Interpretable fuzzy systems for monthly rainfall spatial interpolation and time series prediction

This thesis proposes methodologies to analyze and establish interpretable fuzzy systems for monthly rainfall spatial interpolation and time series prediction. A fuzzy system has been selected due to its capability of handling the uncertainty in the data and due to its interpretability characteristic. In the first part, this thesis proposes a methodology to analyze and establish interpretable fuzzy models for monthly rainfall spatial interpolation using global and local methods. In the global method, the proposed methodology begins with clustering analysis to de-termine the appropriate number of clusters, and fuzzy modeling and a genetic algorithm are then used to establish the fuzzy interpretation model. In the local method, the modu-lar technique has been applied to improve the accuracy of the global models while the interpretability capability of the model is maintained. In the second part, this thesis proposes a methodology to establish single and modular interpretable fuzzy models for monthly rainfall time series predictions. In the single model, the cooperative neuro-fuzzy technique and a genetic algorithm have been used. In the modular model, the modular technique has been applied to simplify the complexi-ty of the single model. The whole system is decomposed into twelve sub-modules ac-cording to the calendar months. The proposed modular model consists of two function-ally consecutive layers, the prediction layer and the aggregation layer. In the aggregation layer, Bayesian reasoning has been applied. The case study used in this thesis is located in the northeast region of Thailand. The proposed models were compared with commonly-used conventional and intelligent methods in the hydrological discipline. The experimental results showed that, in the quantitative aspect, the proposed models can provide good prediction accuracy and, in the qualitative aspect, the proposed models can also meet the criteria used for model in-terpretability assessment

The use of remote sensing to evaluate and detect desert regions

Die Fernerkundung spielt eine signifikante Rolle bei der Bereitstellung von aktuellen Daten zur Schätzung von empirischen Indizes bei Untersuchungen der Umwelt, insbesondere in Trockengebieten. Spektral- und thermische Kanäle in Satellitenbildern werden auch zur Berechnung von Indizes verwendet, um natürliche Phänomene in Trockengebieten – wie etwa Bodendegradation und Desertifikation – aufzuspüren, zu bestimmen und zu evaluieren. In dieser Arbeit wurden zur Identifikation von Desertifikation in der Kashan-Qom Region im Zentraliran fünf Desertifikationsindikatoren verwendet: Vegetation, Oberflächentemperatur, Erosion, Trockenheit und Überflutungen. Diese Indikatoren wurden dargestellt mit Hilfe von: Vegetationsindex (VCI), Temperaturindex (TCI), Revidierte Universelle Bodenverlustgleichung (RUSLE), standardisierter Niederschlagsindex (SPI) und Abfluss. Multispektrale Bilder des MODIS Satelliten wurden für die Berechnung von VCI und TCI herangezogen. Des Weiteren wurden RUSLE, SPI und Abfluss bestimmt. Schließlich wurden mehrere Desertifikationskarten anhand von zwei Modellen – einem konventionellen Modell und einem unscharfen Modell – erstellt. Die Ergebnisse der Modelle wurden mit Hilfe von Feldproben und der Erstellung einer Fehlermatrix analysiert. Im unscharfen Modell wurde ein regelbasiertes System aufgrund von Expertenwissen und einer induktiven datengetriebenen Methode erstellt. Obwohl das unscharfe Modell weniger genau als die konventionelle Methode ist, zeigt es die Unbestimmtheit in den Desertifikationsklassen der erstellten Karten.Remote sensing plays a significant role in providing up-to-date data for the estimating of empirical indices in studying the environment, especially in drylands. The spectral and thermal bands in satellite images are also applied to calculate the indices to detect, identify, and evaluate the natural phenomena in drylands such as land degradation and desertification. In this project, for the identification of desertification in the Kashan-Qom region in Central Iran, five main indicators of desertification are used as follows: vegetation, land surface temperature, erosion, drought, and flooding; therefore, these indices are selected as Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Revised Universal Soil Loss Equation (RUSLE), and Standardized Precipitation Index (SPI), and runoff (Q), respectively. The multi-spectral satellite images of MODIS are used for the calculation of remotely sensed indices such as Vegetation Condition Index (VCI) and Temperature Condition Index (TCI). Furthermore, the ancillary data-based indices, Revised Universal Soil Loss Equation (RUSLE), and Standardized Precipitation Index (SPI), and runoff (Q), are also estimated. Then several desertification maps are produced in two models: conventional method and fuzzy model. The result of each model is also evaluated, that is, the results are assessed by the supplying of field sampling as ground truth references and the defining of error matrix. In the fuzzy modelling, a rule-based system is built by expert knowledge and data-induction method. According to the obtained results, even though the accuracy of the fuzzy model is lower than the conventional method, the fuzzy model represents the uncertainty in the classes of resulted desertification by providing a map for each class

Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture

Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and temporal resolution. This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation data of our times and introduces novel machine learning and big data methods to properly exploit them towards developing applications for sustainable and resilient agriculture. The thesis addresses three distinct thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP), the monitoring of food security and applications for smart and resilient agriculture. The methodological innovations of the developments related to the three thematic areas address the following issues: i) the processing of big Earth Observation (EO) data, ii) the scarcity of annotated data for machine learning model training and iii) the gap between machine learning outputs and actionable advice. This thesis demonstrated how big data technologies such as data cubes, distributed learning, linked open data and semantic enrichment can be used to exploit the data deluge and extract knowledge to address real user needs. Furthermore, this thesis argues for the importance of semi-supervised and unsupervised machine learning models that circumvent the ever-present challenge of scarce annotations and thus allow for model generalization in space and time. Specifically, it is shown how merely few ground truth data are needed to generate high quality crop type maps and crop phenology estimations. Finally, this thesis argues there is considerable distance in value between model inferences and decision making in real-world scenarios and thereby showcases the power of causal and interpretable machine learning in bridging this gap.Comment: Phd thesi

Advances in Modelling of Rainfall Fields

Rainfall is the main input for all hydrological models, such as rainfall–runoff models and the forecasting of landslides triggered by precipitation, with its comprehension being clearly essential for effective water resource management as well. The need to improve the modeling of rainfall fields constitutes a key aspect both for efficiently realizing early warning systems and for carrying out analyses of future scenarios related to occurrences and magnitudes for all induced phenomena. The aim of this Special Issue was hence to provide a collection of innovative contributions for rainfall modeling, focusing on hydrological scales and a context of climate changes. We believe that the contribution from the latest research outcomes presented in this Special Issue can shed novel insights on the comprehension of the hydrological cycle and all the phenomena that are a direct consequence of rainfall. Moreover, all these proposed papers can clearly constitute a valid base of knowledge for improving specific key aspects of rainfall modeling, mainly concerning climate change and how it induces modifications in properties such as magnitude, frequency, duration, and the spatial extension of different types of rainfall fields. The goal should also consider providing useful tools to practitioners for quantifying important design metrics in transient hydrological contexts (quantiles of assigned frequency, hazard functions, intensity–duration–frequency curves, etc.)

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Novel Approaches in Landslide Monitoring and Data Analysis

Significant progress has been made in the last few years that has expanded the knowledge of landslide processes. It is, therefore, necessary to summarize, share and disseminate the latest knowledge and expertise. This Special Issue brings together novel research focused on landslide monitoring, modelling and data analysis

Crop suitability mapping for underutilized crops in South Africa.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Several neglected and underutilised species (NUS) provide solutions to climate change and create a Zero Hunger world, the Sustainable Development Goal 2. However, limited information describing their agronomy, water use, and evaluation of potential growing zones to improve sustainable production has previously been cited as the bottlenecks to their promotion in South Africa's (SA) marginal areas. Therefore, the thesis outlines a series of assessments aimed at fitting NUS in the dryland farming systems of SA. The study successfully mapped current and possible future suitable zones for NUS in South Africa. Initially, the study conducted a scoping review of land suitability methods. After that, South African bioclimatic zones with high rainfall variability and water scarcity were mapped. Using the analytic hierarchy process (AHP), the suitability for selected NUS sorghum (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth and taro (Colocasia esculenta) was mapped. The future growing zones were used using the MaxEnt model. This was only done for KwaZulu Natal. Lastly, the study assessed management strategies such as optimum planting date, plant density, row spacing, and fertiliser inputs for sorghum. The review classified LSA methods reported in articles as traditional (26.6%) and modern (63.4%). Modern approaches, including multicriteria decision-making (MCDM) methods such as AHP (14.9%) and fuzzy methods (12.9%), crop simulation models (9.9%) and machine-learning-related methods (25.7%), are gaining popularity over traditional methods. The review provided the basis and justification for land suitability analysis (LSA) methods to map potential growing zones of NUS. The review concluded that there is no consensus on the most robust method for assessing NUS's current and future suitability. South Africa is a water-scarce country, and rainfall is undoubtedly the dominating factor determining crop production, especially in marginal areas where irrigation facilities are limited for smallholder farmers. Based on these challenges, there is a need to characterise bioclimatic zones in SA that can be qualified under water stress and with high rainfall variation. Mapping high-risk agricultural drought areas were achieved by using the Vegetation Drought Response Index (VegDRI), a hybrid drought index that integrates the Standardized Precipitation Index (SPI), Temperature Condition Index (TCI), and the Vegetation Condition Index (VCI). In NUS production, land use and land classification address questions such as “where”, “why”, and “when” a particular crop is grown within particular agroecology. The study mapped the current and future suitable zones for NUS. The current land suitability assessment was done using Analytic Hierarchy Process (AHP) using multidisciplinary factors, and the future was done through a machine learning model Maxent. The maps developed can contribute to evidence-based and site-specific recommendations for NUS and their mainstreaming. Several NUS are hypothesised to be suitable in dry regions, but the future suitability remains unknown. The future distribution of NUS was modelled based on three representative concentration pathways (RCPs 2.6, 4.5 and 8.5) for the years between 2030 and 2070 using the maximum entropy (MaxEnt) model. The analysis showed a 4.2-25% increase under S1-S3 for sorghum, cowpea, and amaranth growing areas from 2030 to 2070. Across all RCPs, taro is predicted to decrease by 0.3-18 % under S3 from 2050 to 2070 for all three RCPs. Finally, the crop model was used to integrate genotype, environment and management to develop one of the NUS-sorghum production guidelines in KwaZulu-Natal, South Africa. Best sorghum management practices were identified using the Sensitivity Analysis and generalised likelihood uncertainty estimation (GLUE) tools in DSSAT. The best sorghum management is identified by an optimisation procedure that selects the optimum sowing time and planting density-targeting 51,100, 68,200, 102,500, 205,000 and 300 000 plants ha-1 and fertiliser application rate (75 and 100 kg ha-1) with maximum long-term mean yield. The NUS are suitable for drought-prone areas, making them ideal for marginalised farming systems to enhance food and nutrition security

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed