Using causal loop diagrams for the initialization of stakeholder engagement in soil salinity management in agricultural watersheds in developing countries: A case study in the Rechna Doab watershed, Pakistan

Inam, Azhar; Adamowski, Jan; Halbe, Johannes; Prasher, Shiv O.

doi:10.1016/j.jenvman.2015.01.052

Cited by 160 publications

(104 citation statements)

References 29 publications

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“…Accurate and reliable UWD forecasting is necessary to help transition to more effective and sustainable urban water resources planning and management [BUTLER, ADAMOWSKI 2015;HALBE et al 2013;INAM et al 2015;KOLINJIVADI et al 2014;STRAITH et al 2014]. In this study, ELM W models based on their capacities of wavelet transformation and ELM modeling techniques were employed to simulate the UWD in the city of Calgary, Canada.…”

Section: Discussionmentioning

confidence: 99%

Water demand forecasting using extreme learning machines

Tiwari¹,

Adamowski²,

Adamowski³

2016

Journal of Water and Land Development

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The capacity of recently-developed extreme learning machine (ELM) modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W) or bootstrap (B) methods (i.e., ELM, ELM W , ELM B ), was assessed, and compared to that of equivalent traditional artificial neural network-based models (i.e., ANN, ANN W , ANN B ). The urban water demand forecasting models were developed using 3-year water demand and climate datasets for the city of Calgary, Alberta, Canada. While the hybrid ELM B and ANN B models provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANN W and ELM W models provided greater accuracy, with the ELM W model outperforming the ANN W model. Significant improvement in peak urban water demand prediction was only achieved with the ELM W model. The superiority of the ELM W model over both the ANN W or ANN B models demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.

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Section: Discussionmentioning

confidence: 99%

Water demand forecasting using extreme learning machines

Tiwari¹,

Adamowski²,

Adamowski³

2016

Journal of Water and Land Development

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“…Data usage in SDMs can range from none for conceptual models that take the form of loop diagrams, e.g., [46], through qualitative empirical data used to derive decision rules [47], to quantitative empirical data to provide input parameters that shape the strength of feedback relationships. Extensive testing of the structure and parameter values of a model can help to build confidence in both assumed feedback structures and their associated rates of change and information flow [42,48].…”

Section: System Dynamics Modellingmentioning

confidence: 99%

Integrating Modelling Approaches for Understanding Telecoupling: Global Food Trade and Local Land Use

Millington

Xiong

Peterson

et al. 2017

Land

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Abstract:The telecoupling framework is an integrated concept that emphasises socioeconomic and environmental interactions between distant places. Viewed through the lens of the telecoupling framework, land use and food consumption are linked across local to global scales by decision-making agents and trade flows. Quantitatively modelling the dynamics of telecoupled systems like this could be achieved using numerous different modelling approaches. For example, previous approaches to modelling global food trade have often used partial equilibrium economic models, whereas recent approaches to representing local land use decision-making have widely used agent-based modelling. System dynamics models are well established for representing aggregated flows and stores of products and values between distant locations. We argue that hybrid computational models will be useful for capitalising on the strengths these different modelling approaches each have for representing the various concepts in the telecoupling framework. However, integrating multiple modelling approaches into hybrid models faces challenges, including data requirements and uncertainty assessment. To help guide the development of hybrid models for investigating sustainability through the telecoupling framework here we examine important representational and modelling considerations in the context of global food trade and local land use. We report on the development of our own model that incorporates multiple modelling approaches in a modular approach to negotiate the trade-offs between ideal representation and modelling resource constraints. In this initial modelling our focus is on land use and food trade in and between USA, China and Brazil, but also accounting for the rest of the world. We discuss the challenges of integrating multiple modelling approaches to enable analysis of agents, flows, and feedbacks in the telecoupled system. Our analysis indicates differences in representation of agency are possible and should be expected in integrated models. Questions about telecoupling dynamics should be the primary driver in selecting modelling approaches, tempered by resource availability. There is also a need to identify appropriate modelling assessment and analysis tools and learn from their application in other domains.

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“…This is being driven by the expectation that the probabilities of floods and their consequences, caused by changes in the meteorological drivers of floods, or by changing land-use patterns and socio-economic development, will continue to rise in the coming decades [2][3][4][5][6][7][8][9][10][11][12], therefore aggravating existing flood risk. In this light, the concept of managing flood risk has shifted towards exploring more comprehensive and sustainable approaches [13][14][15][16]. This change has been guided by international initiatives and recent legislation at a European level; the European Floods Directive 2007/60/EC [17], is one example.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Strategies for Sustainable EU Flood Risk Management: FOSS and Geospatial Tools—Challenges and Opportunities for Operative Risk Analysis

Albano

Mancusi

Sole

et al. 2015

IJGI

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An analysis of global statistics shows a substantial increase in flood damage over the past few decades. Moreover, it is expected that flood risk will continue to rise due to the combined effect of increasing numbers of people and economic assets in risk-prone areas and the effects of climate change. In order to mitigate the impact of natural hazards on European economies and societies, improved risk assessment, and management needs to be pursued. With the recent transition to a more risk-based approach in European flood management policy, flood analysis models have become an important part of flood risk management (FRM). In this context, free and open-source (FOSS) geospatial models provide better and more complete information to stakeholders regarding their compliance with the Flood Directive (2007/60/EC) for effective and collaborative FRM. A geospatial model is an essential tool to address the European challenge for comprehensive and sustainable FRM because it allows for the use of integrated social and economic quantitative risk outcomes in a spatio-temporal domain. Moreover, a FOSS model can support governance processes using an interactive, transparent and collaborative approach, providing a meaningful experience OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 2705 that both promotes learning and generates knowledge through a process of guided discovery regarding flood risk management. This article aims to organize the available knowledge and characteristics of the methods available to give operational recommendations and principles that can support authorities, local entities, and the stakeholders involved in decision-making with regard to flood risk management in their compliance with the Floods Directive (2007/60/EC).

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Using causal loop diagrams for the initialization of stakeholder engagement in soil salinity management in agricultural watersheds in developing countries: A case study in the Rechna Doab watershed, Pakistan

Cited by 160 publications

References 29 publications

Water demand forecasting using extreme learning machines

Water demand forecasting using extreme learning machines

Integrating Modelling Approaches for Understanding Telecoupling: Global Food Trade and Local Land Use

Collaborative Strategies for Sustainable EU Flood Risk Management: FOSS and Geospatial Tools—Challenges and Opportunities for Operative Risk Analysis

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