DISASTER ACTION PLAN FOR AN URBAN AREA USING GEO-SPATIAL TOOLS

Abstract

The word disaster has been derived from the French word ‘desastre’ refers to bad and stars. The word disaster refers to catastrophe events, have a severe adverse impact on the human, socio-economy and environment. Every part of the world facing natural, human-made and hybrid disaster. Since 1900, Emergency Events Database (EM-DAT) recorded 22,000 massive disasters around the globe. Although, natural disasters are more calamitous, however, human-made disasters also have a significant contribution to disaster losses. Three parameters, such as, exposure, social and physical vulnerability, may be used to evaluate the effect of disasters. Every year the disaster losses are increasing. The negative impact of disasters is more severe in a densely populated area in comparison to the sparsely populated area. Therefore, various international organisations have focused on disaster preparedness, response and mitigation through location-specific management plans. India is also experiencing an increased number of disasters every year. Almost entire landmass facing various types of disaster threat. In India, fire is the most destructive hazard after flood hazards and has been ranked as the fifth most dangerous threat to the community. The annual average death rate of the Chennai district due to urban fire is 74. Therefore, the disaster action plan has become the necessity to diminish the impact of a fire disaster. The losses due to disaster depend upon urban concentration. Therefore, urban change analysis has been performed to determine the urban growth and prediction for the Chennai district. Landsat 3 Multispectral Scanner (MSS), Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM +) sensors data of the year 1981, 1991, 2001 and 2011 has been used for the temporal change analysis. The Land Use/ Land Cover (LU/LC) map has been prepared using Maximum Likelihood Classifier (MLC). Each classified map has five classes, urban, sand, barren land, vegetation and water class. The user’s, producer’s, overall classification accuracy, kappa statistics as well as F-1 measures have been used for the assessment of classified map accuracy. Further, the Post-image classification comparison technique has been used to evaluate quantitative as well as spatio-temporal change analysis. Further, Pearson's Correlation Coefficient (CC), Transition Potential Matrix (TPM) and Land Use Dynamic Degree (LUDD) land-use change indicators have been used for quantitative change analysis. vi The urban growth of Chennai district has been predicted for the year 2021, 2031, 2041 and 2051 using Cellular Automata-Markov (CA-Markov) model. The Multi-Layer Perceptron-Artificial Neural Network (MLP-ANN) has been used to prepare TPM. The distance parameter (from the road to urban) has been taken into account for urban growth assessment. The model validation has been performed using a simulated and reference map of the year 2011. Moreover, the model performance has been evaluated using Artificial Neural Network (ANN) learning curve. The evaluated optimised parameters have been used for the urban growth prediction of the year 2021, 2031, 2041 and 2051. Moreover, the ward-based urban growth has been analysed to determine urban growth in each ward during the year 1981- 2011 using zonal statistics tool in ArcGIS. The spatial and non-spatial information of emergency resources has been collected to prepare an emergency inventory database. The geospatial location of the hospital, schools, police stations, fire stations, religious places, open spaces and bus depot has been collected and stored. The emergency inventory database may be used in an emergency to provide assistance to diminish the impact of fire hazard. In-polygon search analysis and neighbourhood analysis have been used to determine available emergency facilities in each ward. The road network analysis has been performed to evaluate the characteristics and connectivity using ratio and non-ratio measures. The Open Street Map (OSM) road layer data has been used to prepare the road layer data. Further, the generalization of the road network has been carried out to improve the representation of a road network. Two types of the road layer have been prepared to develop the disaster action plan, vehicle-based road network and pedestrian-based road network. Further, three buildings locations lie in the highest residential, commercial and industrial building area coverage have been determined. Moreover, the risk map has been prepared for each building using buffer analysis and a disaster action plan has been prepared accordingly. The closest emergency facilities analysis has been performed using a network analyst in ArcGIS. The four closest emergency facilities locations have been determined for each fire incident location in a residential, industrial and commercial building. The fire incident location has been denoted for these locations by (Fire point locations in Rmax, Imax and Cmax ward) FP_Rmax, FP_Imax, and FP_Cmax. To evaluate nearest emergency facilities from fire incident locations time and distance impedance has been considered. Moreover, the vehicle-based road network has been vii used for the emergency vehicle movement from its source location to the incident location and vice-versa. Similarly, the pedestrian-based road network has used to evacuate people from hazard exposure locations to the safest local emergency collection point. The closest bus depot has been identified to reallocate people from the location collection point to nearest shelters locations. In case of residential building fire incidents, school and religious places, such as, temple, church, mosque and gurudwara premises have been used. Moreover, the emergency route plan and cost matrix have been evaluated for each movement. In order to reduce traffic congestion, the restriction has been imposed on the pedestrian-based road network. The outcomes of the present study, such as, Correlation Coefficient (CC) shows that the urban growth of Chennai district was very high during 1981-1991. However, it has decreased during 1991-2001 and 2001-2011. CC and the volume of quantitative change are inversely proportional to each other. Further, LUDD shows that the annual rate of change in the urban area is gradually decreasing. Since 1981-2011, the urban area has been increased by 62.78 km2 and vegetation cover has been reduced by 97.02 km2. Further, CA-Markov model prediction shows that the urban area of Chennai district will increase by 140.79 km2 in the year 2051. Thus it can be inferred that concentration and compactness of urban areas are increasing, while vegetation cover is decreasing. The urban growth rate was observed 40.99%, 15.29% and 21.27% during 1981-1991, 1991-2001 and 2001- 2011respectivily. However, the prediction shows that the urban growth rate will be 16.90%, 4.62%, 3.84%and 3.54% during 2011-2021, 2021-2031, 2031-2041 and 2041-2051 respectively. The study concluded that the urban growth trend of Chennai district administrative area having decreased urban growth during 1981-2051. The ward-based urban growth analysis concluded that the wards nearby core city area had been densely urbanised and the new urban development took place at the adjacent area of Chennai district administrative boundary. Based on the road network analysis, it has been concluded that the vehicle-based road network is entirely interconnected. The vehicle-based road network pattern has a delta road network pattern. It may provide various sets of the shortest path from source to destination. The four closest emergency facilities from FP_Rmax have been determined. The closest facilities analysis concluded that the Government Institute of Rehabilitation Medicine is the nearest viii medical facilities available from FP_Rmax location will take 57.32 sec with 619.17 m travelling distance from its source location. Similarly, the nearest police station and fire brigade respond in 48.76 sec and 76.27 sec to travel from their respective originating locations. The nearest open space for emergency collection is 591.25 m away from FP_Rmax location. It will cover in 532.13 sec. using a pedestrian-based road network by walk. The nearest open space is SDAT cricket ground is school ground. Therefore, it may be used as shelters in an emergency. Moreover, the bus facilities are not required to reallocate the people from LECP. Similarly, nearest medical, police and fire stations and local collection point locations have been identified for the FP_Imax location and FP_Cmax location. The emergency route plan and cost matrix evaluated show that the minimum travel time is 61, 213.42 and 166.52 sec for medical, police and fire-brigade vehicles respectively. In meanwhile duration, people will reach the local collection point by walking using the pedestrian-based road network. It is 820.24 m apart from the FP_Imax location and will cover in 738.21 sec. From the LECP_Imax location, people will use Metropolitan Transportation Corporation Chennai (MTCC) bus facilities to reach the nearest bus depot. In a fire emergency in an opted commercial building, the first medical vehicle will be available in 32.73 sec. for rescue. Similarly, other emergency vehicles may reach in a short time duration. Initiation of the response and rescue process in the initial phase of fire hazard may diminish or reduce its impact. The present study recommended that, using Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) or combination of these techniques might be used to separate the object with similar spectral reflectance in order to achieve higher classification accuracy. Similarly, the above techniques, coupled with GIS, may provide a robust disaster action plan using live or real-time traffic data.