標(biāo)題: Titlebook: Computational Intelligence for Water and Environmental Sciences; Omid Bozorg-Haddad,Babak Zolghadr-Asli Book 2022 The Editor(s) (if applic [打印本頁(yè)] 作者: retort 時(shí)間: 2025-3-21 17:46
書(shū)目名稱Computational Intelligence for Water and Environmental Sciences影響因子(影響力)
書(shū)目名稱Computational Intelligence for Water and Environmental Sciences影響因子(影響力)學(xué)科排名
書(shū)目名稱Computational Intelligence for Water and Environmental Sciences網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Computational Intelligence for Water and Environmental Sciences網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Computational Intelligence for Water and Environmental Sciences被引頻次
書(shū)目名稱Computational Intelligence for Water and Environmental Sciences被引頻次學(xué)科排名
書(shū)目名稱Computational Intelligence for Water and Environmental Sciences年度引用
書(shū)目名稱Computational Intelligence for Water and Environmental Sciences年度引用學(xué)科排名
書(shū)目名稱Computational Intelligence for Water and Environmental Sciences讀者反饋
書(shū)目名稱Computational Intelligence for Water and Environmental Sciences讀者反饋學(xué)科排名
作者: 安裝 時(shí)間: 2025-3-21 21:56 作者: 半導(dǎo)體 時(shí)間: 2025-3-22 03:55 作者: rods366 時(shí)間: 2025-3-22 07:18
978-981-19-2521-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: Ingest 時(shí)間: 2025-3-22 09:56 作者: Opponent 時(shí)間: 2025-3-22 15:16 作者: Opponent 時(shí)間: 2025-3-22 17:03 作者: 成份 時(shí)間: 2025-3-23 01:02
Deployment Criteria for Strategic Defences,application and promising results. The inspiration of PSO is the collaborative swarm behavior of biological populations—a noted computational intelligence technique. There is no doubt that PSO has made outstanding contributions to vast water and environmental science problems in the real world thus 作者: 燈泡 時(shí)間: 2025-3-23 01:47 作者: commonsense 時(shí)間: 2025-3-23 08:43
https://doi.org/10.1007/978-3-642-57332-3 calibration of hydrologic model parameters with conflicting objectives attempts to adjust the parameter values in terms of different objective functions. Thus, this research carried out a procedure of multi-objective optimization for a distributed and single event-based rainfall–runoff model (i.e. 作者: Intrepid 時(shí)間: 2025-3-23 12:12
Strategic delegation in the trade union,ef introduction and literature review of the ACO and its application are demonstrated in detail. Then the process and the basic pseudo-code of ACO are introduced as well. Additionally, the Antlion Optimization (ALO) algorithm is represented as a single objective algorithm of the Ant family. Finally,作者: orient 時(shí)間: 2025-3-23 14:25 作者: 巧思 時(shí)間: 2025-3-23 22:01 作者: 固執(zhí)點(diǎn)好 時(shí)間: 2025-3-24 00:32 作者: 字謎游戲 時(shí)間: 2025-3-24 02:23
Innovation from Component Development,and researchers began to think of making patterns from the brain’s structure. As a result, different ANN models were developed by imitating the human brain. Neural networks are computer programs that, like the human brain, are made up of arbitrary numbers of cells, nodes, units, or neurons that rela作者: 結(jié)果 時(shí)間: 2025-3-24 08:09 作者: Abduct 時(shí)間: 2025-3-24 13:28
Wissensorientiertes Performance Measurementpermit computer systems to explore patterns in data. It is quite challenging and difficult for traditional machine learning techniques to obtain information and pattern from big and complex data. As a subset of machine learning or even artificial intelligence, deep learning focuses on developing lar作者: Finasteride 時(shí)間: 2025-3-24 16:12
Wissensorientiertes Performance Measurementnd agricultural projects. It is necessary to apply appropriate methods to achieve accurate policies and decisions for sustainable water and environmental development in such circumstances. With an increasing amount of “big data” with complex and nonlinear relationships, data-driven methods such as m作者: 頭盔 時(shí)間: 2025-3-24 20:24 作者: Extricate 時(shí)間: 2025-3-24 23:40 作者: Banquet 時(shí)間: 2025-3-25 03:21
Bernd W. Wirtz,Nikolai Lihotzky MBAor forecasting DO several days in advance. First, the correlation between DO data at several times lags were calculated using the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Second, the DO concentration time series were decomposed using the empirical wavelet trans作者: 不如屎殼郎 時(shí)間: 2025-3-25 07:44
Computational Intelligence for Water and Environmental Sciences978-981-19-2519-1Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: investigate 時(shí)間: 2025-3-25 14:10 作者: 遭受 時(shí)間: 2025-3-25 17:28
1860-949X en imposed serious challenges to traditional deterministic precise frameworks. The topic caters to postgraduate students and senior researchers who are interested in computational intelligence approach to issues stemming from water and environmental sciences..978-981-19-2521-4978-981-19-2519-1Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: Malaise 時(shí)間: 2025-3-25 20:08 作者: Hla461 時(shí)間: 2025-3-26 02:42 作者: antiandrogen 時(shí)間: 2025-3-26 04:43
https://doi.org/10.1007/978-3-642-57332-3 evaluated through 5000 epochs and the best values of NSE (0.91), ESP (0) and RTS (0) were obtained for the simulation based on the third storm event (05/10/2005). The non-dominated solutions, extracted from the AMALGAM approach were plotted in a three-dimensional space to form the Pareto front. Acc作者: cortisol 時(shí)間: 2025-3-26 09:30
Asuman Koc Yurtkur,Tezcan Abas?zff and semi-treated sanitary/industrial sewage discharge. Therefore, artificial intelligence (AI) techniques are used to decrease model development costs and improve prediction errors, achieving more efficient models. In this chapter, some well-known techniques and AI-based methods are introduced, a作者: 有偏見(jiàn) 時(shí)間: 2025-3-26 16:00
Strategic Thinking and Risk Attitudeal classification in advanced ways. After classifying the data, DT curiously searches for the rules governing the data decision space. It should be noted, however, that DT can be used for both classification and regression problems, but is often used more in classification problems. It should be not作者: 努力趕上 時(shí)間: 2025-3-26 17:37
Wissensorientiertes Performance Measurementn appropriate model, particularly in modeling large (big) and complex datasets. This chapter provides a review of deep learning concepts, introduce some of the developed deep learning structures, and their application in water and environmental studies.作者: escalate 時(shí)間: 2025-3-26 22:58 作者: 碎石 時(shí)間: 2025-3-27 02:05
Die vernetzte Konsumgüterbrancheent and statistical models, includes Radial Based Function Neural Network (RBFNN), Adaptive Neuro Fuzzy Inference System (ANFIS), Feedforward Neural Network (FFNN), Linear Regression (LR), Generalized Linear Regression (GLR), and Support Vector Regression (SVR). An example of a real urban water dist作者: lambaste 時(shí)間: 2025-3-27 05:30 作者: 鞠躬 時(shí)間: 2025-3-27 11:35
Optimization Algorithms Surpassing Metaphorhe number of algorithms based on natural behaviours has increased, the majority of them deal with some ordeals such as being stuck in local optimal results. Hence, these ordeals pave the way for the advent of new mathematical, population-based techniques. In this chapter, three powerful metaphor-fre作者: Nerve-Block 時(shí)間: 2025-3-27 15:23
A Survey of PSO Contributions to Water and Environmental Sciencesvey revealed that PSO has been employed both solely and collaboratively with other approaches like machine learning techniques and simulation software to solve single- and multiple-objective problems of different sectors from surface water to renewable energy generation.作者: MAIM 時(shí)間: 2025-3-27 20:07 作者: ALLEY 時(shí)間: 2025-3-28 00:29
Data Mining Methods for Modeling in Water Scienceff and semi-treated sanitary/industrial sewage discharge. Therefore, artificial intelligence (AI) techniques are used to decrease model development costs and improve prediction errors, achieving more efficient models. In this chapter, some well-known techniques and AI-based methods are introduced, a作者: Charade 時(shí)間: 2025-3-28 02:29 作者: Halfhearted 時(shí)間: 2025-3-28 07:57
Deep Learning Application in Water and Environmental Sciencesn appropriate model, particularly in modeling large (big) and complex datasets. This chapter provides a review of deep learning concepts, introduce some of the developed deep learning structures, and their application in water and environmental studies.作者: Mercurial 時(shí)間: 2025-3-28 13:08
Support Vector Machine Applications in Water and Environmental Sciencesls concept and its application in water and environmental sciences. Furthermore, this chapter introduces different types of SVM models and other emerging ones. Finally, the challenges of this method for future studies will be discussed.作者: 彩色的蠟筆 時(shí)間: 2025-3-28 18:24
Application of Artificial Neural Network and Fuzzy Logic in the Urban Water Distribution Networks Pient and statistical models, includes Radial Based Function Neural Network (RBFNN), Adaptive Neuro Fuzzy Inference System (ANFIS), Feedforward Neural Network (FFNN), Linear Regression (LR), Generalized Linear Regression (GLR), and Support Vector Regression (SVR). An example of a real urban water dist作者: 野蠻 時(shí)間: 2025-3-28 20:55
Parallel Chaos Search Based Incremental Extreme Learning Machine Based Empirical Wavelet Transform: t mean square error (RMSE), and the mean absolute error (MAE). Comparison between simple and hybrid models reveals the importance of the signal decomposition in improving the models performances and also it was demonstrated that the models performances decreased by the increase of the forecasting ti作者: 最高峰 時(shí)間: 2025-3-29 01:34 作者: mutineer 時(shí)間: 2025-3-29 03:43 作者: 恃強(qiáng)凌弱的人 時(shí)間: 2025-3-29 09:13
Strategic delegation in the trade union, introduced as well. Additionally, the Antlion Optimization (ALO) algorithm is represented as a single objective algorithm of the Ant family. Finally, a Multi-objective Antlion Optimization (MOALO) algorithm and its pseudo code are suggested to put forward the implementation of this algorithm.作者: 無(wú)政府主義者 時(shí)間: 2025-3-29 13:38 作者: Obvious 時(shí)間: 2025-3-29 18:03
Ant Colony Optimization Algorithms: Introductory Steps to Understanding introduced as well. Additionally, the Antlion Optimization (ALO) algorithm is represented as a single objective algorithm of the Ant family. Finally, a Multi-objective Antlion Optimization (MOALO) algorithm and its pseudo code are suggested to put forward the implementation of this algorithm.作者: Sarcoma 時(shí)間: 2025-3-29 22:54
Genetic Programming (GP): An Introduction and Practical Applicationcedure, and the computational steps of GP algorithm are detailed. Moreover, several main steps of problem-solving in GP process explained. Finally, a pseudo code of GP algorithm is also stated to demonstrate the implementation of this technique.作者: consent 時(shí)間: 2025-3-30 00:41 作者: 門窗的側(cè)柱 時(shí)間: 2025-3-30 05:12 作者: 世俗 時(shí)間: 2025-3-30 11:53
Space-Strike Arms and International Securitycan be applied to improve optimization algorithms’ performance. This chapter aims to introduce some of these methods and their theory, along with different combination methods to achieve better performance in finding the global solution.作者: subacute 時(shí)間: 2025-3-30 13:25 作者: 鋼筆記下懲罰 時(shí)間: 2025-3-30 20:07 作者: 縮影 時(shí)間: 2025-3-31 00:44 作者: 疼死我了 時(shí)間: 2025-3-31 01:37 作者: isotope 時(shí)間: 2025-3-31 05:20 作者: arrhythmic 時(shí)間: 2025-3-31 10:11
https://doi.org/10.1007/978-1-349-11792-5 (FA), Multi-objective Firefly Algorithm (MOFA), Developed Firefly Algorithm (DFA) and Multi-objective Developed Firefly Algorithm (MODFA) will explain with their concepts and applications in environmental science and water resources fields of research.作者: anesthesia 時(shí)間: 2025-3-31 16:01 作者: 政府 時(shí)間: 2025-3-31 19:01 作者: FLAG 時(shí)間: 2025-4-1 01:06
The Basis of Artificial Neural Network (ANN): Structures, Algorithms and Functions an output pattern/s using the input variables. In this chapter, the neural networks of perceptron and multi-layer perceptron are described. Then, some previous applications and researches of the ANN in water resources are stated. Finally, the ANN code written in the MATLAB environment is presented.作者: MORPH 時(shí)間: 2025-4-1 02:22
Oznur Gulen Ertosun,Zafer Adiguzel basics and basic concepts of classification and then the SVM model are stated. After that, the details and relations of the ruler and the types of functions used in it are discussed. At the end of this chapter, various software applications for this tool will be introduced, and how some of the work will be described.
