This article aims to develop a Web-GIS based landslide early warning system (EWS) for the Chittagong Metropolitan Area (CMA), Bangladesh, where, in recent years, rainfall-induced landslides have caused great losses of lives and property. A method for combining static landslide susceptibility maps and rainfall thresholds is proposed by introducing a purposely-build hazard matrix. To begin with, eleven factor maps: soil permeability; surface geology; landcover; altitude; slope; aspect; distance to stream; fault line; hill cut; road cut; and drainage network along with a detailed landslide inventory map were produced. These maps were used, and four methods were applied: artificial neural network (ANN); multiple regressions; principal component analysis; and support vector machine to produce landslide susceptibility maps. After model validation, the ANN map was found best fitting and was classified into never warning, low, medium, and high susceptibility zones. Rainfall threshold analysis (1960–2017) revealed consecutive 5-day periods of rainfall of 71–282 mm could initiate landslides in CMA. Later, the threshold was classified into three rainfall rates: low rainfall (70–160 mm), medium rainfall (161–250 mm), and high rainfall (>250 mm). Each landslide was associated with a hazard class (no warning vs. warning state) based on the assumption that the higher the susceptibility, the lower the rainfall. Finally, the EWS was developed using various libraries and frameworks that is connected with a reliable online-based weather application programming interface. The system is publicly available, dynamic, and replicable to similar contexts and is able to disseminate alerts five days in advance via email notifications. The proposed EWS is novel and the first of its kind in Bangladesh, and can be applied to mitigate landslide disaster risks.

This article aims to develop a Web-GIS based landslide early warning system (EWS) for Chittagong Metropolitan Area (CMA), Bangladesh where in recent years landslides have caused great losses to lives and properties. The main triggering factors for landslides were torrential rainfalls, hill cutting and forest destruction. Eleven factor maps – soil permeability, surface geology, landcover, altitude, slope, aspect; and distance to stream, fault line, hill cut, road cut, and drainage network – along with a detailed landslide inventory map were produced. For modeling methods: artificial neural network (ANN), multiple regressions, principal component analysis and support vector machine methods were applied to produce landslide susceptibility maps. After model validation, the best-fit ANN map was classified as never warning, low, medium, and high susceptibility zones. Rainfall threshold analysis (1960-2017) revealed consecutive 5-day rainfall between 70-160 mm could initiate landslides in high susceptibility zone. The EWS was developed using various libraries and frameworks – MySQL, PHP, JavaScript, OpenLayers, W2UI, and jQuery along with HTML5 and CSS. The system is publicly available, dynamic, replicable in a similar context and is able to disseminate alerts five days in advance via email notifications. The EWS is original and first of its kind in Bangladesh which can be used to mitigate landslide disaster risks.