and Bandibas, J., 2009. Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN). Geomorphology, In press.
97. Kung, G.T.C., Hsiao, E.C.L., Schuster, M., Juang, C.H., 2007. A neural network approach to estimating deflection of diaphragm walls caused by excavation in clays. Computer and Geotechnics, 34, 385–396.
98. Kuo, J.T., Wang, Y.Y., Lung, W.S., 2006. A hybrid neural-genetic algorithm for reservoir water quality management. Water Research, 40, 1367–1376.
99. Lee, D.H., Lin, H.M., Chang, S.K., 2005. Investigation on the slope failure and its remediation methods of the mountain highway – Alishan highway. Sino-Geotechnics, 104, 63–74 (In Chinese).
100. Lee, D.H., Tien, K.G., Huang, S.C., Lin, G.C., 1996. Investigation of slope failure induced by typhoon Herb at low elevation area at Alishan highway. Sino-Geotechnics, 57, 81–91 (In Chinese).
101. Lee, S. and Evangelista, D.G., 2006. Earthquake-induced landslide-susceptibility mapping using an artificial neural network. Natural Hazards and Earth System Science, 6 (5), 687–695.
102. Lee, S., Ryu, J.H., Won, J.S., Prak, H.J., 2004. Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Engineering Geology, 71, 289–302.
103. Li, Y. and Häußler, A., 1996. Artificial evolution of neural networks and its application to feedback control. Artificial Intelligence in Engineering, 10, 143–153.
104. Liao, S. S. C., and Whitman, R. V., 1986. Overburden correction factors for SPT in sand. Journal of Geotechnical Engineering, ASCE, 112(3), 373–377.
105. Liao, S.S.C., Veneziano, D., Whitman, R.V., 1988. Regression models for evaluating liquefaction brobability. Journal of Geotechnical Engineering, 114(4), 389–441.
106. Lin, H.M., Chang, S.K., Wu, J.H., Juang, C.H., 2009. Neural network-based model for assessing failure potential of highway slopes in the Alishan, Taiwan Area: Pre- and post-earthquake investigation. Engineering Geology, 104, 280–289.
107. Lin, M.L., Jeng, F.S., 2000. Characteristics of hazards induced by extremely heavy rainfall in Central Taiwan – Typhoon Herb. Engineering Geology 58, 191–207.
108. Lin, W.T., Chou, W.C., Lin, C.Y., 2008. Earthquake-induced landslide hazard and vegetation recovery assessment using remotely sensed data and a neural network-based classifier: a case study in central Taiwan. Natural Hazards, 47 (3), 331–347.
109. Liu, C.N., Dong, J.J., Peng, Y.F., Huang, H.F., 2009. Effects of strong ground motion on the susceptibility of gully type debris flows. Engineering Geology, 104, 241–253.
110. Lu, C.Y., 2001. Slope failure characteristics of Ali-San highway during earthquake and typhoon. Master thesis, Department of Civil Engineering, National Chen Kung University, Tainan, Taiwan (In Chinese).
111. Lucasius, C.B. and Kateman, G., 1989. Applications of genetic algorithms in chemometrics. In J. David Schaffer, Proceedings of the Third International Conference on Genetic Algorithms, San Mateo: Morgan Kaufmann, 170–176.
112. Maniezzo, V., 1994. Genetic evolution of the topology and weight distribution of neural networks. IEEE Neural Network, 5, 39–53.
113. Maranon, A., Ruiz, P.D., Nurse, A.D., Huntley, J.M., Rivera, L., Zhou, G., 2007. Identification of subsurface deliminations in composite laminates. Composites Science and Technology, 67, 2817–2826.
114. Masters, T., 1994. Practical neural network recipes in C++. Academic Press, Boston MA.
115. McCulloch, W.S., Pitts, W., 1943. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5 115–137.
116. Melchiorre, C., Matteucci, M., Azzoni, A., Zanchi, A., 2008. Artificial neural network and cluster analysis in landslide susceptibility zonation. Geomorphology, 94, 379–400.
117. Michalewicz, Z., 1992. Genetic algorithms + data structures = evolution programs, New York: Springer.
118. Mitchell, M., 1996. An Introduction to Genetic Algorithms. MIT Press, Massachusetts.
119. Mondino, E.B., Giardino, M., Perotti, L., 2009. A neural network method for analysis of hyperspectral imagery with application to the Cassas landslide (Susa Valley, NW-Italy). Geomorphology, 110, 20–27.
120. Montana, D.J., Davis, L., 1989. Training Feedforward Neural Networks Using Genetic Algorithms. Proceedings of the International Joint Conference on Artificial Intelligence, 762–767.
121. Neaupane, K.M., Achet, S.H., 2004. Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya. Engineering Geology, 74, 213–226.
122. Nefeslioglu, H.A., Gokceoglu, C., Sonmez, H., 2008. An assessment on the use of logistic regression and artificial neural network with different sampling strategies for the preparation of landslide susceptibility maps. Engineering Geology, 97, 171–191.
123. Negnevitsky, M., 2002. Artificial Intelligence – A Guide to Intelligent Systems. Pearson Education Limited, Harlow, England.
124. Ni, S.H., Lu, P.C., Juang, C.H., 1995. A fuzzy neural network approach to evaluation of slope failure potential. Journal of Microcomputers in Civil Engineering, 11, 59–66.
125. Paola, J.D., 1994. Neural network classification of multispectral imagery. MSc thesis, The University of Arizona, USA.
126. Pierrot, H.J. and Hinterding, R., 1997. Using multi-chromosomes to solve a simple mixed integer problem. Advanced Topics in Artificial Intelligence, 1342, 137–146.
127. Pierson, L.A., Vickle, R.V., 1993. Rockfall Hazard Rating System – Participant’s Manual. Publication No. FHWA SA-93-057, Federal Highway Administration, U.S. Department of Transportation.
128. Ramakrishnan, D., Singh, T.N., Purwar, N., Barde, K.S., Gulati, A., Gupta, S., 2008. Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data, Gujarat, India. Computational Geosciences, 12, 491–501.
129. Reed, J., Toombs, R., Barricelli, N.A., 1967. Simulation of biological evolution and machine learning. Journal of Theoretical Biology, 17(3), 319–342.
130. Ripley, B.D., 1993. Statistical aspects of neural networks. In: Barndoff-Neilsen OE, Jensen JL, Kendall WS, editors. Networks and chaos-statistical and probabilistic aspects. Chapman & Hall, London, 40–123.
131. Ronald, S., Kirkby, S., Eklund, P., 1997. Multi-chromosome mixed encodings for heterogeneous problems. Proceedings of the IEEE Conference on Evolutionary Computation, 37–42.
132. Rossi, M., Guzzetti, F., Reichenbach, P., Mondini, A., Peruccacci, S., 2010. Optimal landslide susceptibility zonation based on multiple forecasts. Geomorphology, 114(3), 129–142.
133. Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. Learning representations by backpropagating errors. Nature, 323, 533–536.
134. Schmertmann, J.H., 1978. Use the SPT to measure dynamic soil properties. ASTM Special Technical Publication, 654, 341–355.
135. Sedki, A., Ouazar, D., Mazoudi, E.E., 2009. Evolving neural network using real coded genetic algorithm for daily rainfall–runoff forecasting. Expert Systems with Applications, 36, 4523–4527.
136. Seed, H.B., 1979. Liquefaction and cyclic mobility evaluation for level ground during earthquake. Journal of Geotechnical Engineering Division, 105(GT2), 201–255.
137. Seed, H.B., Idriss, I.M., 1971. Simplified procedure for evaluating soil liquefaction potential. Journal of the Soil Mechanics and Foundations Division, 97(9), 1249–1273.
138. Seed, H.B., Tokimatsu, K., Harder, L.F., Chung R.M., 1985. Influence of SPT procedures in soil liquefaction resistance evaluations. Journal of Gectechnical Engineering, 111(12), 1425–1445.
139. Seed. H.B., Idriss, I.M., Arango, I., 1982. Evaluation of liquefaction potential using field performance data. Journal of Geotechnical Engineering, 109(3), 458–482.
140. Sengupta, A. and Upadhyay, A., 2009. Locating the critical failure surface in a slope stability analysis by genetic algorithm. Applied Soft Computing, 9, 387–392.
141. Sexton, R.S., Dorsey, R.E., Johnson, J.D., 1998. Toward global optimization of neural networks: A comparison of the genetic algorithm and backpropagation. Decision Support Systems, 22, 171–185.
142. Shepherd, G.M. and Koch, C., 1990. Introduction to synaptic circuits. The Synaptic Organization of the Brain. G.M. Shepherd, ed., Oxford University Press, New York, 3–31.
143. Sonmez, H., Gokceoglu, C., Nefeslioglu, H.A., Kayabasi, A., 2006. Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation. International Journal of Rock Mechanics and Mining Sciences, 43, 224–235.
144. Srinivas, M. and Patnaik L.M., 1994. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems Man and Cybernetics Part B Cybernetic, 24, 656–666.
145. Srinivasulu, S. and Jain, A., 2006. A comparative analysis of training methods for artificial neural network rainfall-runoff models. Applied Soft Computing, 6, 293–306.
146. Süer, G.A., Badurdeen, F., Dissanayake, N., 2008. Capacitated lot sizing by using multi-chromosome crossover strategy. Journal of Intelligent Manufacturing, 19, 273–282.
147. Tokimatsu, K., and Yoshimi, Y., 1983. Empirical correlation of soil liquefaction based on SPT-N value and fines contents. Soil and Foundations, 23(4), 56–74.
148. Tung, C.P., Hsu, S.Y., Liu, C.M., Li, J.S., 2003. Application of the genetic algorithm for optimizing operation rules of the LiYuTan Reservoir in Taiwan. Journal of the American Water Resources Association, 39(3), 649–657.
149. Tutkun, N., 2009. Parameter estimation in mathematical models using the real coded genetic algorithms. Expert Systems with Applications, 36, 3342–3345.
150. Ural, D.N., Tolon, M., 2008. Slope Stability during Earthquakes: A Neural Network Application. Geotechnical Special Publication, 179, 878–885.
151. Veith, T.L., Wolfe, M.L., Heatwole, C.D., 2003. Optimization procedure for cost effective BMP placement at a watershed scale. Journal of the American Water Resources Association, 39 (6), 1331–1343.
152. Vipin, K.S., Sitharam, T.G., Anbazhagan, P., 2009. Probabilistic evaluation of seismic soil liquefaction potential based on SPT data. Natural Hazards, Online First.
153. Wang, C. (1994).A theory of generalization in learning machines with neural application. PhD thesis, The University of Pennsylvania, USA.
154. Wang, H.B., Xu, W.Y., Xu, R.C., 2005. Slope stability evaluation using Back Propagation Neural Networks. Engineering Geology, 80, 302–315.
155. Wang, L., 2005. A hybrid genetic algorithm-neural network strategy for simulation optimization. Applied Mathematics and Computation 170, 1329–1343.
156. Werbos, P., 1974. Beyond regression: New tools for prediction and analysis in the behavioral sciences. Ph.D. thesis, Harvard University, Cambridge, Mass, USA.
157. Whitley, D., Starkweather T., Bogart, C., 1990. Genetic algorithms and neural networks: Optimizing connections and connectivity. Parallel Computing, 14, 347–361.
158. Wright, A., 1991. Genetic algorithms for real parameter optimization. In G. J. E. Rawlin, Foundations of genetic algorithms 1, 205–218. San Mateo: Morgan Kaufmann.
159. Wu, J.H., Lin, H.M., Lee D.H., Fang, S.C., 2005a. Integrity assessment of rock mass behind the shotcreted slope using thermography. Engineering Geology, 80, 154–173.
160. Wu, Y.M., Shin, T.C., and Tsai, Y B., 2001. Near real-time mapping of peak ground acceleration and peak ground velocity following a strong earthquake. Bulletin of the Seismological Society of America. 91(5), 1218–1228.
161. Wu, Y.P., Tang, H.M., Ge, X.R., 2005b. Application of BP model to landslide hazard risk prediction. Rock and Soil Mechanics (Yantu Lixue), 26 (9), 1409–1413.
162. Xue, X.H., Zhang, W.H., Liu, H.J., 2007. Evaluation of slope stability based on genetic algorithm and fuzzy neural network. Rock and Soil Mechanics, 28 (12), 2643–2648.
163. Yan, H., Zheng, J., Jiang, Y., Peng, C., Xiao, S., 2008. Selecting critical clinical features for heart diseases diagnosis with a real-coded genetic algorithm. Applied Soft Computing, 8, 1105–1111.
164. Yasumasa, I., Hiroaki, K., Yoshiaki, S., Masaya, H., Toshikuni, N., Yashio, I., 1994. Application of an improved genetic algorithm to the learning of neural networks. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E77-A(4), 731–735.
165. Yilmaz, I., 2009. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural network and their comparison: A case study from Kat landslides (Tokat-Turkey). Computers & Geosciences, 35, 1125–1138.
166. Yoon, B., Holmes, D.J., Langholz, G., and Kandel, A., 1994. Efficient genetic algorithms for training layered feedforward neural networks. Information Sciences, 76, 67–85.
167. Youd, T.L., Idriss, I.M., Andrus, R.D., Arango, I., Castro, G., Christian, J.T., Dobry, R., Finn, W.D.L., Harder Jr., L.F., Hynes, M.E., Ishihara, K., Koester, J.P., Liao, S.S.C., Marcuson III, W.F., Martin, G.R., Mitchell, J.K., Moriwaki, Y., Power, M.S., Robertson, P.K., Seed, R.B., Stokoe II, K.H., 2001. Liquefaction resistance of soils: Summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils. Journal of Geotechnical and Geoenvironmental Engineering, 127(10), 817–833.
168. Zhang, J., Chung, H.S.H., Zhong, J., 2005. Adaptive crossover and mutation in genetic algorithms based on clustering technique. Proceedings of the 2005 conference on Genetic and evolutionary computation, Washington DC, USA, 1577–1578.
169. Zolfaghari, A.R., Heath, A.C., McCombie, P.F., 2005. Simple genetic algorithm search for critical non-circular failure surface in slope stability analysis. Computers and Geotechnics, 32, 139–152.