Cargando…

Water resource modeling and computational technologies /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Zakwan, Mohammad (Editor ), Wahid, Abdul (Editor ), Niazkar, Majid (Editor ), Chatterjee, Uday (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam, Netherlands : Elsevier, 2022.
Colección:Current directions in water scarcity research ; v. 7.
Temas:
Acceso en línea:Texto completo

MARC

LEADER 00000cam a2200000 i 4500
001 SCIDIR_on1348884630
003 OCoLC
005 20231120010709.0
006 m o d
007 cr cnu---unuuu
008 221026s2022 ne o 001 0 eng d
040 |a OPELS  |b eng  |e rda  |e pn  |c OPELS  |d EBLCP  |d OCLCF  |d UKAHL  |d OCLCQ  |d UKMGB  |d OCLCO 
015 |a GBC2E1603  |2 bnb 
016 7 |a 020710488  |2 Uk 
019 |a 1355092927 
020 |z 9780323919104  |q (print) 
020 |a 9780323985178  |q (ePub ebook) 
020 |a 0323985173 
035 |a (OCoLC)1348884630  |z (OCoLC)1355092927 
050 4 |a TD353 
082 0 4 |a 363.6/1015118  |2 23 
245 0 0 |a Water resource modeling and computational technologies /  |c edited by Mohammad Zakwan, Abdul Wahid, Majid Niazkar, Uday Chatterjee. 
264 1 |a Amsterdam, Netherlands :  |b Elsevier,  |c 2022. 
300 |a 1 online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Current directions in water scarcity research ;  |v volume 7 
500 |a Includes index. 
588 0 |a Online resource; title from PDF title page (ScienceDirect, viewed October 26, 2022). 
505 0 |a Intro -- Water Resource Modeling and Computational Technologies -- Copyright -- Contents -- Contributors -- About the editors -- Foreword -- Preface -- Acknowledgments -- Section I: Introduction -- Chapter 1: Artificial intelligence and machine learning in water resources engineering -- 1. Introduction -- 2. Materials and methods -- 2.1. Selection of search terms -- 2.2. Scientometric review -- 3. Evolution of artificial intelligence and machine learning -- 4. Results and discussion -- 5. Conclusion -- References -- Section II: Application of artificial intelligence to water resources 
505 8 |a Chapter 2: Demystifying artificial intelligence amidst sustainable agricultural water management -- 1. Introduction -- 1.1. Review objectives and chapter organization -- 2. AI in agriculture -- 2.1. AI in preagricultural (preparatory) activities -- 2.1.1. Case studies: ``Agri-e-calculator and sowing app�� -- 2.2. AI during agricultural activities -- 2.3. AI in postagricultural activities -- 3. Current and future scope in AI for agriculture -- 4. Challenges of AI in agriculture -- 5. Conclusions -- Acknowledgments -- Conflict of interest -- References -- Further reading 
505 8 |a Chapter 3: Bidirectional long short-term memory-based empirical wavelet transform: A new hybrid artificial -- 1. Introduction -- 2. Materials and methods -- 2.1. Study site and data used -- 2.2. Performance assessment of the models -- 2.3. Methodology -- 2.3.1. Bidirectional long short term memory (BiLSTM) -- 2.3.2. Gaussian process regression (GPR) -- 2.3.3. Support vector regression (SVR) -- 2.3.4. Empirical wavelet transform (EWT) -- 3. Results and discussion -- 4. Conclusions -- 5. Recommendations -- References 
505 8 |a Chapter 4: Fuzzy logic modeling of groundwater potential in Marinduque, Philippines -- 1. Introduction -- 2. Material and methods -- 2.1. Study site -- 2.2. Data -- 2.3. Groundwater potential mapping using fuzzy logic -- 2.3.1. Identification of membership function -- 2.3.2. Determination of aggregation function -- 2.3.3. Calculation of the performance metrics of the fuzzy aggregation functions -- 3. Results -- 4. Discussion -- 5. Conclusion -- References -- Chapter 5: Soft-computing approach to scour depth prediction under wall jets -- 1. Introduction -- 2. Materials and methods 
505 8 |a 2.1. Effect of various parameters on equilibrium depth of scour -- 2.2. Existing prediction equations for maximum scour depth -- 3. Results and discussion -- 3.1. Statistical error analysis -- 3.2. Artificial neural network (ANN) model -- 3.3. Adaptive neuro-fuzzy interference system (ANFIS) model -- 4. Conclusions -- References -- Section III: Image processing applications in water resources -- Chapter 6: Assessment of water resources using remote sensing and GIS techniques -- 1. Introduction -- 2. Remote sensing and GIS: Tools for sustainability of water resources 
650 0 |a Water-supply  |x Mathematical models. 
650 0 |a Water-supply  |x Data processing. 
650 6 |a Eau  |0 (CaQQLa)201-0007151  |x Approvisionnement  |0 (CaQQLa)201-0007151  |x Mod�eles math�ematiques.  |0 (CaQQLa)201-0379082 
650 6 |a Eau  |0 (CaQQLa)201-0007151  |x Approvisionnement  |0 (CaQQLa)201-0007151  |x Informatique.  |0 (CaQQLa)201-0380011 
650 7 |a Water-supply  |x Data processing  |2 fast  |0 (OCoLC)fst01172367 
650 7 |a Water-supply  |x Mathematical models  |2 fast  |0 (OCoLC)fst01172403 
700 1 |a Zakwan, Mohammad,  |e editor. 
700 1 |a Wahid, Abdul,  |e editor. 
700 1 |a Niazkar, Majid,  |e editor. 
700 1 |a Chatterjee, Uday,  |e editor. 
776 0 8 |i Print version :  |z 9780323919104 
830 0 |a Current directions in water scarcity research ;  |v v. 7. 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/bookseries/25427946/7  |z Texto completo