GIS-Based Multi-Criteria Decision Analysis (MCDA) and Analytical Hierarchy Process (AHP) Techniques to Derive Flood Risks Management on Rice Productivity in Gishari Marshland  

作　　者：Jean Nepo Nsengiyumva Emmanuel Nshimiyimana Jean Marie Ntakirutimana Phocas Musabyimana Yvonne Akimana Fred Shema Set Niyitanga Séverin Hishamunda Callixte Musinga Mpamabara Eliezel Habineza Jean Nepo Nsengiyumva;Emmanuel Nshimiyimana;Jean Marie Ntakirutimana;Phocas Musabyimana;Yvonne Akimana;Fred Shema;Set Niyitanga;Séverin Hishamunda;Callixte Musinga Mpamabara;Eliezel Habineza(Department of Agricultural Engineering, Rwanda Polytechnic, IPRC Gishari, Rwamagana, Rwanda;Department of Environmental Information System, University of Lay Adventist of Kigali, Kigali Campus, Kigali, Rwanda;School of Environmental Science and Engineering, Department of Environmental Engineering, Suzhou University of Science and Technology, Suzhou, China;Department of Agricultural and Resources Economics, School of Agriculture and Environmental Sciences, Jomo Kenyatta University of Agriculture and Technology, Kigali Campus, Kigali, Rwanda;Department of Smart Agriculture System, Hankyong National University, Anseong, Kyonggi, South Korea)

机构地区：[1]Department of Agricultural Engineering, Rwanda Polytechnic, IPRC Gishari, Rwamagana, Rwanda [2]Department of Environmental Information System, University of Lay Adventist of Kigali, Kigali Campus, Kigali, Rwanda [3]School of Environmental Science and Engineering, Department of Environmental Engineering, Suzhou University of Science and Technology, Suzhou, China [4]Department of Agricultural and Resources Economics, School of Agriculture and Environmental Sciences, Jomo Kenyatta University of Agriculture and Technology, Kigali Campus, Kigali, Rwanda [5]Department of Smart Agriculture System, Hankyong National University, Anseong, Kyonggi, South Korea

出　　处：《Journal of Geoscience and Environment Protection》2024年第3期222-249,共28页地球科学和环境保护期刊（英文）

摘　　要：Floods are phenomenon with significant socio-economic implications mainly for human loss, agriculture, livestock, soil loss and land degradation, for which many researchers try to identify the most appropriate methodologies by analyzing their temporal and spatial development. This study therefore attempts to employ the GIS-based multi-criteria decision analysis and analytical hierarchy process techniques to derive the flood risks management on rice productivity in the Gishari Agricultural Marshland in Rwamagana district, Rwanda. Here, six influencing potential factors to flooding, including river slope, soil texture, Land Use Land Cover through Land Sat 8, rainfall, river distance and Digital Elevation Model are considered for the delineation of flood risk zones. Data acquisition like Landsat 8 images, DEM, land use land cover, slope, and soil class in the study area were considered. Results showed that if the DEM is outdated or inaccurate due to changes in the terrain, such as construction, excavation, or erosion, the predicted flood patterns might not reflect the actual water flow. This could result unexpected flood extents and depths, potentially inundating rice fields that were not previously at risk and this, expectedly explained that the increase 1 m in elevation would reduce the rice productivity by 0.17% due to unplanned flood risks in marshland. It was found that the change in rainfall distribution in Gishari agricultural marshland would also decrease the rice productivity by 0.0018%, which is a sign that rainfall is a major factor of flooding in rice scheme. Rainfall distribution plays a crucial role in flooding analysis and can directly impact rice productivity. Oppositely, another causal factor was Land Use Land Cover (LULC), where the Multivariate Logistic Regression Model Analysis findings showed that the increase of one unit in Land Use Land Cover would increase rice productivity by 0.17% of the total rice productivity from the Gishari Agricultural Marshland. Based on findings from these techniquesFloods are phenomenon with significant socio-economic implications mainly for human loss, agriculture, livestock, soil loss and land degradation, for which many researchers try to identify the most appropriate methodologies by analyzing their temporal and spatial development. This study therefore attempts to employ the GIS-based multi-criteria decision analysis and analytical hierarchy process techniques to derive the flood risks management on rice productivity in the Gishari Agricultural Marshland in Rwamagana district, Rwanda. Here, six influencing potential factors to flooding, including river slope, soil texture, Land Use Land Cover through Land Sat 8, rainfall, river distance and Digital Elevation Model are considered for the delineation of flood risk zones. Data acquisition like Landsat 8 images, DEM, land use land cover, slope, and soil class in the study area were considered. Results showed that if the DEM is outdated or inaccurate due to changes in the terrain, such as construction, excavation, or erosion, the predicted flood patterns might not reflect the actual water flow. This could result unexpected flood extents and depths, potentially inundating rice fields that were not previously at risk and this, expectedly explained that the increase 1 m in elevation would reduce the rice productivity by 0.17% due to unplanned flood risks in marshland. It was found that the change in rainfall distribution in Gishari agricultural marshland would also decrease the rice productivity by 0.0018%, which is a sign that rainfall is a major factor of flooding in rice scheme. Rainfall distribution plays a crucial role in flooding analysis and can directly impact rice productivity. Oppositely, another causal factor was Land Use Land Cover (LULC), where the Multivariate Logistic Regression Model Analysis findings showed that the increase of one unit in Land Use Land Cover would increase rice productivity by 0.17% of the total rice productivity from the Gishari Agricultural Marshland. Based on findings from these techniques

关 键 词：Multi Criteria Decision Analysis (MCDA) Analytical Hierarchy Analysis (AHA) GIS RS and DEM 

分 类 号：S51[农业科学—作物学]