Much has been written about theory and practice in the law, and the tension between practitioners and theorists. Judges do not cite theoretical articles often; they rarely "apply" theories to particular cases. These arguments are not revisited. Instead the Essay explores the working and interaction of theory and practice, practitioners and theorists. The Essay starts with a story about solving a legal issue using our intellectual tools - theory, practice, and their progenies: experience and "gut." Next the Essay elaborates on the nature of theory, practice, experience and "gut." The third part of the Essay discusses theories that are helpful to practitioners and those that are less helpful. The Essay concludes that practitioners theorize, and theorists practice. They use these intellectual tools differently because the goals and orientations of theorists and practitioners, and the constraints under which they act, differ. Theory, practice, experience and "gut" help us think, remember, decide and create. They complement each other like the two sides of the same coin: distinct but inseparable.

Of Theory and Practice

The flooding in Bangladesh during monsoon season is very common and frequently happens. Consequently, people have been experiencing tremendous damage to properties, infrastructures, and human casualties. Usually, floods are one of the devastating disasters from nature, but for developing nations like Bangladesh, flooding becomes worse. Due to the dynamic and complex nature of the flooding, the prediction of flooding sites was usually very difficult for flood management. But the artificial intelligence and advanced remote sensing techniques together could predict and identify the possible sites, which are vulnerable to flooding. The present work aimed to predict and identify the flooding sites or flood susceptible zones in the Teesta River basin by employing state-of-the-art novel ensemble machine learning algorithms. We developed ensembles of bagging with REPtree, random forest (RF), M5P, and random tree (RT) algorithms for obtaining reliable and highly accurate results. Twelve factors, which are considered as the conditioning factors, and 413 current and former flooding points were identified for flooding susceptibility modelling. The Information Gain ratio statistical technique was utilized to determine the influence of the factors for flooding. We applied receiver operating characteristic curve (ROC) for validation of the flood susceptible models. The Freidman test, Wilcoxon signed-rank test, Kruskal–Wallis test and Kolmogorov–Smirnov test were applied together for the first time in flood susceptibility modelling to compare the models with each other. Results showed that more than 800 km2 area was predicted as the very high flood susceptibility zones by all algorithms. The ROC curve showed that all models achieved more than 0.85 area under the curve indicating highly accurate flood models. For flood susceptibility modelling, the bagging with M5P performed superior, followed by bagging with RF, bagging with REPtree and bagging with RT. The methodology and solution-oriented results presented in this paper will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate measures to avoid potential damages.

Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms

More than 6% of Switzerland is prone to slope instability. New federal regulations require regional authorities (cantons) to generate natural hazard maps and the zoning of mass movements to restrict development on hazard-prone land. The paper discusses the proposed three-step procedure of hazard identification, hazard assessment and risk management. The Codes of Practice concerning the hazard maps involve the standard use of three col- ours (red, blue and yellow) to indicate areas of prohibited construction, construction with certain safety requirements and construction without re- striction. ResumePlus de 6% du territoire suisse est soumis a des phenomenes d'instabilites de terrain. De nouv- elles bases legales exigent des autoritesr egionales l'etablissement de cartes de danger et le zonage pour les mouvements de terrain afin de restreindre le developpement dans les zones sensibles. Une pro- cedure en trois etapes comprend l'identification du danger, l'evaluation du danger et la gestion du ris- que. L'application dans l'amenagement de ces cartes de danger peut etre resumee ainsi: dans les zones rouges les constructions sont interdites, dans les zones bleues les constructions sont autorisees lors- que des prescriptions techniques de securitesont respectees et dans les zones jaunes les constructions sont autorisees.

Hazard assessment in Switzerland - Codes of Practice for mass

This paper combines research and commentary to reinforce the importance of integrating hazard interactions and interaction networks (cascades) into multi-hazard methodologies. We present a synthesis of the differences between multi-layer single-hazard approaches and multi-hazard approaches that integrate such interactions. This synthesis suggests that ignoring interactions between important environmental and anthropogenic processes could distort management priorities, increase vulnerability to other spatially relevant hazards or underestimate disaster risk. In this paper we proceed to present an enhanced multi-hazard framework through the following steps: (i) description and definition of three groups (natural hazards, anthropogenic processes and technological hazards/disasters) as relevant components of a multi-hazard environment, (ii) outlining of three types of interaction relationship (triggering, increased probability, and catalysis/impedance), and (iii) assessment of the importance of networks of interactions (cascades) through case study examples (based on the literature, field observations and semi-structured interviews). We further propose two visualisation frameworks to represent these networks of interactions: hazard interaction matrices and hazard/process flow diagrams. Our approach reinforces the importance of integrating interactions between different aspects of the Earth system, together with human activity, into enhanced multi-hazard methodologies. Multi-hazard approaches support the holistic assessment of hazard potential and consequently disaster risk. We conclude by describing three ways by which understanding networks of interactions contributes to the theoretical and practical understanding of hazards, disaster risk reduction and Earth system management. Understanding interactions and interaction networks helps us to better (i) model the observed reality of disaster events, (ii) constrain potential changes in physical and social vulnerability between successive hazards, and (iii) prioritise resource allocation for mitigation and disaster risk reduction.

Hazard Interactions and Interaction Networks (Cascades) within Multi-Hazard Methodologies

Modelling multi-hazard threats to cultural heritage sites and environmental sustainability: The present and future scenarios

This study sought to produce an accurate multi-hazard risk map for a mountainous region of Iran. The study area is in southwestern Iran. The region has experienced numerous extreme natural events in recent decades. This study models the probabilities of snow avalanches, landslides, wildfires, land subsidence, and floods using machine learning models that include support vector machine (SVM), boosted regression tree (BRT), and generalized linear model (GLM). Climatic, topographic, geological, social, and morphological factors were the main input variables used. The data were obtained from several sources. The accuracies of GLM, SVM, and functional discriminant analysis (FDA) models indicate that SVM is the most accurate for predicting landslides, land subsidence, and flood hazards in the study area. GLM is the best algorithm for wildfire mapping, and FDA is the most accurate model for predicting snow avalanche risk. The values of AUC (area under curve) for all five hazards using the best models are greater than 0.8, demonstrating that the model's predictive abilities are acceptable. A machine learning approach can prove to be very useful tool for hazard management and disaster mitigation, particularly for multi-hazard modeling. The predictive maps produce valuable baselines for risk management in the study area, providing evidence to manage future human interaction with hazards.

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A machine learning framework for multi-hazards modeling and mapping in a mountainous area.

Catastrophic floods cause deaths, injuries, and property damages in communities around the world. The losses can be worse among those who are more vulnerable to exposure and this can be enhanced by communities' vulnerabilities. People in undeveloped and developing countries, like Iran, are more vulnerable and may be more exposed to flood hazards. In this study we investigate the vulnerabilities of 1622 schools to flood hazard in Chaharmahal and Bakhtiari Province, Iran. We used four machine learning models to produce flood susceptibility maps. The analytic hierarchy process method was enhanced with distance from schools to create a school-focused flood-risk map. The results indicate that 492 rural schools and 147 urban schools are in very high-risk locations. Furthermore, 54% of rural students and 8% of urban students study schools in locations of very high flood risk. The situation should be examined very closely and mitigating actions are urgently needed.

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Assessing the susceptibility of schools to flood events in Iran.

Mass movements are among the most dangerous natural hazards in mountainous regions. The present study employs machine learning (ML) models for mass movement susceptibility mapping (MMSM) in Iran based on a comprehensive dataset of 864 mass movements which include debris flow, landslide, and rockfall during the last 42 years (1977–2019) as well as 12 conditional factors. The results of validation stage show that RF (random forest) is the most viable model for mass movement susceptibility maps. In addition, MARS (multivariate adaptive regression splines), MDA (mixture discriminant additive), and BRT (boosted regression trees) models also provide relatively accurate results. Results of the AUC for validation of produced maps were 0.968, 0.845, 0.828, and 0.765 for RF, MARS, MDA, and BRT, respectively. Based on MMSM generated by RF model, 32% of study area is identified to be under high and very high susceptibility classes. Most of the endangered areas for mass movement are in the west and central parts of the Chaharmahal and Bakhtiari Province. In addition, our findings indicate that elevation, slope angle, distance from roads, and distance from faults are critical factors for mass movement. Our results provide a perspective view for decision makers to mitigate natural hazards.

A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran)

Soil erosion is a serious threat to human well-being and life. Gully erosion is an important process of soil degradation that causes significant soil loss in different climates. Many researchers believe that the reason for paying attention to gully erosion is the high volume of this type of erosion compared to other types of erosion, and gully erosion produces several times more sediment. The soil that is eroded by the gully causes great economic losses by sedimentation in waterways, dam reservoirs, and port areas. The main aim of the present study was to identify the most important factors affecting gully erosion development in the Chaharmahal and Bakhtiari provinces of Iran and to obtain appropriate solutions to control this type of erosion. Areas affected by gully erosion in the study area were identified using field visits. Using the geographical location of identified gullies, a map of the spatial distribution of areas affected by gully erosion in the study area was prepared. Finally, among the identified gullies, 24 active and reagent gullies were selected for further studies. Gully volume as a dependent variable and factors such as sodium ratio, percentage of soluble salts, percentages of clay, silt, and sand in gully areas, gully floor slopes, gully upstream slopes, mean rainfall, and vegetation density as independent variables were considered. The measured parameters were statistically analyzed using SPSS software with a multiple regression relationship. The results showed that the three factors of the gully upstream slope, the percentage of soluble salts in the soil, and the density of vegetation with a coefficient of determination of 0.94 had the greatest effect on gully development in the study area. The slope of the watershed above the gully forehead (gully upstream slope) alone accounts for 86% of the changes in gully erosion volume. The main reason for this result can be attributed to the flooding of this factor and the speed of surface runoff.

Sayed Naeim Emami

Papers

A machine learning framework for multi-hazards modeling and mapping in a mountainous area.

Assessing the susceptibility of schools to flood events in Iran.

A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran)

Identify the important driving forces on gully erosion, Chaharmahal and Bakhtiari province, Iran