Optimisation of Dynamic Heterogeneous Rainfall Sensor Networks in the Context of Citizen Observatories

1. Introduction 
1.1 Background 
1.2 Motivation 
1.3 Innovation 
1.4 Objectives 
1.5 Layout of this thesis 
1.6 Highlights 
2. Literature review and proposed framework 
2.1 Introduction 
2.2 Sensors and sensor networks 
2.3 Models of precipitation for rainfall-runoff simulation 
2.4 Simulation of Rainfall-runoff processes using lumped conceptual models 
2.5 Classification of approaches for sensor network evaluation 
2.6 Proposed framework for sensor network design 
2.7 Conclusions 
3. Case studies 
3.1 Introduction 
3.2 Bacchiglione River 
3.3 Brue Catchment 
3.4 Don River 
4. Advancing Kriging methods for merging heterogeneous data sources in nonstationary precipitation fields 
4.1 Introduction 
4.2 Dealing with data of variable measurement uncertainty 
4.3 Estimating uncertainty due to partial recording 
4.4 Handling Non-stationarity in the kriging framework 
4.5 Application in the Brue Catchment 
4.6 Conclusions 
5. Optimisation of static precipitation sensor networks and robustness analysis 
5.1 Introduction 
5.2 Formulation of decision variable encoding 
5.3 Selection of decision variable encoding and of optimisation algorithm 
5.4 Exploring relationships between various objective functions 
5.5 Solving the optimal design problem for the selected objective functions 
5.6 Analysis of robustness 
5.7 Conclusions 
6. Optimisation of dynamic precipitation sensor networks 
6.1 Introduction 
6.2 Posing the optimisation problem 
6.3 Objective functions and corresponding strategies for deployment 
6.4 Experimental setup and solution of the optimisation problem 
6.5 Results and discussion 
6.6 Conclusions 
7. Conclusions and recommendations 
7.1 Summary 
7.2 Conclusions 
7.3 Limitations 
7.4 Outlook and recommendations 
Bibliography 
ANNEX 1. Overview of candidate algorithms for sensor network optimisation 
ANNEX 2. Hydrological models used for the Brue catchment
ANNEX 3. Perturbation specification for simulating incomplete precipitation data