Mapping the customer centric weather index insurance service design using quality function deployment

PurposeThe present paper aims to propose a framework on weather index insurance (WII) service design by using quality function deployment (QFD).Design/methodology/approachThis study utilizes QFD technique to propose a customer oriented framework on WII service design. In initial phase, customer and design requirements were gathered to derive the relationship between customers' and managers' voice for construct the house of quality (HOQ). Later on, prioritized customer and design requirements as QFD outcome were utilized to develop the action plan matrix in order to suggest the future action plans.FindingsThis study proposed a customer centric framework on WII service design to address the customer requirements. Findings show that adequate claim payments, hassle free prompt claim payment and transparency in losses computation are prioritized customer requirements with highest importance rating, whereas, accurate claim estimation, claim management system and advancement of technology are prioritized service design necessities with highest importance rating.Research limitations/implicationsThe proposed WII service design can enhance the quality of WII service by attain the higher standards of WII service in order to completely satisfy the customers.Practical implicationsThe proposed WII service design can provide a solution to the problems faced by WII industry by improve the customer's service experience and satisfaction.Originality/valueBased on best of author's knowledge, this paper first proposed a framework on WII service design by integrating customer and design requirements by using QFD.

PREDICTION OF FOREST FIRE USING NEURAL NETWORKS WITH BACKPROPAGATION LEARNING AND EXREME LEARNING MACHINE APPROACH USING METEOROLOGICAL AND WEATHER INDEX VARIABLES

Forest fire is one of important catastrophic events and have great impact on environment, infrastructure and human life. In this study, we discuss the method for prediction of the size of the forest fire using the hybrid approach between Fuzzy-C-Means clustering (FCM) and Neural Networks (NN) classification with backpropagation learning and extreme learning machine approach. For comparison purpose, we consider a similar hybrid approach, i.e., FCM with the classical Support Vector Machine (SVM) classification approach. In the empirical study, we apply the considered methods using several meteorological and Forest Weather Index (FWI) variables. We found that the best approach will be obtained using hybrid FCM-SVM for data training, where the best performance obtains for hybrid FCM-NN-backpropagation for data testing.

Development of Google Earth Engine Fire Weather Index Calculator for Indonesian Fire Danger Rating System

Tropical peatlands make up almost ten percent of the land surface in Indonesia, making peat fires detrimental not only for global atmospheric carbon levels, but also to public health and socioeconomic activities in the region. Indonesian Fire Danger Rating System (FDRS) was developed based on the Canadian Forest Fire Weather Index System (CFFWIS), using three different fuel codes and three indices representing fire behaviour. Daily Fire Weather Index (FWI) calculation is done by the Meteorological Climatological and Geophysical Agency (BMKG) with data from its synoptic weather stations network. Distribution of such weather stations are sparse, therefore this paper reports on the development of Fire Weather Index calculator on Google Earth Engine, using high resolution weather data, provided by weather model and remote-sensing open datasets. The resulting application is capable of generating daily maps of FWI components to be used by the Indonesian Fire Danger Rating System.

Modelling the barriers of weather index insurance service adoption integrating expert mining and ISM Fuzzy-MICMAC

PurposeThe purpose of this study is to explore and prioritize the barriers that affect weather index-insurance (WII) adoption among customers by utilizing interpretive structural modelling (ISM) and fuzzy-MICMAC.Design/methodology/approachThis paper utilized the combined approach in two phases. In first phase comprehensive literature study and expert mining method have been performed to identify and validate WII adoption barriers. In second phase, ISM has been utilized to examine the direct relationships among WII adoption barriers in order to develop a structural model. Further, fuzzy-MICMAC method has been utilized to analyse indirect relationships among barriers to explore dependence and driver power.FindingsThis study has identified 15 key barriers of WII adoption among customers and developed a structural model based on binary direct relationship using ISM. Later, the outcomes of ISM model have been utilized for analysing the dependence and driver power of each WII adoption barriers in cluster form using fuzzy-MICMAC. The customer awareness related WII adoption barrier are mainly at the top level, WII demand related barriers are in the centre and WII supply related barriers at the bottom level in ISM model.Practical implicationsThe findings offered important insights for WII insurers to understand mutual relationships amongst WII adoption barriers and assists in developing strategy to eliminate dominant key barriers in order to enhance their customer base.Originality/valueBased on best of author's knowledge this paper firstly integrates the ISM fuzzy-MICMAC method into identification and prioritization of barriers that affects WII adoption among customers.

Fire prediction with logistic regression on territory of Bosnia and Herzegovina

The Canadian Fire Weather Index system [1] has been used worldwide by many countries as classic approach in fire prediction. It represents system that account for the effects of fuel moisture and weather conditions on fire behaviour. It numerical outputs are based on calculation of four meteorological elements: air temperature, relative humidity, wind speed and precipitation in last 24h. In this paper meteorological data in combination with Canadian Fire Weather Index system (CFWI) components is used as input to predict fire occurrence using logistic regression model. As logistic regression is a supervised machine learning method it’s based on user input in the form of dataset. Dataset is collected using NASA GES DISC Giovanni web-based application in the form of daily area-averaged time series in period of 31.7.2010 to 31.7.2020, it’s analysed and pre-processed before it is used as input for logit model. CFWI components values are not imported but calculated in run-time based on pre-processed meteorological data. As a result of this research windows application was developed to assist fire managers and all those involved in studying the fire behaviour.